Miracle-dz/UniFAR-FTUs huggingface dataset:
Search for AN O(100 Gev) Mass Right-Handed Electron Neutrino at the Hera Electron-Proton Collider Using the ZEUS Detector

Search for an O(100 GeV ) Mass Right-Handed Electron Neutrino at the HERA Electron-Proton Collider Using the ZEUS Detector Lawrence Lee Wai We have analyzed 0:82 pb 1 of e p and 2:89 pb 1 of e+p neutral current deep inelastic scattering events produced by the HERA electron-proton collider and collected by the ZEUS detector during 1993 and 1994. A search is made for a right-handed coupling electron neutrino with mass of O(100 GeV) (Ne). The Ne is assumed to be produced via t-channel exchange of a right-handed W (WR), and would decay promptly by radiating a WR. We reconstruct the two jet invariant mass to search for a mass peak corresponding to MWR . Similarly, we reconstruct the electron/positron plus two jet invariant mass to search for a mass peak corresponding toMNe. A suggestive excess of data above expected background is observed for a particular point in the (MWR ;MNe) plane, but no convincing evidence is found for the existence of a Ne and WR. Limits are set upon MNe, MWR, and the right-handed coupling (gR). With more e p scattering data, nearly the entire region of MWR < 100 GeV and MWR < MNe < 200 GeV can be probed to a sensitivity corresponding to gR = 0:55 gL (where gL is the coupling constant associated with the standard model W ).
A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification

Urban functional regions (UFRs) are formed and developed with human social actions and can reflect urban land use types. Appropriately identifying UFRs helps solve existing urban problems, optimize the spatial structure of cities, and provide a database for sustainable urban development. Most existing studies focus on developing novel methods and fusing multiple data sources, but neglect the impact of heterogeneous spatial units on UFR identification results. In this work, a hierarchical spatial unit partition approach was proposed to split the research area into many hierarchical units whilst considering the mixed degree of each unit. We further explored its capacity for identifying UFRs by integrating it with three widely used UFR identification methods. The results reveal that our proposed approach can correctly identify 10% more UFRs compared with the traditional grid‐based method, showing the efficiency of our proposed approach for identifying UFRs at a finer scale. This work provides support for those who are engaged in urban planning and urban policymaking, promoting urban sustainable development.
Exponential Stabilization of the Complete Attitude of an Underactuated Spacecraft

In this paper, two kinematic controllers capable of stabilizing the complete attitude of an underactuated satellite are presented. Previous controllers in the literature impose limitations on the satellite’s initial conditions or give rise to state manifolds which can lead to undesired behavior. We utilize averaging theory to construct two controllers that exponentially stabilize the satellite’s complete attitude without the previous limitations. Both control laws are shown to yield exponential stability, with the second control law having potentially a larger region of attraction.
Understanding Interface-Controlled Resistance Drift in Superlattice Phase Change Memory

Resistance drift in phase change memory (PCM) reduces the accuracy of analog computing applications such as neural network inference. Recently, PCMs based on superlattice (SL) phase change layers have shown low resistance drift, however the origin of this low drift remains unexplored. Here, we uncover that resistance drift in SL-PCM based on alternating layers of Sb2Te3 and Ge2Sb2Te5 (GST) is controlled by the number of SL interfaces as well as the degree of SL intermixing. Temperature-dependent measurements reveal smaller and more stable activation energy upon annealing (thus suppressed structural relaxation) in our SL-PCM vs. control GST devices, accounting for the low resistance drift. By controlling SL interfaces, we achieve low resistance drift coefficient ${v} < 0.01$ in these SL-PCMs, maintained after extensive cycling and at various read voltages and intervals - showing robustness required for analog computing with PCM.
On Improving the Potential Field Method for Ring Formation

In the ring formation control problem, the follower is designated to approach a ring defined relative to the leader. This type of formation gives the follower more flexibility as it trails the leader. To achieve such formation, the follower’s reference velocity is derived using the potential field method, and then a sliding surface is constructed to enforce the follower to track the reference. Motivated by the Levenberg-Marquardt algorithm, this paper proposes modifications on the potential field method to further enhance the follower’s performance in the ring formation. Specifically, adding the approximated Hessian into the formulation of the potential field has a damping effect on the controller and improves the follower’s performance by reducing oscillations about the sliding surface. The use of the approximated Hessian, which is introduced in this paper in the context of ring formation, extends and is useful for control design of potential field methods in general. The proposed algorithm is discussed and interpreted from the feedback control viewpoint, where stability is guaranteed. A numerical example is also presented to illustrate the efficacy of the proposed algorithm.
Design and Performance Evaluation of the Improved INS-Assisted Vector Tracking for the Multipath in Urban Canyons

