Miracle-dz/UniFAR-FTUs huggingface dataset:
Brief Unscrambling of DVB-RCS2

To meet the demand of a new generation Interactive Satellite System(ISS),the part 1 and part 2 of DVB-RCS2 specifications were approved by ETSI in May and August 2011 separately.Some key technologies of the new standard are analyzed.The newly added forward link network control signaling and some other improvements of DVB-RCS2 are highlighted elaborated and some changes to the return link are further discussed.The feasibility of establishing our own ISS standard is also explored.
Fairies in the Box: Children’s Perception and Interaction towards Voice Assistants

In recent years, the growing popularity of smart speakers (e.g., Google Home and Alexa) has facilitated young children’s interaction with internet-based devices and provided them with more opportunities to obtain access to online information. This review summarizes the current state of the research by examining smart speakers’ core characteristics, children’s conceptualization and interaction with smart speakers, and the influences on children’s learning and habits. Our review shows that (a) the natural language processing technology and central computing system (Internet) contribute to the uniqueness of smart speakers; (b) although children tend to attribute human characteristics (e.g., smart and friendly) to smart speakers, they might judge these voice assistant devices as neither explicitly living nor nonliving in ontological perception; (c) children’s overattributing certain knowledge (e.g., questions about personal information) to smart speakers does not necessarily mean that this device is believed to be omniscient; and (d) in terms of promoting children’s learning, smart speakers might not be more effective than a real human, and the interaction with smart speakers may not be conducive to children’s maintenance of civilized social norms. Implications for children’s conceptualization and interaction of smart speakers and the design of children-oriented smart agents are also discussed.
PrivacySignal: Privacy-Preserving Traffic Signal Control for Intelligent Transportation System

A new trend of using deep reinforcement learning for traffic signal control has become a spotlight in the Intelligent Transportation System (ITS). However, the traditional intelligent traffic signal control system always collects and transmits vehicle information (e.g., vehicle location, speed, etc.) in the form of plaintext, which would result in the leakage of commuters’ privacy and thus bring unnecessary troubles to users. In this paper, we propose a privacy-preserving traffic signal control for an intelligent transportation system (PrivacySignal). It relies on the existing road facilities to achieve the privacy of commuters, which guarantees the practicality of the system. Real-time decision-making and confidentiality of the system can be achieved simultaneously via the design of a series of secure and efficient interactive protocols, that are based on additive secret sharing, to perform the deep $Q$ -network (DQN). Moreover, the security of PrivacySignal is testified, meanwhile, the system effectiveness, and the overall efficiency of PrivacySignal is demonstrated through theoretical analysis and simulation experiments. Compared with the existing privacy-preserving schemes of the intelligent traffic signal, PrivacySignal provides a general DQN based privacy-preserving traffic signal control strategy architecture with high efficiency and low-performance loss.
A Novel Plug-and-Play Factor Graph Method for Asynchronous Absolute/Relative Measurements Fusion in Multisensor Positioning

The presence of asynchronous absolute and relative measurements has posed a great challenge to the current multisensor positioning method in robotic systems. Although traditional factor graph methods possess a capability of plug-and-play, only a compromised performance can be obtained when dealing with an increasing number of asynchronous observations. In this article, a novel plug-and-play factor graph method for asynchronous absolute/relative measurements fusion in multisensor positioning is proposed. Different from traditional methods, a fixed-rate graph model is formed. The variable nodes in the graph are built at a fixed update rate to do the optimization, which is not affected by the arrival of measurements. Asynchronous absolute and relative measurements between two successive variable nodes are associated with corresponding variable nodes in the graph via the propagation of closed-form inertial measurement unit (IMU) preintegration. The simulations, datasets, and field tests are carried out to validate the proposed method. The results indicate that the closed-form IMU preintegration method has better dynamic adaptability in fusion system to formulate the association with asynchronous measurements. On this basis, the proposed method can integrate asynchronous measurements in a plug-and-play manner and achieve better performance compared to current methods. Meanwhile, the computational load can also be reduced.
Multi-Task Learning With Latent Variation Decomposition for Multivariate Responses in a Manufacturing Network

