Miracle-dz/UniFAR-FTUs huggingface dataset:
Terrain Mapping for Autonomous Trucks in Surface Mine

Maps with different representations play an essential role in the development of automotive intelligence. In order to enhance the capacity of autonomous trucks in surface mine, an extensible terrain mapping system based on LiDAR is proposed in this paper. Point cloud map, 2.5D grid map, and mesh map are integrated into a unified and extensible map-building framework. In order to adapt to the unique characteristics of surface mine, terrain mapping methods are proposed based on existing approaches. Each map-building method builds a local robot-centered map for time-sensitive tasks. Local maps are fused into global maps in the cloud for non-time-sensitive tasks. The construction method of the point cloud map can avoid the loss of information when updating the map by computing convex hulls. The 2.5D grid map can model the unstructured and rugged terrain of mines. The mesh map is built based on Poisson reconstruction, which is conducive to human-truck interaction. In addition, the map maintenance method in the framework is proposed. Experiments are conducted with datasets collected in real-world scenes.
Magnetohydrodynamic Simulations of the Formation of Molecular Clouds toward the Stellar Cluster Westerlund 2: Interaction of a Jet with a Clumpy Interstellar Medium

The formation mechanism of CO clouds observed with the NANTEN2 and Mopra telescopes toward the stellar cluster Westerlund 2 is studied by 3D magnetohydrodynamic simulations, taking into account the interstellar cooling. These molecular clouds show a peculiar shape composed of an arc-shaped cloud on one side of the TeV γ-ray source HESS J1023-575 and a linear distribution of clouds (jet clouds) on the other side. We propose that these clouds are formed by the interaction of a jet with clumps of interstellar neutral hydrogen (H i). By studying the dependence of the shape of dense cold clouds formed by shock compression and cooling on the filling factor of H i clumps, we found that the density distribution of H i clumps determines the shape of molecular clouds formed by the jet–cloud interaction: arc clouds are formed when the filling factor is large. On the other hand, when the filling factor is small, molecular clouds align with the jet. The jet propagates faster in models with small filling factors.
Integrated High-Definition Maps for Self-Localization with Vector and Polygon Data

Highly accurate self-localization is necessary to achieve safe autonomous driving. Recently, map-based self-localization has been attracting attention. Maps generally consist of point cloud information and vector information. However, this information is managed separately and is not compatible. Therefore, we propose a map format that bridges point cloud information and vector information. Vector data is extracted for objects in the environment and associated with a normal distribution representing detailed geometric information. The proposed map can be used as a vector map for various tasks in autonomous driving, and the set of normal distributions provides accurate self-localization. Experiments show that the proposed self-localization achieves lane-level accuracy (~25 cm location) in urban Tokyo.
Reconfigurable Dual-Type Sensor for Resonant and Broadband Liquid Materials Characterization

A novel sensor is proposed featuring a reconfigurable dual-type operation offering both resonant and broadband liquid materials characterization. Advantageously, both types of measurements are carried out for the same liquid sample without disconnecting the measurement setup. The sensor is realized in Printed Circuit Board (PCB) technology as a microstrip transmission line (MSL) loaded with a Complementary Split Ring Resonator (CSRR) with an accompanying 3D printed liquid container. The default type is the resonant measurement offering a robust characterization in the vicinity of a single frequency well suited for the detection of even small material parameters change from the resonant frequency and quality factor deviation in the reflection coefficient of the CSRR loaded MSL submerged in the liquid. For broadband type measurement, the CSRR is shorted with a stamper to create a uniform ground plane while the relative permittivity of the liquid is determined from the S-parameters of an MSL having a dielectric cover on top. An exemplary sensing setup was developed with resonance at 1.5 GHz, fabricated, and experimentally tested for a set of alcohol samples. The obtained results proved the features of the proposed technique.
GenPa-SLAM: Using a General Panoptic Segmentation for a Real-Time Semantic Landmark SLAM

