Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Base Station Sleeping and Functional Split Orchestration in Crosshaul-Based V-RAN

The intensive deployment of base stations (BSs) in the radio access network (RAN) incurs huge energy consumption and operating overheads. Although the centralized RAN (C-RAN) architecture can significantly relieve the BS overheads by centralizing BS functions, the strict front-haul requirements of C-RAN make it challenging to realize a fully centralized RAN. Recently, the virtualized RAN (V-RAN) architecture has been proposed, allowing flexible function split (FS) and crosshaul to balance the centralization and mid-haul requirements. On the other hand, considering the tidal effect of traffic, sleep some BSs with low loads and migrating their traffic to other BSs is an effective way to further reduce energy consumption. In this paper, we investigate joint BS sleeping and FS orchestration in crosshaul-based V-RAN that jointly optimizes BS working mode, FS, traffic migration, and routing selection. The joint optimization model is formulated as a mixed-integer nonlinear programming (MINLP) that minimizes total expenditure, including RAN energy consumption and operating overheads. Considering the complexity of the problem, we propose a heuristic algorithm to solve it in polynomial time. Simulation results validate that our algorithm can save significant expenditure compared to baselines, and the results can reach within 1.13 times the optimum in a relatively short time.
Particulate Matter Detection in Mines Using 3D Light Detection and Ranging Technology

This paper proposes a novel portable prototype and self-contained Air Quality (AQ) monitoring device that utilizes Light Detection and Ranging (LiDAR) technology to take its measurements. The novel device aims to improve mining safety by collecting and analyzing the AQ inside mines and displaying the real-time conditions to personnel. The intent is to create a 3D map of the environment and display potentially hazardous Atmospheric Particulate Matter (APM). To achieve this goal, we prototype a portable, compact, and easy-to-operate system that utilizes LiDAR to detect APM. Then, we propose how the collected data can be used to calculate real-time AQ conditions. Finally, we illustrate selected results to show the importance and feasibility of our novel prototype.
Task-Oriented Over-the-Air Computation for Multi-Device Edge Split Inference

A task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device edge split inference system. In the considered system, local noise-corrupted feature vectors are aggregated at the server via AirComp to generate a denoised one for the subsequent inference task. By considering classification tasks, the transmit precoders at edge devices and receive beamforming at edge server are jointly designed in an effort to rein in the aggregation error and maximize the inference accuracy, which is approximately measured by a surrogate but more tractable metric called discriminant gain. It is found that the conventional AirComp beamforming design for minimizing the mean square error between the aggregated feature vector by AirComp and the ideally aggregated one may not lead to the optimal classification accuracy, as it fails to respect the fact that some feature dimensions are more sensitive to the aggregation error than the others in terms of the classification accuracy. To tackle this issue, a new task-oriented AirComp scheme is proposed for directly maximizing the derived discriminant gain. The superiority of the proposed scheme over the heuristic benchmarks is verified by extensive experimental results based on a concrete inference task of human motion recognition.
Compensated Sequential Model Predictive Control for Permanent Magnet Synchronous Machine

This paper proposes a new method for controlling permanent magnet synchronous machines with and without field weakening. It is proposed to use the Sequential Model Predictive Control technique with high-frequency injection and reference generation from the analytical solution of the Maximum Torque Per Ampere (MTPA) curve. The results show a better torque ripple and current total harmonic distortion concerning the Field Oriented Control technique in nominal conditions.
Local time variations of high‐energy plasmaspheric ion pitch angle distributions

Recent observations from the Van Allen Probes Helium Oxygen Proton Electron (HOPE) instrument revealed a persistent depletion in the 1–10 eV ion population in the postmidnight sector during quiet times in the 2 < L < 3 region. This study explores the source of this ion depletion by developing an algorithm to classify 26 months of pitch angle distributions measured by the HOPE instrument. We correct the HOPE low energy fluxes for spacecraft potential using measurements from the Electric Field and Waves (EFW) instrument. A high percentage of low count pitch angle distributions is found in the postmidnight sector coupled with a low percentage of ion distributions peaked perpendicular to the field line. A peak in loss cone distributions in the dusk sector is also observed. These results characterize the nature of the dearth of the near 90° pitch angle 1–10 eV ion population in the near‐Earth postmidnight sector. This study also shows, for the first time, low‐energy HOPE differential number fluxes corrected for spacecraft potential and 1–10 eV H+ fluxes at different levels of geomagnetic activity.
Short to Medium-Reach Wireline Transceivers Using Single-Ended Signaling, Clock Forwarding, and Spatial Encoding for Die-to-Die Applications