Dynamic short-delay multipath in increasing dense urban canyons is a challenge to the global navigation satellite system (GNSS)-based positioning systems. However, the performance of existing multipath methods for a stand-alone receiver is limited on dealing with short-delay multipath. Vector tracking-based deep coupling with an inertial navigation system (INS) has the potential to suppress multipath errors by tracking direct signals using more accurate and robust positions. However, in the typical deep couplings reported, the unmodeled multipath error is generally regarded as noise with other errors, and consequently, their capacity for multipath suppression is not satisfying. To solve these problems, an improved deep coupling structure with a multipath error estimator (MPEE) is proposed and implemented in two schemes. Different from other methods, this modification creates nearly no extra complexity and computation cost. In this way, the inner correlation caused by multipath interference between channels can be decoupled, and the underlying requirements for modeling the errors accurately to construct vector feedback are fulfilled. Besides, for comparison analysis, a software-defined platform, including different tracking and integration approaches, is designed. Finally, the proposed methods are evaluated in a weak signal, short-delay multipath, dynamic multipath, and the real-life multipath scenarios, and their superiority in positioning, measurement improvement, and tracking robustness are validated.
Application of a new net primary production methodology: a daily to annual-scale data set for the North Sea, derived from autonomous underwater gliders and satellite Earth observation

Abstract. Shelf seas play a key role in both the global carbon cycle and coastal marine ecosystems through the draw-down and fixing of carbon, as measured through phytoplankton net primary production (NPP). Measuring NPP in situ and extrapolating this to the local, regional, and global scale presents challenges however because of limitations with the techniques utilised (e.g. radiocarbon isotopes), data sparsity, and the inherent biogeochemical heterogeneity of coastal and open-shelf waters. Here, we introduce a new data set generated using a technique based on the synergistic use of in situ glider profiles and satellite Earth observation measurements which can be implemented in a real-time or delayed-mode system (https://doi.org/10.5285/e6974644-2026-0f94-e053-6c86abc00109; Loveday and Smyth, 2022). We apply this system to a fleet of gliders successively deployed over a 19-month time frame in the North Sea, generating an unprecedented fine-scale time series of NPP in the region. At a large scale, this time series gives close agreement with existing satellite-based estimates of NPP for the region and previous in situ estimates. What has not been elucidated before is the high-frequency, small-scale, depth-resolved variability associated with bloom phenology, mesoscale phenomena, and mixed layer dynamics.
Gated Convolutional Neural Network for Wind Turbine Blade Icing Detection

Blade icing detection plays an important role in wind turbine protection and maintenance. Employing well trained deep learning model is a promising method for blade ice detection but needs effective neural networks for sensors data analysis. In this paper, we propose a GRU-gated Convolutional Neural Network (GCNN) to better fuse the information between sensors and temporal information for icing detection. Specifically, with the superior performance of feature extraction, Convolutional Neural Network (CNN) can effectively extract the correlation information of multiple sensors data. Then GRU fuses the temporal information of the feature extracted by CNN to perform the gate of the CNN layer to control the information passed on for icing detection. The proposed method is evaluated on monitoring data generated from 25 wind turbines by two wind farms. The experimental results verify the feasibility and effectiveness of the proposed GCNN.
Analysis of Optimal Integration of EVs and DGs Into CIGRE’s MV Benchmark Model

In today’s transportation sector, the growing number of electric vehicles (EVs) is progressively replacing petroleum-fueled vehicles, which are also expected to minimize greenhouse gas emissions. The main problem with EVs is the requirement of charging energy, which is fed through distribution network, while simultaneously feeding already connected load. The EV’s integration with the distribution network will overload the network due to EV’s charging load, which will eventually trip the power system protection. In this context, it is necessary to minimize power losses, improve voltage profile with sustainable power supply network. Therefore, optimal placement of EVs is required in the distribution network. Conventionally, researchers have used DGs to minimize power losses and improve voltage profile. In this paper, authors analyzed the effect of EV’s integration with simultaneous placement of distributed generations (DGs). The integration of EVs with higher penetration of DGs is cumbersome due to higher power losses and voltage variances that are outside allowable boundaries. The optimal placement of EVs into the distribution system with higher penetration of DGs is proposed in this paper using a battle royale optimization (BRO) algorithm. Since it is a new problem on CIGRE network which is not discussed before. Hence, the authors compared results with three most famous algorithms namely genetic algorithm (GA), particle swarm optimization (PSO), and accelerated PSO (APSO). The optimization problem is developed as a multi-objective function while decreasing active and reactive power losses, and minimising maximum voltage deviation index. The studied distribution network is the CIGRE 14-bus medium voltage (MV) distribution network. Three case studies are taken in which EVs are integrated in two scenarios with optimally sized and located DGs systems in the CIGRE distribution network using MATLAB. Case-1 includes simple network with DGs integration. Case-2 includes EVs only in the simple network, and case-3 finally includes EVs and DGs for optimal placement with minimum losses. The placement of the EVs results in a decrease in power losses and voltage deviation indices. The bus voltages of case-2, on the other hand, stay unchanged when the EVs are integrated. Case 3 with BRO showed the large reduction in power losses owing to the addition of EVs to the distribution network with DGs (from 19.98 kW, and 19.89 kVar to 2.54 kW, and 3.35 kVar).
A Coupling Enhanced Emergency Braking Approach for Double Pendulum Cranes