Alongside a detailed knowledge about the current environment, the ego position is a major aspect in autonomous driving. Solving the task of localization and mapping typically consists of a front-end and a back-end. While the front-end extracts features and solves the data association problem, the back-end performs the localization and the mapping of the extracted features. In this work, we present a novel LiDAR-based SLAM algorithm: In the front-end, a general panoptic segmentation algorithm is used for extracting clustered and classified objects. Knowing the class of each individual cluster gives us the opportunity to efficiently extract various geometric primitives, such as cylinders, planes, lines or bushes. Furthermore, potentially dynamic things, such as cars and trucks, are neglected. A graph-based back-end optimizes the vehicle trajectory and the landmarks position online. Our approach is evaluated on the KITTI odometry benchmark and further experiments using two different LiDAR sensors on our testing vehicles.
Relative Permittivity Measurements With SIW Resonant Cavities at mm- Wave Frequencies

In this paper an effective though simple method to accurately characterize dielectric substrate materials at mm-wave frequencies is presented. The method is based on the use of a resonant cavity in SIW technology realized in a substrate made out of the material to characterize. Based on the measured resonant frequency, it is then possible to extract the electrical properties of the material exclusively from the geometry of the cavity. The SIW cavity is fed by two grounded coplanar waveguides to reduce the loading effect of the transmission lines and to avoid errors in the estimations of relative permittivity. The proposed cavity provides a solution for material characterization for emerging 5G-and-beyond applications operating at high frequencies. Measurements have been performed in the frequency range from 10 GHz to 30 GHz, but the design is scalable to lower or higher frequency ranges, if within the possibilities of the manufacturing process of the cavity.
Work in Progress: ACAC: An Adaptive Congestion-aware Approximate Communication Mechanism for Network-on-Chip Systems

Data-intensive applications, such as machine learning and pattern recognition, result in heavy Network-on-Chip (NoC) communication loads and a tremendous increase in the communication latency. At the same time, the error-tolerant nature of these applications makes approximate communication an effective way to relieve the sharp increase of the network latency. This paper proposes an adaptive congestion-aware approximate communication mechanism (ACAC) that can alleviate the communication congestion of NoC systems in heavy communication loads. Our cycle-accurate simulations have shown that the proposed ACAC effectively reduces the network latency similar to ABDTR under a 22% to 52% lower data approximate ratio and significantly decreases the additional compression control traffic volume under real applications.
Mobile App Driven Localization for Motorcycles Based on an Adaptive Unscented Kalman Filter

Precise and robust localization of vehicles on proving grounds and test facilities is an essential requirement for their safe operation. Although motorcyclists are exposed to significantly greater risks of injury compared to drivers of passenger cars and trucks, no such monitoring systems adjusted to motorcycles exist to date. In this work, we present an Adaptive Unscented Kalman Filter (AUKF) based loosely coupled navigation filter for estimating the position, velocity, and orientation of a motorcycle using a smartphone with an external sensor setup consisting of a GNSS sensor and an IMU. A key advantage of the AUKF navigation filter in the context of proving ground monitoring is that it can be used with different vehicles, which have varying vibration characteristics, without manual re-tuning. Adequate parametrization with regard to the GNSS and IMU sensors and the implementation on a suitable hardware system are addressed. The novel implementation of the navigation filter on a smartphone connected to the sensor setup over a wireless interface allows the flexible integration with different devices and sensor systems, which acts jointly with the adaptivity characteristic of the AUKF. Field tests with a motorcycle demonstrate the applicability and robustness of the developed approach, as well as its performance with respect to a total station reference, achieving a mean 2D error of 0.79 m without RTCM correction data. The orientation estimation of the AUKF navigation filter shows qualitatively improved performance and faster yaw angle convergence compared to a regular UKF. In future work, the orientation estimation of the navigation filter will be validated against an attitude and heading reference system, and tested with different sensor setups and motorcycle models.
Deep Virtual-to-Real Distillation for Pedestrian Crossing Prediction

