Miracle-dz/UniFAR-FTUs huggingface dataset:
On Troubleshooting in AGV-based Autonomous Systems

Modern systems tend to become more autonomous. A system can be considered completely autonomous and robust if it does not just manage its tasks autonomously but also manages the faults and failures by adapting its behaviour without external help from a human. There is a wide range of scientific research on autonomous systems with automated guided vehicles (AGVs), robotic fleets and swarms in the industry and logistics. Most research works consider communication and coordination within an autonomous system, collision avoidance, failure mitigation and fault tolerance. The prime aim of the said research is to achieve the autonomous execution of the task, avoiding possible troubles and tolerating faults in the system. Unfortunately, there is a lack of research regarding troubleshooting within autonomous systems utilizing AGVs. Here, we define troubleshooting as the process of detecting a fault, an error or a failure in the system, isolating it, analysing and eventually eliminating it using a troubleshooter, a robot explicitly made for such purposes. In this work, we present an overview of the state of the art in the field of troubleshooting for AGV-driven autonomous systems. This paper aims to become a starting point for further research in the subject at hand.
The Impact of Detailed Distribution System Representation in Dynamic Sub-Transmission-Distribution Co-Simulation

There has been a significant growth in the distributed energy resources (DERs) connected to the distribution networks in recent years, increasing the need for modeling the distribution networks in detail in conjunction with the sub-transmission/transmission networks. This paper models a real distribution/ sub-transmission network using a three-phase/three-sequence co-simulation. One of the modeled distribution feeders has a high penetration of DERs with significant reverse power flow and is modeled including the secondary network. Custom user-defined models are used to represent the solar photovoltaic (PV) units on the feeder including advanced controls and abnormal voltage responses from IEEE 1547–2018 standard. The co-simulation framework developed supports power flow/steady state as well as dynamic analysis. Using this developed framework, this paper studies the impact of balanced and unbalanced faults applied to the distribution and sub-transmission networks. The impacts of the faults on the feeder with the high penetration of DERs are studied in terms of the solar PV units tripping due to under/overvoltages and the resulting change in the feeder-head flow. It is seen that the detailed modeling of the distribution network is needed for accurately capturing the response from the distribution-connected DERs during fault events both on the distribution as well as sub-transmission networks.
SiamBAG: Band Attention Grouping-Based Siamese Object Tracking Network for Hyperspectral Videos

A hyperspectral video contains frames with numerous spectral bands, providing fine reflectance information for object identification and tracking. Enriched features can be learned from spectral–spatial data using deep learning models. However, due to the difficulty in hyperspectral video collection, deep model training is often insufficient, causing reduced performance during the testing stage. To address this issue, we present a novel band attention grouping-based Siamese framework (SiamBAG) for hyperspectral object tracking. SiamBAG uses massive color object tracking data to train a deep neural network. Band weights obtained by the band attention module are used to group a hyperspectral image (HSI) into multiple three-channel false-color images with approximate total group weights. Then multiple enhanced images obtained by histogram equalization are fed to the proposed SiamBAG network to generate a classification branch, a regression branch, and a scale-tuning branch. In the classification branch, the response maps of multiple groups are fused by regularized group weights to estimate the position of objects. Then the regression branch is used to obtain the initial object position of objects. The position offsets are fed back to the scale tune branch to relocate and fine-tune the object position by exploiting the similarity between template features and detection features. Experimental results demonstrate that the proposed tracker achieves superior tracking performance than other methods. The source codes of this article will be released at https://github.com/zephyrhours/Hyperspectral-Object-Tracking-SiamBAG.
Quantifying the Trade-off Between Latency and Power Consumption in Bluetooth Low Energy and Its Mitigation by Using a Wake-Up Receiver