Crane systems are commonly used transportation tools in industry to transport heavy cargoes to corresponding target positions. If the cargo shape is large which could not be regarded as a mass point or the hook mass also needs to be taken into account, the cranes would perform as double pendulum systems, whose dynamics are more complex than those of single pendulum systems. Since the working environment for crane systems can be very complex in practice, unavoidable emergency situations may occur, which are very dangerous. To avoid possible accidents, effective emergency braking methods are urgently needed, especially for double pendulum cranes with large cargoes. Considering this fact, this paper presents a coupling enhanced braking strategy for double pendulum cranes, which stops the trolley fast and achieves double pendulum vibration elimination objectives simultaneously. Specifically, an elaborately designed variable consisting of the trolley movement, the hook’s swing, and the payload’s swing, is utilized as feedback, which enhances the system couplings and improves the vibration elimination ability. Some constraint items are also introduced to restrict the payload in a safety domain and further avoid possible collisions. The effectiveness is rigorously analyzed and proved by mathematical analysis. Some experiments are executed to show the performance of the designed strategy.
A Federated Learning Framework for Automated Decision Making with Microscopic Traffic Simulation

In recent years, exploring autonomous vehicles has become an emerging research topic due to the massive opportuni-ties to deploy deep learning models in real-world transportation applications. Although deep learning models have been widely applied to microscopic traffic simulations (MTS), they are limited to offline prescriptive analysis or predictive analysis. However, dynamics of a traffic environment frequently change over time; little research has been focused on dynamically updating deep learning models parameters in response to real-time traffic conditions. Further, for dynamic model parameter updates, it is difficult to preserve privacy since raw data is transmitted to the server for model updating (training). In this paper, we present a federated multimodal microscopic traffic simulation (FedMMTS) framework that incorporates federated learning to preserve data privacy and applies multimodal data analytics to achieve a more accurate sensing of the environment. We present preliminary experiments to demonstrate the effectiveness of this framework in a microscopic traffic environment. To the best of our knowledge, this is the first work that incorporates federated learning and data multimodality in MTS environments.
Regions of Exponential Convergence for a Coverage Controller

Coverage controllers provide various capabilities for multi-agent networks with respect to spatial distribution and sensing. Although convergence guarantees are provided for many of the variations on the coverage control problem, they rarely demonstrate exponential convergence or rely on conjecture to do so. Local exponential stability also opens new avenues of research within coverage control, such as robust controller design or combination with online estimation. The main result of this work is proving local exponential stability for a mutli-agent network using a coverage controller, as well as the existence of a ball around local equilibria for which this holds.
MIS-Based GaN Schottky Barrier Diodes: Interfacial Conditions on the Reverse and Forward Properties

In this work, we have conducted systematic studies on the interfacial conditions for gallium nitride (GaN)-based trench metal/insulator/semiconductor (MIS)-type barrier Schottky rectifier (TMBS) with the help of the T-CAD tools. Our results show that the donor-type traps tend to reduce the Schottky barrier height (<inline-formula> <tex-math notation="LaTeX">${q}\varphi _{s}$ </tex-math></inline-formula>), which weakens the charge coupling effect, increases the leakage current, and finally reduces the breakdown voltage (BV). On the contrary, the acceptor-type traps at the contact interface and mesa sidewall will increase <inline-formula> <tex-math notation="LaTeX">${q}\varphi _{s}$ </tex-math></inline-formula> and the turn-on voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{ON}}$ </tex-math></inline-formula>) because they can capture electrons in the mesa region and show the negative polarity. This then enhances the electron depletion at the contact interface and the mesa sidewall, resulting in an increase in <inline-formula> <tex-math notation="LaTeX">${q}\varphi _{s}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${V}_{\mathrm{ON}}$ </tex-math></inline-formula>. Therefore, this process can increase the reverse blocking characteristics. However, for solving the complicated interfacial conditions in the Schottky contact region, we propose using an MIS structure with a 1-nm Al2O3 insulation layer for the GaN-based TMBS rectifiers. Based on the results, the tunneling process and thermionic-emission (TE) process take into account for the current transport mechanism for the MIS-TMBS rectifiers. Meanwhile, the 1-nm thick Al2O3 interlayer increases the effective Schottky barrier height (<inline-formula> <tex-math notation="LaTeX">${q}\varphi _{s} + {q}\varphi _{T}$ </tex-math></inline-formula>), which significantly reduces the reverse leakage. In addition, this design offers more freedom in selecting the Schottky contact electrode for the MIS-TMBS rectifier.
A HYDROCHEMICAL HYBRID CODE FOR ASTROPHYSICAL PROBLEMS. I. CODE VERIFICATION AND BENCHMARKS FOR A PHOTON-DOMINATED REGION (PDR)