This paper presents architectures for short to medium-reach wireline transceivers for die-to-die applications. A 480Gb/s/mm1.7pJ/b wireline transceiver in 7nm CMOS is presented. A 6b/7b spatial encoder improves reference noise immunity. Clock skew and dutycycle calibrations are used in the transmitter and receiver. Offset correction and clock recovery continuously operate on regular data traffic. The transceiver achieves an eye width of approximately 0.5$\cup$1 with a BER <1e-15 while operating at 40Gb/s/wire.
Taking Standardized C-ITS Communication to the next LEVEL - Lightweight and Extensible Vehicle-to-Everything Library

Cooperative Intelligent Transportation Systems (C-ITSs) facilitate safe, efficient, and sustainable driving through vehicle-to-everything (V2X) communication. Standardized services and messages regulate the exchange of basic information required for an unrestricted view of the traffic situation. However, many applications need information that goes beyond. Furthermore, compliance with these standards is cumbersome when testing on a small-scale or in simulation. The result: standards are often bypassed. Therefore, this paper presents a standard-compliant, lightweight, easy-to-integrate library that is application-independent and open source. We integrate the library in a small-scale test bed and use Decentralized Environmental Notification Messages (DENMs) to trigger the speed reduction of a vehicle in a bad weather situation. Through Cooperative Awareness Messages (CAMs), the vehicle shares its status information, including its current velocity. The experiment demonstrates a working location-dependent exchange of information between a Road Side Unit (RSU) and the vehicle, utilizing LEVEL as a communication stack.
GADaM on the road - Smart Approach to Multi-Access Networks: Analytical and Practical Evaluation in Various Urban Mobile Environments

Multipath scheduling has been a hot research topic in recent years thanks to the development of multipath transport protocols (MP-TCP and MP-QUIC) and the wide deployment of 5G infrastructures. These schedulers take advantage of multiple physical interfaces on end-user devices, thus improving performance and reliability. While Peekaboo, the state-of-the-art scheduler, seems to provide the best performance in some specific environments, some evidence is that other existing schedulers may outperform it in more realistic network conditions. Based on this observation, we proposed GADaM, stands for Generic Adaptive Deep-learning based Multipath scheduler selector. GADaM’s role is to select the most appropriate scheduler under specific network conditions dynamically. The proposed paradigm proved its effectiveness in simulations, but its behavior in more realistic (typically mobile) environments remains largely unknown. This paper presents an experimental evaluation of GaDAM in real-world scenarios performed in an urban area near Paris, France. We design and implement a framework to serve this purpose, which takes mobility and fairness into consideration. Our results confirm the advantage of GaDAM’s approach compared to the deterministic scheduler selection approach.
Information-Aware Sensing Framework for Long-Lasting IoT Sensors in Greenhouse

A sensor network is an underpinning infrastructure that enables various future IoT applications, such as precision agriculture, smart farm, and greenhouse monitoring. However, these sensor devices often suffer from short-lived battery lifetime that incurs frequent maintenance operation. Although there have been a few attempts to smartly reduce the power consumption associated with communication tasks of the sensors, very few have addressed the power consumption of sensing tasks. In light of this shortcoming, we propose an information-aware sensing framework that adaptively adjusts the sensing interval for energy-saving operations based on the learned behavior of the sensor data. To prove the effectiveness of the proposed framework, we have deployed four BLE beacons equipped with luminosity and temperature sensors to collect real-life data from a desert greenhouse, which is then used to train and evaluate our proposed framework. Additionally, we have implemented the proposed framework on a commodity BLE beacon device to validate the energy-saving performance of the proposed framework. The results demonstrate that the proposed framework can effectively reduce the energy consumption involved in sensing tasks by 30% and extend the battery lifetime by up to 75%.
WiFi sensing of Human Activity Recognition using Continuous AoA-ToF Maps