Pedestrian crossing is one of the most typical behavior which conflicts with natural driving behavior of vehicles. Consequently, pedestrian crossing prediction is one of the primary task that influences the vehicle planning for safe driving. However, current methods that rely on the practically collected data in real driving scenes cannot depict and cover all kinds of scene condition in real traffic world. To this end, we formulate a deep virtual to real distillation framework by introducing the synthetic data that can be generated conveniently, and borrow the abundant information of pedestrian movement in synthetic videos for the pedestrian crossing prediction in real data with a simple and lightweight implementation. In order to verify this framework, we construct a benchmark with 4667 virtual videos owning about 745k frames (called Virtual-PedCross-4667), and evaluate the proposed method on two challenging datasets collected in real driving situations, i.e., JAAD and PIE datasets. State-of-the-art performance of this framework is demonstrated by exhaustive experiment analysis. The dataset and code can be downloaded from the website 1 1http://www.lotvs. net/code_data/.
Subgroup Division and Group-Based Control Strategy for Mixed Traffic on Two-Lane Highway

To alleviate the traffic congestion, a group-based control strategy for mixed traffic on two-lane highway is presented. First, vehicles in the same lane are divided into subgroups, then the group-based control strategies for group split and group formation are given, the detailed operating rules for CAVs under different conditions is developed. In addition, the method proposed in this paper is verified by VISSIM. Results show that our method has obvious advantages in reducing travel time and the total times of stops.
Injection Simulation: an Efficient Validation Framework for Autonomous Driving System

Building a simulation environment is of great significance for realizing efficient and low-cost autonomous driving tests. However, even expensive 3D scene rendering methods cannot guarantee the authenticity of the data generated in the simulation system. Therefore, we expect to develop a simulation platform driven by real scene data, not only to pursue the aesthetic or realism of visual effects, but also to pay more attention to the generation of simulation data needed to verify the effectiveness of autonomous driving systems. In this paper, we propose a simulation framework for autonomous vehicles, which is called “injection simulation”. The innovations of the framework include: i) A joint scene generation method based on high-precision semantic map and SUMO is proposed to generate various complex traffic scenes or construct “corner cases” required for rapid testing; ii) A virtual bridge between ROS and SUMO is constructed to enable traffic flow vehicles and autonomous vehicles to interact in a full-duplex mode in the simulation environment to achieve the injection simulation of the fusion layer; iii) A real-time LiDAR data generation method based on a large-scale real point cloud map and virtual traffic flow is proposed to realize the injection simulation of the perception layer. The proposed framework can effectively test autonomous driving algorithms, and is easy to implement different test modes such as Software-in-the loop (SiL) and Vehicle-in-the-loop (ViL).
Supernova dust yields: the role of metallicity, rotation, and fallback

Supernovae (SNe) are considered to have a major role in dust enrichment of high redshift galaxies and, due to the short lifetimes of interstellar grains, in dust replenishment of local galaxies. Here we explore how SN dust yields depend on the mass, metallicity, and rotation rate of the progenitor stars, and on the properties of the explosion. To this aim, assuming uniform mixing inside the ejecta, we quantify the dust mass produced by a sample of SN models with progenitor masses $13~M_{\odot} \leq M \leq 120~M_{\odot}$, metallicity $\rm -3 \leq [Fe/H] \leq 0$, rotation rate $\rm v_{\rm rot} = 0$ and $300$~km/s, that explode with a fixed energy of $1.2 \times 10^{51}$~erg (FE models) or with explosion properties calibrated to reproduce the $\rm ^{56}Ni$ - $M$ relation inferred from SN observations (CE models). We find that rotation favours more efficient dust production, particularly for more massive, low metallicity stars, but that metallicity and explosion properties have the largest effects on the dust mass and its composition. In FE models, SNe with $M \leq 20 - 25 ~M_{\odot}$ are more efficient at forming dust: between 0.1 and 1 $M_\odot$ is formed in a single explosion, with a composition dominated by silicates, carbon and magnetite grains when $\rm [Fe/H] = 0$, and by carbon and magnetite grains when $\rm [Fe/H] < 0$. In CE models, the ejecta are massive and metal-rich and dust production is more efficient. The dust mass increases with $M$ and it is dominated by silicates, at all [Fe/H].
Quintessence in a brane world