Bluetooth Low Energy (BLE) is one of the primary wireless communication protocols for Internet of Things (IoT) devices due to its inherently low energy consumption. BLE's duty-cycled scheme reduces energy consumption, but at the cost of latency. Wake-Up Receivers (WuRXs) have been proposed to mitigate this trade-off, but most attention seems to have been paid to the circuit design rather than assessing exactly how beneficial a WuRX could be. In this paper, we analyze the power consumption and latency impact of a $\boldsymbol{200\mu \text{W WuRX}}$ added to a state-of-the-art commercial BLE transceiver for relevant IoT scenarios. The results show that the latency/power trade-off can be significantly relaxed for both peripheral and central devices in initiating a connection. Furthermore, if the role of central and peripherals can be changed (peripherals scan instead of advertise), the power consumption of the most energy-constrained devices can be further reduced in scenarios that require less than 200ms latency to initiate a connection. Also, for a maximum latency of 4s, a duty-cycled WuRX enables equal $\boldsymbol{60}\mu \mathbf{W}$ average power consumption for both the central and peripheral devices. This is extremely useful when both ends of the link face similar battery constraints, as would be the case in many IoT scenarios.
PhishCatcher: Client-Side Defense Against Web Spoofing Attacks Using Machine Learning

Cyber security confronts a tremendous challenge of maintaining the confidentiality and integrity of user’s private information such as password and PIN code. Billions of users are exposed daily to fake login pages requesting secret information. There are many ways to trick a user to visit a web page such as, phishing mails, tempting advertisements, click-jacking, malware, SQL injection, session hijacking, man-in-the-middle, denial of service and cross-site scripting attacks. Web spoofing or phishing is an electronic trick in which the attacker constructs a malicious copy of a legitimate web page and request users’ private information such as password. To counter such exploits, researchers have proposed several security strategies but they face latency and accuracy issues. To overcome such issues, we propose and develop client-side defence mechanism based on machine learning techniques to detect spoofed web pages and protect users from phishing attacks. As a proof of concept, a Google Chrome extension dubbed as PhishCatcher, is developed that implements our machine learning algorithm that classifies a URL as suspicious or trustful. The algorithm takes four different types of web features as input and then random forest classifier decides whether a login web page is spoofed or not. To assess the accuracy and precision of the extension, multiple experiments were carried on real web applications. The experimental results show remarkable accuracy of 98.5% and precision as 98.5% from the trials performed on 400 classified phished and 400 legitimate URLs. Furthermore, to measure the latency of our tool, we performed experiments over forty phished URLs. The average recorded response time of PhishCatcher was just 62.5 milliseconds.
A Location Method for AC Fault in LCC-HVDC System Based on Virtual Conduction Width Characteristics

When an AC fault occurs in HVDC transmission system, the transient response change in the rate of change of current and voltage, which may lead to the unexpected action behavior of converter protection. This is attributed to the electrical quantity similarities between internal and external faults, leading to difficulties in fault location identification and reducing the sensitivity in protection measure activation. To realize internal and external fault identification and quickly deal with external faults, an AC fault detection method for LCC-HVDC systems based on virtual conduction width characteristics is proposed. First, by utilizing the ratio integral of the three-phase AC current on the valve side to the reference current, the virtual conduction width flowing through each valve in the sampling window is calculated. Second, according to the size relationship among virtual conduction widths, internal and external faults are identified. Third, after determining the external faults, the difference in the virtual conduction width for each valve branch in the sampling window is calculated to locate the fault phase. Finally, the simulation results based on EMTDC/PSCAD show that the proposed method can quickly identify internal and external faults and locate fault phases and is not affected by fault moment and fault resistance.
Fault Tolerant Architecture Design of a CubeSat Command and Data Handling System

In recent years CubeSat satellite missions have been covering an increasing number of applications with a relatively low-cost budget. In this context, most CubeSat missions use COTS components. Therefore a variety of fault tolerance techniques must be used to obtain the required dependability. This work presents the design methodology that is being followed by the authors in the design of a command and data handling subsystem (C&DH) for CubeSats. The final design of this proposed C&DH subsystem will be included as a secondary payload in the Latin American Satellite Mission, a 3U CubeSat remote sensing mission that will acquire Earth's surface images for use in forestry and agricultural analysis. At the time of writing this paper, a C&DH preliminary version is complete, and a set of fault injection experiments are under development with the goal of identifying and fixing weaknesses in the fault tolerance mechanisms. A review and analysis of C&DH subsystems in recent commer-cial and academic CubeSat projects are presented. As a result of this analysis, a master-supervisor architecture was adopted. The method for estimating the expected rate of SEU and SEFI radiation effects in the planned orbit of the Latin American Satellite Mission is presented, and the adopted master-supervisor architecture is detailed. Finally, a brief description of the planned fault injection experiments is presented.
Utility Maximization in Satellite Networks Using Onboard Caching