Joint communication and sensing technique has been adopted for smart home design and other applications recently. WiFi sensing, which utilizes mutually orthogonal channel response to monitor the changes in the medium, is regarded as one of key techniques in this field. Human activity recognition using wireless communication systems is a key function of future internet of things systems. The effective and inexpensive WiFi sensing system can help people with device-free controlling, and healthcare monitoring without concern of image information leakage that uses a camera system. In this article, we proposed a continuous angle of arrival and time of flight (AoA-ToF) maps based method that adopts multiple signals classification analysis on commercial and off-the-shelf WiFi devices to detect human activities. Our experimental results ensure the effectiveness of the proposed system for the human activity recognition (HAR) task with 8 activities among 5 users in three directions. The performance of our system achieves 85.6% accuracy on average. Meanwhile, we evaluate the performance of our system under different conditions, including direction and user identity. The results show the system’s robustness for human activity recognition under such conditions.
Reinforcement Learning Based Coexistence in Mixed 802.11ax and Legacy WLANs

The new 802.11 amendment, 802.11ax, represents a significant shift in the WLAN operation, specially in the MAC layer where the access mechanism is now OFDMA. In particular, the Access Point (AP) is now responsible for scheduling the terminals’ transmissions, which avoids collisions and results in an efficient usage of the spectrum. However, a full transition to this new technology is not foreseeable for several years, and until then mixed scenarios that also include legacy stations will be predominant. In this context, where both the AP and the legacy stations use CSMA/CA to access the channel, a very challenging aspect is the coexistence between both types of stations, where naturally the AP should have priority but legacy stations should not be excluded. In this paper we present a deep reinforcement learning system that adjusts the contention window so as to maximize a certain notion of fairness. Differently to previous proposals, none of which to the best of our knowledge focused on this mixed scenario, the choice of parameters that characterize the environment is informed on existing 802.11 models. This results for instance in a stable choice of the contention window and larger throughputs. Thorough simulations corroborate the performance of the proposed method, which we make available at https://github.com/ffrommel/RLinWiFi.
Design of Wideband Conformal Vivaldi Antenna Array

This paper presents a Vivaldi antenna designed for IEEE C-band applications from 4.3 to 9.8 GHz with the aim of achieving -10 dB bandwidth from 4–9 GHz with a gain of 5–9 dBi throughout the band. The designed Vivaldi antenna is fed through a microstrip line for smoother slot transition. The Vivaldi antenna was used to develop a conformal antenna array for wide-angle beam scanning mounted on the periphery of a sphere which provides 360° beam scanning in the azimuthal plane and $\pm 30^{o}$ in the elevation angle. Vivaldi antenna provides a maximum realized gain of 8.32 dBi gain at 7 GHz at $\theta=0^{o}$. Two different antenna arrays are presented. The first antenna array consists of $1\times 5$ antenna elements and provides a beam scanning capability of $\theta=\pm 30^{0}$ in the $\phi=90^{o}$ plane with a realized gain of 13.79 dBi, 12.56 dBi, and 12.00 dBi at the target scan angles of $\theta_{t}=0^{o},\ 15^{o}$, and $\theta_{t}=30^{o}$ respectively. Finally, a 3-D conformal Vivaldi antenna array is presented with a maximum realized gain of 19.97 dBi gain at $\theta=0^{o}$. ANSYS HFSS was used to verify the validity of the designs.
Propagation Graph Representation Learning and Its Implementation in Direct Path Representation

This paper proposes a novel graph-learning-based radio propagation model, referred to as propagation graph representation learning. Recent advancements in deep learning have succeeded in developing site-specific path loss prediction; however, predicting shadowing without on-site measurement data is still a critical challenge. Propagation graph representation learning aims to express the site-specific propagation process. In the envisioned graph, nodes represent a transmitter, receivers, and obstructions, while edges represent propagation conditions between nodes. The graph structure enables us to recognize the reflection, diffraction, and shielding for accurate shadowing estimation. We also propose a feedforward neural network (FFNN) based representation model in a direct path scenario. Through the simulation using actual datasets in urban areas, we demonstrate that the proposal achieves twenty times faster computation than ray tracing while predicting shadowing well.
Identification of Elevated Aerosol Layers and their properties by using ground-based aerosol measurements over the foothills of the central Himalayas

Previous studies have identified and examined the elevated aerosol layers (EAL) using LiDAR measurements from CALIPSO or MPLNET. Here we identify the EALs and their properties using ground-based aerosol measurements in Mukteshwar, a remote site in the Himalayan Mountains in northern India, from 2006–2013 (March-August only). EAL events are screened from the 8-year measurements by considering the ratio of PMIO to PM2.5 equal to or greater than two. BC increases by two to threefold and shows a good association with the PM concentration during the EAL events. The presence of strongly absorbing and scattering aerosol in EAL can be seen by their varying range of 10–50 Mm-1 and 50–350 Mm-1, respectively. CALIPSO, MPLNET, Dust Aerosol Optical Thickness, and NASA'S Worldview Earthdata are used to verify the EAL events identified from the ground-based aerosol measurements. The back-trajectory analysis clearly shows an influx of airmass from Arabia's arid regions and the west Arabian sea.
Optimal Joint Radar and Communications Beamforming for the Low-Altitude Airborne Vehicles in SAGIN

A symbolic feature that integrates the space, air and ground network components for service in challenging and remote areas is being envisaged with continuity and high mobility of the 6G mobile system. Simultaneously providing sensing and connectivity over the radio signal becomes essential to support the management of low-space air crafts in the mobile system with limited spectrum resources. In this paper, we investigate the optimal joint radar and communications beamforming scheme with the presence of the clutter to support the low-space airborne vehicles, e.g. unmanned aerial vehicles or drones that are essential components of Non-Terrestrial Networks. The proposed scheme achieves the optimal signal-to-clutter-plus-noise ratio of the sensing function while maintaining the performance of the predefined communications. The novel application of approximations and rank-reduction algorithms in this work maximizes the joint radar and communications performance, for a system model similar to the one that is solved with a local optimum solution in a previous work. The numeric simulation results show that our approach maintains low complexity while guaranteeing the global optimum beamforming solution.
Identification of aerosol types over the central Indo-Gangetic Plain using multi-satellite observations

Identification and quantification of aerosol types in the central Indo-Gangetic Plain (IGP) region is done using long-term (2005 - 2019) and simultaneous datasets, from satellite and reanalysis, of aerosol optical depth (AOD), absorbing aerosol index (AAI) and Ångström exponent (α). In order to identify aerosol types in the central IGP, we used two classification methods by assigning the predefined thresholds of (i) AOD and $\alpha$ and (ii) AOD, a and AAI. Results derived from Method 1 suggest anthropogenic aerosols (AA) are dominant during the study period followed by mixed type (MT) and biomass burning (BB) aerosols over Gorakhpur. From Method 2, it is apparent that absorbing aerosols dominate during pre-monsoon (PM), post-monsoon (PoM) and winter (W) seasons, and non-absorbing aerosols during monsoon (M). BB aerosols derived from method 1 and carbonaceous aerosols from method 2 showed the highest contribution during PoM and W season. This is due to increased burning of agricultural waste and forest fires in the IGP region during this period. Dust aerosols derived from both methods showed maximum contribution during PM season, with marginal contributions during M and W seasons, respectively, and negligible during PoM season.
Static Human Localization Using FMCW MIMO Radar

Although a FMCW MIMO radar can effectively track moving people, it is a challenge for radar to detect static people. When a person is at rest, radar signals reflected from the human body are mixed up with that from stationary objects. In this paper, we present a static human localization method using FMCW MIMO radar. The method uses radar signal variations and spectral characteristics caused by vital activities to identify human locations. We test the proposed method with an off-the-shelf radar. The results show that our method can detect human presence and get human locations in a large area. Furthermore, the detected human location can be used to judge whether the target is lying on the floor, which is helpful for fall detection.
Integrated Braking and Traction Torque Vectoring Control Based on Vehicle Yaw Rate for Stability Improvement of All-Wheel-Drive Electric Vehicles

EVs with independent wheel driving greatly improve vehicle stability in poor road conditions. Wheel torques can be precisely controlled through electric motors driven using advanced technologies. As a result, various types of advanced chassis assistance systems (ACAS) can be implemented. This paper proposes an integrated torque vectoring control based on wheel slip regulation in both braking and traction modes. For generating the corrective yaw moment, the vehicle yaw rate is monitored. The corrective yaw moment is distributed into traction and braking torques based on an equal-opposite components approach. The proposed torque vectoring control scheme is validated in simulation and the results show its superiority when compared to conventional schemes.
A Steganographic Technique for JPEG Using Modified Quantization Table

This paper proposes a novel and high capacity steganographic approach based on Discrete Cosine Transformation (DCT) and JPEG compression. JPEG technique divides the input image into non-overlapping blocks of 8×8 pixels and uses the DCT transformation. In our proposed method, we also divide the cover-image into disjoint blocks of 8×8 pixels. For each quantized DCT block, the least two-significant bits (2-LSBs) of each middle and high frequency coefficient are modified to embed two secret bits. Our experiment result shows that the proposed approach can provide higher information- hiding capacity and better stego image quality than Chang et al. methods based on the conventional blocks of 8×8 pixels.
On-line control of the limbs during bimanual reaching is independent with directly- and symbolically-cued target perturbations

ii Table of contents . iii List of tables. v List of figures. vi Acknowledgements . vii Dedication . viii 1 Introduction. 1 1.1 Bimanual reaching . 1 1.2 Cortical crosstalk . 4 1.2.1 Response programming . 4 1.2.2 Stimulus identification or response selection . 5 1.2. 3 Response selection . 7 1.3 Spatial coupling of movement trajectories . 8 1.3.1 On-line control of bimanual reaches. 9 2 Experiment one . 12 2.1 Methods. 12 2.1.1 Participants. 12 2.1.2 Task and apparatus. 12 2.1.3 Design . 14 2.1. 4 Data analysis . 15 2.2 Results and discussion . 17 2.2.1 No difference in the onsets of adjustments with direct or symbolic cues. 22 2.2.2 Movement times of the perturbed limbs were longer than the unperturbed limbs . 23 2.2. 3 Constant and variable error were comparable with the unperturbed limbs and controls . 24 2.2. 4 Forward and lateral positions did not reveal spatial interference . 25 3 Experiment two . 30 3.1 Methods. 30 3.1.1 Participants. 30 3.1.2 Task and apparatus. 30 iv 3.1.3 Design . 31 3.1. 4 Data analysis . 32 3.2 Results. 33 3.2.1 Movement times of the perturbed limbs were longer than the unperturbed limbs . 37 3.2.2 Onsets of adjustments were longer with symbolic cues than direct cues . 38 3.2. 3 Constant and variable error were comparable with the unperturbed limbs and controls . 39 3.2. 4 Forward positions revealed small, transient spatial interference that was larger with symbolic cues than direct cues. 40 4 General discussion . 44 4.1 Spatial interference . 44 4.2 Challenges to independent on-line control . 48 4.3 Cue-to-target translation . 49 4.4 Temporal interference. 50 4.5 Conclusion . 50 References. 52 Appendicies. 55 Appendix A: Pilot experiment . 55 Appendix B: Ethics certificate . 58
THE ABDUS SALAM INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS HORSESHOES CHAOS AND STABILITY OF A DELAYED VAN DER POL-DUFFING OSCILLATOR UNDER A BOUNDED DOUBLE WELL POTENTIAL

In this paper a van der Pol-Duffing oscillator with a bounded double well potential and a delayed (positive and negative) position and velocity feedback is considered. Attention is focussed on the effects of time delay on stability, escape motion and horseshoes chaos. Using Forde and Nelson’s theorem, harmonic balance and Melnikov criterion for chaos, the boundary conditions for such phenomena are derived. It appears that, time delay can be used as simple switch to avoid and/or create complex behavior of the model.