We reanalyze a new quintessence scenario in a brane world model, assuming that a quintessence scalar field is confined in our three-dimensional brane world. We study three typical quintessence models: (1) an inverse-power-law potential, (2) an exponential potential, and (3) a kinetic-term quintessence $(k$-essence) model. With an inverse-power-law potential model $[V(\ensuremath{\varphi})={\ensuremath{\mu}}^{\ensuremath{\alpha}+4}{\ensuremath{\varphi}}^{\ensuremath{-}\ensuremath{\alpha}}],$ we show that in the quadratic dominant stage the density parameter of a scalar field ${\ensuremath{\Omega}}_{\ensuremath{\varphi}}$ decreases as ${a}^{\ensuremath{-}4(\ensuremath{\alpha}\ensuremath{-}2)/(\ensuremath{\alpha}+2)}$ for $2l\ensuremath{\alpha}l6,$ which is followed by the conventional quintessence scenario. This feature provides us wider initial conditions for successful quintessence. In fact, even if the universe is initially scalar-field dominated, it eventually evolves into a radiation dominated era in the ${\ensuremath{\rho}}^{2}$-dominant stage. Assuming an equipartition condition, we discuss constraints on parameters, with the result that $\ensuremath{\alpha}g~4$ is required. This constraint also restricts the value of the five-dimensional Planck mass, e.g., $4\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}14}{m}_{4}\ensuremath{\lesssim}{m}_{5}\ensuremath{\lesssim}3\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}13}{m}_{4}$ for $\ensuremath{\alpha}=5. $ For an exponential potential model $V={\ensuremath{\mu}}^{4}\mathrm{exp}(\ensuremath{-}\ensuremath{\lambda}\ensuremath{\varphi}{/m}_{4}),$ we may not find a natural and successful quintessence scenario as it is, while for a kinetic-term quintessence, we find a tracking solution even in the ${\ensuremath{\rho}}^{2}$-dominant stage, rather than the ${\ensuremath{\Omega}}_{\ensuremath{\varphi}}$-decreasing solution for an inverse-power-law potential. Then we do find a slight advantage in a brane world. Only the density parameter increases more slowly in the ${\ensuremath{\rho}}^{2}$-dominant stage, which provides a wider initial condition for successful quintessence.
DSLCD-based Tunnel Secondary Lining Cavities Detection in Ground Penetrating Radar Images

Because the depth is large, the detection area is wide, the interference is much, and the discrimination depends on expert experience, it is difficult to detect cavities inside the secondary lining of the tunnel efficiently. In order to solve this problem, we propose an end-to-end improved Faster R-CNN based network model named Deep Secondary Lining Cavity Detection (DSLCD) network. The model is divided into three parts: the feature extraction network, the region proposal network, and the classification and regression network. In the feature extraction network, a Balanced Feature Pyramid is used to strengthen the multi-layer features, while in the region suggestion network, the Guided Anchoring method is used to improve the model’s adaptability to the various size of the secondary lining voids target and to enhance the accuracy of the secondary lining voids target positioning by optimizing the ROI alignment process. The experimental results show that the DSLCD network has a decent detection accuracy on our GPR image dataset containing both reinforced concrete and plain concrete, which meets the engineering application requirements.
Analysis and Design of High-Efficiency Modular Multilevel Resonant DC-DC Converter

This paper demonstrates a high-efficiency modular multilevel resonant DC-DC converter (MMRC) with zero-voltage switching (ZVS) capability. In order to minimize the conduction loss in the converter, optimizing the root-mean-square (RMS) current flowing through switching devices is considered an effective approach. The analysis of circuit configuration and operating principle show that the RMS value of the current flowing through switching devices is closely related to the factors such as the resonant tank parameters, switching frequency, converter output voltage and current, etc. A quantitative analysis that considers all these factors has been performed to evaluate the RMS current of all the components in the circuit. When the circuit parameters are carefully designed, the switch current waveform can be close to the square waveform, which has a low RMS value and results in low conduction loss. And a design example based on the theoretical analysis is presented to show the design procedures of the presented converter. A 600 W 48 V-to-12 V prototype is built with the parameters obtained from the design example section. Simulation and experiments have been performed to verify the high-efficiency feature of the designed converter. The measured converter peak efficiency reaches 99.55% when it operates at 200 kHz. And its power density can be as high as 795 W/in3.
RTAS: Road Test with Artificial Scenarios

The decision making module in self-driving cars requires extensive performance testings and safety evaluations on real roads. While providing a naturalistic driving environment, the road test poses several problems, including uncontrollable risks, rare complex and dangerous scenarios, and higher costs. Testing in a simulator could provide unrestricted scenarios, but it lacks real-time dynamic feedback from either vehicle or road. To bridge the gap between physical and virtual testing, we propose a novel test and evaluation system, called Road Test with Artificial Scenarios (RTAS), which injects generated virtual scenarios to the physical VUT (Vehicle Under Test) in real time. For virtual scenarios, we propose a deep generative network with the road structure, layout of traffic participants and expected safety critical measurement as inputs. Meanwhile, the VUT is driving in a controlled physical environment (e.g. a test track) and its motion planner is modified by directly taking generated scenarios as inputs. Furthermore, the motion of the VUT is captured by localization devices and passed to the scenario generation as the instant feedback of the VUT. To demonstrate the feasibility of our proposal, we implement a prototype based on a scaled indoor test field, which integrates our scenario generation with Carla simulator and a 1/18 scale vehicle running on a scale indoor test field.
Greek Parliament Members on Instagram: A study on building a political image

This paper focuses on examining how Greek MPs leverage Instagram and carry out a twofold investigation that addresses two main questions: Do politicians prefer to communicate over Instagram their private or public life moments? How does the use of Instagram by Greek politicians affect their perception by the public? To validate our findings, we construct a dataset on Greek MPs’ attributes and proceed with its statistical description and visualization. A hierarchical, multiple linear regression framework is employed to model the number of MPs posts with respect to various predictor variables. Data is gradually introduced, creating a series of models of increased complexity and deducing whether the added predictors account for any more variance, thus either accepting or dropping the respective independent variables. Acquired models can subsequently be compared with ANOVA to determine the significance of the models’ change.
Designing Stateless Control-Plane for Next-Generation Telecom Networks

Control-plane operations are of significant importance in enabling telecom service delivery. The promise of low latency data delivery by 5G networks has encouraged machine-type communication in addition to existing human-type service consumption. This change has greatly challenged the telecom operators to maintain a highly scalable and resilient telecom core to deal with the potentially volatile nature of traffic demand. To that end, sofwarization and virtualization of core network entities have extensively helped to achieve a high degree of flexibility and scalability. Complementing this, separating the computing from the storage as a second degree of decoupling is required to make network functions highly resilient. In this work, we propose the stateless model for control-plane functions to make core network transparent to failures. In addition to achieving resiliency, we present the importance of stateless control-plane functions in reducing overall latency mainly induced during handover procedure execution.
Graphene Plasmonic Tunable Multisensors in Terahertz Band

We propose tunable graphene-based multisensors sustaining absorption efficiency exceeding 95% in the terahertz domain. Due to the graphene components, the absorption frequency is tunable with gate voltage. The perfect absorber can operate as a refractive index sensor with a sensitivity around $54.54\ \mu \mathrm{m}/ \text{RIU}$. More importantly, it can be operated as a temperature sensor with the sensitivity of $13.75\ \mu \mathrm{m}/\mathrm{K}$. Due to the high sensing performance, the structure has a great potential for chemical applications.
Expoentes de Lyapunov e Decomposição de Oseledets

In this dissertation, we present and prove a version of Oseledets’ Multiplicative Ergodic Theorem for Lebesgue spaces. We follow an approach by M. Raghunathan exploring Kingman’s Subadditive Ergodic Theorem, which we also present and prove. Along the way, we analyse Furstenberg-Kesten’s Theorem, seen as a corollary of the last and a seminal form of the first.