The predictable mobility of satellites enables a store-carry-forward paradigm for flows tolerant of long delivery time. Compared to end-to-end direct transmission, this paradigm has less inter-satellite link usage. Using onboard caches to store and carry flows, we propose a time-expanded graph (TEG)-based traffic engineering (TE) scheme. We use a conventional resource allocation algorithm for single-epoch network utility maximization. Then, we propose a load-and-delay-aware path selection (LDAPS) method to control multi-epoch resource contention and improve long-term network utility. Simulation of an actual satellite system reveals that our scheme achieves higher long-term network utility than existing non-caching TE methods. Under the same cache size, LDAPS has better performance than the shortest path strategy.
Frequency-Domain Based Iterative Learning Control for 2-D Discrete Systems

In the existing of traditional iterative learning control (ILC) results for two-dimensional (2-D) discrete systems with time-domain based analysis approach, fixed boundary states do not affect the complete convergence of P-type ILC law. However, it does affect the ILC convergence properties in the frequency domain. This paper first investigates the frequency-domain ILC tracking problem for 2-D discrete systems with different boundary states. An extended P-type ILC law is designed and a sufficient convergence condition of which can be derived through a rigorous mathematical proof. A simulation example is given to verify the effectiveness and validation of the proposed extended P-type ILC law. Finally, some comparison results on traditional P-type ILC law and D-type ILC law are presented.
Multi-Source Domain Transfer Learning on Epilepsy Diagnosis

Epilepsy is a neurological disease that occurs in all ages and seriously threatens physical and mental health. There are two problems in the present study. One is the limitation of the amount of publicly available medical data. And the other is that the distributions of the data are different but correlated. Conventional machine learning methods are not applicable. But transfer learning method has shown promising performance in solving both problems. In this paper, a multi-source domain transfer learning method called MDTL for epilepsy diagnosis is proposed. In order to fully exploit the specific features and common features of the dataset, we propose a domain specific feature extractor and a common feature extractor. For enhancing data, we transform the signals into time-frequency diagrams to rotate and crop. The three types of electrocardiogram (ECG) time-frequency diagram are put to train model, and the model is transferred to electroencephalogram (EEG) time-frequency diagrams. The results confirm that MDTL is effective in epilepsy diagnosis.
Multi-modal medical image classification method combining graph convolution neural networks

In recent years, a single medical image is prone to lose hidden features with low resolution and salient features with high noise but rich information, ignoring the connection of multiple images. Therefore, to remedy for the shortcomings, this paper proposes a multi-modal medical image classification method combined with graph convolutional neural networks(MB-pGCN). First, for the first modality image, we use the ResNet-152 model to extract features, and obtain local disease-related region features with the inter-group comparison method. It utilizes weak attention learning to extract local salient features. For the second modality, we use the patch extraction method to embed. Then it combines the acquired local image features and uses the complementary information carried by the modalities to construct a graph convolutional neural network, designing the convergence layer to achieve graph pooling, which takes advantage of the node features and the local structural relationships between nodes. Ultimately, it splices global features with local features to achieve classification. To validate the proposed classification model framework, we choose ADNI, COVID-19 datasets that are in line with the research, showing that MB-pGCN outperforms other models.
Controlled Size Oils Based Green Fabrication of Silver Nanoparticles for Photocatalytic and Antimicrobial Application

The particle size at the nanometric level allows the manifestation of remarkable properties, chiefly due to changes in surface-to-volume ratio. This study is attributed to the novel green synthesis of nano silver by using essential oils as a capping and reducing agent. Clove oil, cinnamon oil, and cardamom oil were selected for the eco-friendly and low-cost fabrication of silver nanoparticles. The prepared nanoparticles were characterized by photoluminescence spectroscopy, FT-IR spectroscopy, X-Ray diffraction, energy dispersive X-ray spectroscopy, dynamic laser light scattering, thermogravimetric analysis, and transmission electron microscopy. It was found that samples prepared by using cinnamon oil (20 nm) and cardamom oil (12 nm) had smaller particle sizes as compared to those synthesized by using clove oil (45 nm). All the prepared samples exhibited very strong antimicrobial activities with a clear zone of inhibition (6–24 mm) against Staphylococcus aureus, Klebsiella pneumoniae, and Candida albicans. Very resilient photocatalytic activities of the samples were observed against Allura red and fast green dyes. It was concluded that the cinnamon oil-based system is the best size reducer and size homogenizer (less chances of agglomeration) as compared to clove oil and cardamom oil (more chances of agglomeration) for the synthesis of silver nanoparticles.
Product Market Competition, Corporate Investment, and Firm Value: Scrutinizing the Role of Economic Policy Uncertainty