Modeling the relationships between the quality response variables and process settings or in situ sensing variables is a fundamental problem in quality engineering. Such relationships are important for product quality prediction, process monitoring, and optimization. Data collected from a single system often only carry limited information, making modeling one system at a time challenging. Multi-task learning (MTL) jointly models similar-but-non-identical systems and utilizes the similarities among systems for better performance. However, existing MTL becomes much less effective if important variables are missing or unmeasurable in the underlying process (latent variables). More importantly, commonly shared latent variables across systems often reflect important process patterns/behaviors, deserving more investigations. We proposed an MTL framework for multivariate or profile responses by explicitly decomposing the variation among systems into explainable variation and latent variation. Specifically, the explainable variation is from variables observed in data, while the latent variation is from the latent basis functions automatically generated from model residuals. The proposed method improves the prediction accuracy and interpretability of modeling. The simulation and a case study in a silicon ingot manufacturing network demonstrate that the proposed method can improve the quality modeling performance and recover critical process knowledge for silicon ingot manufacturing based on Czochralski (CZ) process. Note to Practitioners—This research is motivated by quality modeling of a semiconductor manufacturing network consisting of multiple furnaces (systems) producing silicon ingots. An accurate quality model in manufacturing is essential for downstream tasks such as process monitoring and optimization. As data collected from a single system often only carry limited information, aggregating data from multiple systems in quality modeling can significantly improve the performance. However, different systems in a network are often similar-but-non-identical to each other due to different degradation statuses, usage history, product receipts. As a result, data from different systems are heterogeneous, making combining all data for modeling inappropriate. Multi-task learning (MTL) solves this problem and recovers the similar-but-non-identical nature of systems. However, existing MTL is much less effective if important latent factors/variables are missing or unmeasurable in the underlying process. More importantly, such latent factors can reflect important process patterns/behaviors. This work improves the MTL framework by explicitly explaining the unexplained variations using the latent factors automatically generated from model residuals. The simulation and the case study in a silicon ingot manufacturing network demonstrate that the proposed method improves the quality modeling performance and recovers critical process knowledge for silicon ingot manufacturing based on Czochralski (CZ) process.
Karanahan: A Potential New Treatment Option for Human Breast Cancer and Its Validation in a Clinical Setting

Introduction: Karanahan, a cancer treatment technology aimed at eradicating tumor-initiating stem cells, has already proven effective in 7 tumor models. Karanahan comprises the following procedures: (1) collecting surgical specimens, (2) determining the duration of the DNA repair process in tumor cells exposed to a cross-linking cytostatic agent, and (3) determining the time point, when cells, including tumor-initiating stem cells, are synchronized in the certain phase of the cell cycle after triple exposure to the cytostatic, becoming vulnerable for the terminal treatment, which is supposed to completely eliminate the rest of survived tumor-initiating stem cells. Determining these basic tumor properties allows to design the schedule for the administration of a cross-linking cytostatic and a complex composite DNA preparation. Being conducted in accordance with the schedule designed, Karanahan results in the large-scale apoptosis of tumor cells with elimination of tumor-initiating stem cells. Methods: Breast tumor specimens were obtained from patients, and basic tumor properties essential for conducting Karanahan therapy were determined. Results: We report the first use of Karanahan in patients diagnosed with breast cancer. Technical details of handling surgical specimens for determining the essential Karanahan parameters (tumor volume, cell number, cell proliferation status, etc) have been worked out. The terminally ill patient, who was undergoing palliative treatment and whose tumor specimen matched the required criteria, received a complete course of Karanahan. Conclusions: The results of the treatment conducted indicate that Karanahan technology has a therapeutic potency and can be used as a breast cancer treatment option.
Surface Modification of Sulfur-Assisted Reduced Graphene Oxide with Poly(phenylene sulfide) for Multifunctional Nanocomposites

The sulfur on the sulfur-assisted reduced graphene oxide (SrGO) surface provides the origin of poly(phenylene sulfide) PPS-grafting via SNAr mechanism. In-situ polymerization from sulfur on SrGO afforded surface modification of SrGO, resulting in enhanced dispersibility in PPS. The tensile strength, electrical and thermal conductivities, and flame retardancy of PPS-coated SrGO were efficiently enhanced using highly concentrated SrGO and masterbatch (MB) for industrial purposes. Three-dimensional X-ray microtomography scanning revealed that diluting MB in the PPS resin afforded finely distributed SrGO across the PPS resin, compared to the aggregated state of graphene oxide. For the samples after dilution, the thermal conductivity and flame retardancy of PPS/SrGO are preserved and typically enhanced by up to 20%. The proposed PPS/SrGO MB shows potential application as an additive for reinforced PPS due to the ease of addition during the extrusion process.
A Review of Machine Learning Applications for Credit Card Fraud Detection with A Case study

Purpose - This paper aims to highlight the widely used supervised techniques applied for fraud detection. In addition, this paper aims to apply some techniques to evaluate their performance on real-world data and develop an ensemble model as a potential solution for this problem. Design/Methodology- Different techniques applied in this study for fraud detection purposes are logistic regression, decision tree, random forest, KNN, and XGBoost. The confusion matrix gives information about the assignment of inputs to the different classes. This study uses precision and recall to evaluate the performance, calculated based on the confusion matrix. Findings- XGBoost is the fastest and is expected to have the best performance; however, it is only outperforming the random forest in terms of accuracy, precision, recall, and f1-score. In general, the KNN and logistic regression have better performance, which means they better detect fraudulent transactions. Practical Implications- The new model can be applied to new data instead of the previous techniques.
Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters

Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach.
Blind-Zone-Free Metal Object Detection for Wireless EV Chargers Employing DD Coils by Passive Electromagnetic Sensing

Metal object detection (MOD) is crucial for the safe operation of ground-assembled wireless electric vehicle charging (WEVC) systems. Electromagnetic sensing by passive coils is a reliable MOD method for high-power applications, such as WEVC. Previous designs of sensing coils are mainly targeted at the systems with unpolarized coils (e.g., circular coils). However, they are not suitable for those with DD-coil-coupling engaged due to the fundamentally different field pattern. To fix this problem, in this article, a field-oriented design of sensing coils is proposed, which mainly aims at blind-zone elimination while maintaining high sensitivity and cost-effectiveness. The blind zone arising from axial symmetry of the field is removed by size-modulated C-shape coil units. And removing the central blind spot relies on an extra patch coil by utilizing the characteristic of parallel magnetic flux generated by DD coils. Tested by 13 positions (central blind spot excluded) where MOs might exist using a 3-kW WEVC prototype, the optimized sensing coils completely remove the axial blind zones when 50 mV is set as the threshold voltage. Moreover, for the central blind spot, the proposed patch coil can stably identify MO intrusion no matter if the prototype operates in light-load or heavy-load conditions.
Security Perception of IoT Devices in Smart Homes

IoT devices are used frequently in smart homes. To better understand how users perceive the security of IoT devices in their smart homes, a model was developed and tested with multiple linear regression. A total of 306 participants participated in the survey with measurement items, out of which 121 had already been using IoT devices in their smart homes. The results show that users’ awareness of data breaches, ransomware attacks, personal information access breaches, and device vulnerabilities have an effect on IoT security importance. On the other hand, users often do not check their security settings and feel safe while using IoT devices. This paper provides an overview of users’ perception of security while using IoT devices, and can help developers build better devices and help raise awareness of security among users.
An improved NMPC-NDOB scheme for trajectory tracking of unmanned surface vessel

This study investigates the trajectory-tracking problem of fully actuated unmanned surface vessels (USVs). A three-degree-of-freedom (3-DOF) dynamic model was used with three control variables: surge force, sway force, and yaw moment, where the model uncertainties, environmental disturbances, and actuator saturation were also considered. An improved nonlinear model predictive control (NMPC) method was investigated to improve the control performance in the trajectory tracking of USVs, and a nonlinear optimization problem based on the finite horizon was developed to minimize the divergence between the actual and the reference states. Moreover, a nonlinear disturbance observer (NDOB) was employed to estimate the environmental disturbance, which could enhance the robustness of the controller. Furthermore, an event-triggered mechanism was also employed to reduce the computational frequency of NMPC. Simulations of slide mode control, NMPC, and improved NMPC were performed in the trajectory-tracking control. Besides, the simulation results verified the effectiveness and robustness of the proposed improved NMPC-NDOB scheme.
DNA Methylation Mediates EMT Gene Expression in Human Pancreatic Ductal Adenocarcinoma Cell Lines

Due to abundant stroma and extracellular matrix, accompanied by lack of vascularization, pancreatic ductal adenocarcinoma (PDAC) is characterized by severe hypoxia. Epigenetic regulation is likely one of the mechanisms driving hypoxia-induced epithelial-to-mesenchymal transition (EMT), responsible for PDAC aggressiveness and dismal prognosis. To verify the role of DNA methylation in this process, we assessed gene expression and DNA methylation changes in four PDAC cell lines. BxPC-3, MIA PaCa-2, PANC-1, and SU.86. 86 cells were exposed to conditioned media containing cytokines and inflammatory molecules in normoxic and hypoxic (1% O2) conditions for 2 and 6 days. Cancer Inflammation and Immunity Crosstalk and Human Epithelial to Mesenchymal Transition RT² Profiler PCR Arrays were used to identify top deregulated inflammatory and EMT-related genes. Their mRNA expression and DNA methylation were quantified by qRT-PCR and pyrosequencing. BxPC-3 and SU.86. 86 cell lines were the most sensitive to hypoxia and inflammation. Although the methylation of gene promoters correlated with gene expression negatively, it was not significantly influenced by experimental conditions. However, DNA methyltransferase inhibitor decitabine efficiently decreased DNA methylation up to 53% and reactivated all silenced genes. These results confirm the role of DNA methylation in EMT-related gene regulation and uncover possible new targets involved in PDAC progression.
Privacy-Enhanced and Practical Truth Discovery in Two-Server Mobile Crowdsensing

In mobile crowdsensing, truth discovery (TD) enables a crowdsensing server to extract truthful information from possibly conflicting crowdsensing data. TD provides a more accurate truth estimation than traditional truth inference methods like majority voting and averaging. However, there still exist crucial data privacy (including sensory data, inferred truths, and intermediates) and practicability (e.g., efficiency, utility, and non-interaction) concerns in real-world crowdsensing applications. Existing researches either fail to provide adequate data privacy protection throughout the entire TD procedure or suffer from low practicability. In this paper, we propose two schemes: a basic privacy-aware TD scheme (BPTD) and a privacy-enhanced TD scheme (PETD) with two servers for mobile crowdsensing, comprehensively considering both privacy and practicability. BPTD is straightforwardly conducted on shared data with few user-side interactions, while achieving high efficiency. To further liberate mobile users and prevent disclosure of the intermediates, PETD incorporates a novel partial decryption-based Paillier Cryptosystem to work with secret sharing, offering enhanced privacy protection without relying on any user-side involvement. Additionally, we improve the efficiency of PETD via data packing. Security analysis shows the desired privacy goals. Compared to prior studies with the best security guarantees, our extensive experiments demonstrate a comparable and superior performance regarding different metrics.
Fresnel Line-of-Sight Probability With Applications in Airborne Platform-Assisted Communications

The ecosystem of airborne platforms is maturing rapidly and becoming essential to meeting the communication requirements of modern wireless networks. In such a context, the speed, range, and quality of service are highly dependent on a timely access to the right amount and type of affordable spectrum. In this way, evaluating the statistical properties of the air-to-ground (AtG) channel in different built-up propagation environments with regard to the operating frequency is crucial for performance analysis of airborne platform-assisted communications. In this paper, we construct a framework for the line-of-sight (LoS) probability based on the intrusion ratio of obstacles within the first Fresnel zone, yielding an analytical expression for the LoS probability that is sensitive to the operating frequency, the transmitter and receiver heights, the horizontal distance between them, and three other parameters depicting the statistical properties of the urban environment. The model developed is accurate enough to capture scattering mechanisms such as reflection and diffraction, while being sufficiently flexible mathematically to allow, based for instance on powerful analytical tools such as stochastic geometry, a seamless and physically meaningful system-level performance evaluation of modern wireless networks in the presence of airborne platforms.
Evaluating and forecasting direct carbon emissions of electricity production: A case study for South East Europe

ABSTRACT Global effort to reduce greenhouse gas emissions is continuously growing stronger. Realization of the direct correlation of quality of life to the environmental aspects of the energy sector has been at the heart of the new energy paradigm. In light of this, ensuring full awareness of current and potential emissions, with regard to market circumstances and policy implications, is crucial in acquiring a better overview on the current issues and the future development of the energy system. Research presented in this paper offers a method for calculating direct CO2 emissions from electricity generation. It also offers an insight into current issues and future prospects of the South East Europe electricity market. Observing various scenarios, the region’s emission intensity is evaluated, with special consideration given to the potential impact of varying carbon prices.
Tolerância a falhas em multi-controladores SDN: eficiência de controladores RYU usando Mininet

O Paradigma SDN surgiu como um caminho para a realização de uma interação entre redes e aplicações. Sua arquitetura centralizada faz com que um controlador tenha uma visão central da rede e sua característica de programação permite que os aplicativos programem a rede. Nessa direção, o Mininet é uma ferramenta amplamente utilizada para emular cenários de redes SDN. Contudo, ao emular um cenário com multi-controladores, o módulo nativo do Mininet gera uma sobrecarga na taxa de utilização dos recursos de Memória e CPU. Nesse sentido, este artigo apresenta um módulo, com características funcionais de um multi-controlador, gerando um melhor compromisso (tradeoff) na taxa de utilização de Memória e CPU, em caso de indisponibilidade de um controlador em um cenário SDN com topologia de rede hierárquica.