A two-dimensional hydrochemical hybrid code, KM2, is constructed to deal with astrophysical problems that would require coupled hydrodynamical and chemical evolution. The code assumes axisymmetry in a cylindrical coordinate system and consists of two modules: a hydrodynamics module and a chemistry module. The hydrodynamics module solves hydrodynamics using a Godunov-type finite volume scheme and treats included chemical species as passively advected scalars. The chemistry module implicitly solves nonequilibrium chemistry and change of energy due to thermal processes with transfer of external ultraviolet radiation. Self-shielding effects on photodissociation of CO and H2 are included. In this introductory paper, the adopted numerical method is presented, along with code verifications using the hydrodynamics module and a benchmark on the chemistry module with reactions specific to a photon-dominated region (PDR). Finally, as an example of the expected capability, the hydrochemical evolution of a PDR is presented based on the PDR benchmark.
Serum cytokine/chemokine profile and clinical/paraclinical data in COVID-19 deceased and recovered patients

Objectives: The induction of an intense immune response and cytokine storm is proposed to be central in the pathogenesis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The study evaluated serum cytokine/chemokine profiles, and clinical and paraclinical data of COVID-19 deceased and recovered patients in Iran. Methods: The severity of disease, clinical data, and routine laboratory and inflammatory cytokine/chemokine responses were retrospectively explored in 60 in-hospital patients in northern Iran. Characteristics of those who deceased (n = 30) were compared to recovered (n = 30), and associations with serum levels of potential disease regulating pro- and anti-inflammatory mediators were studied. Results: The serum levels of IFN-γ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12, IL-17, IP-10, MIP1-α, MCP1, RANTES, and TNF-α were upregulated in all COVID-19 patients when compared to healthy and gender-matched individuals (n = 30). Although with no significant difference between deceased and recovered cases, the serum levels of all cytokines/chemokines tended to be higher in the severely diseased non-surviving patients. Association analyses revealed that all cytokine/chemokine levels (except IL-10) significantly affect the disease outcome. Conclusion: This study provides more evidence for the association of cytokine/chemokine levels with the clinical course and outcome of COVID-19. More studies are needed to consider this measurement as an indicator of disease stage and strategy for treatment.
Factors Influencing Audit Quality of Banking Business Sector in Indonesia

Improved audit quality is needed to increase investor’s confidence in a company's financial statements. Competence and independence are requirements to produce a quality audit. This study is a quantitative study that aimed to analyze the effect of audit tenure, time budget pressure, firm size, and auditor specialization on audit quality. The population and data in this study comprised banking companies listed in the Indonesia Stock Exchange (IDX) in 2015-2019 and were obtained using the purposive sampling method. Earnings surprise benchmark was used as a measure of audit quality in this study with logistic regression analysis method as the analysis method. The results of the study indicated that audit tenure and time budget pressure did not provide an increase in audit quality. While the size of the company had an effect on the decline in audit quality significantly, the auditor specialization greatly improved audit quality. The implication of this research proves that to maintain audit quality, auditors must always maintain and improve their professional qualifications and plan more mature audits on audit work in larger companies.
DPF-SLAM: Dense semantic SLAM based on dynamic probability fusion in dynamic environments

With the development of simultaneous localization and mapping (SLAM) technology, Dynamic SLAM has been a challenging research topic. This paper presents a dynamic probability fusion SLAM (DPF-SLAM) algorithm, which adds a semantic segmentation thread and a dense reconstruction thread to ORB-SLAM2. We integrate dynamic prior probability obtained by semantic segmentation with dynamic probability obtained by dynamic point detection, which can decrease the effect of dynamic objects on the localization accuracy and meanwhile achieve the dense reconstruction of a static background. We evaluate DPF-SLAM system on the public TUM RGBD dataset. The experimental results show that the proposed algorithm has better localization accuracy than DS-SLAM and ORB-SLAM2, which are two relatively new algorithms in this area, and can obtain a good dense reconstruction effect. Moreover, through the performance comparison with our previous work, it is found that the algorithm speed and positioning accuracy are improved.