This study examines the effects of product market competition on corporate investment and firm value and the moderating role of economic policy uncertainty on this relationship. The firm-level data of 1971 listed corporate firms for BRIC (Brazil, Russia, India, China) countries during 2009–2020 were used, totaling 23,652 observations. Using the GMM estimates, our results depict that product market competition significantly influences corporate investment and firm value in BRIC countries. The result also reveals that economic policy uncertainty plays a significant role in the impact of product market competition on corporate investment and firm value at Brazilian, Russian, Indian, and Chinese firms. The study’s findings contribute to the body of knowledge by providing new evidence on the relationship between PMC, corporate investment, and firm value. These findings are vital for policymakers and regulatory bodies to focus on economic uncertainty in a competitive environment without jeopardizing investment returns in emerging markets.
Identification of Immunodominant Proteins of the Leishmania (Viannia) naiffi SubProteome as Pan-Specific Vaccine Targets against Leishmaniasis

Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. A well-modulated immune response that is established after the long-lasting clinical cure of leishmaniasis can represent a standard requirement for a vaccine. Previous studies demonstrated that Leishmania (Viannia) naiffi causes benign disease and its antigens induce well-modulated immune responses in vitro. In this work we aimed to identify the immunodominant proteins present in the soluble extract of L. naiffi (sLnAg) as candidates for composing a pan-specific anti-leishmaniasis vaccine. After immunoblotting using cured patients of cutaneous leishmaniasis sera and proteomics approaches, we identified a group of antigenic proteins from the sLnAg. In silico analyses allowed us to select mildly similar proteins to the host; in addition, we evaluated the binding potential and degree of promiscuity of the protein epitopes to HLA molecules and to B-cell receptors. We selected 24 immunodominant proteins from a sub-proteome with 328 proteins. Homology analysis allowed the identification of 13 proteins with the most orthologues among seven Leishmania species. This work demonstrated the potential of these proteins as promising vaccine targets capable of inducing humoral and cellular pan-specific immune responses in humans, which may in the future contribute to the control of leishmaniasis.
Role of Reference Frames for a Safe Human–Robot Interaction

Safety plays a key role in human–robot interactions in collaborative robot (cobot) applications. This paper provides a general procedure to guarantee safe workstations allowing human operations, robot contributions, the dynamical environment, and time-variant objects in a set of collaborative robotic tasks. The proposed methodology focuses on the contribution and the mapping of reference frames. Multiple reference frame representation agents are defined at the same time by considering egocentric, allocentric, and route-centric perspectives. The agents are processed to provide a minimal and effective assessment of the ongoing human–robot interactions. The proposed formulation is based on the generalization and proper synthesis of multiple cooperating reference frame agents at the same time. Accordingly, it is possible to achieve a real-time assessment of the safety-related implications through the implementation and fast calculation of proper safety-related quantitative indices. This allows us to define and promptly regulate the controlling parameters of the involved cobot without velocity limitations that are recognized as the main disadvantage. A set of experiments has been realized and investigated to demonstrate the feasibility and effectiveness of the research by using a seven-DOF anthropomorphic arm in combination with a psychometric test. The acquired results agree with the current literature in terms of the kinematic, position, and velocity aspects; use measurement methods based on tests provided to the operator; and introduce novel features of work cell arranging, including the use of virtual instrumentation. Finally, the associated analytical–topological treatments have enabled the development of a safe and comfortable measure to the human–robot relation with satisfactory experimental results compared to previous research. Nevertheless, the robot posture, human perception, and learning technologies would have to apply research from multidisciplinary fields such as psychology, gesture, communication, and social sciences in order to be prepared for positioning in real-world applications that offer new challenges for cobot applications.
A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019

Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas.