pwc-archive/papers-with-abstracts huggingface dataset:
Impact of current limiters and fast voltage boosters in grid-forming VSC-based generators on transient stability
Authors: Régulo E. Ávila-Martínez, Javier Renedo, Luis Rouco, Aurelio Garcia-Cerrada, Lukas Sigrist, Xavier Guillaud, Taoufik Qoria
Date: 2024-05-09 00:00:00

Transient stability is a complex phenomenon presented in multi-machine and multi-converter systems, and it is still considered a key limiting factor for stressed power systems. The increasing integration of non-synchronous generation further emphasises the need to address the challenges of improving the transient stability faced by these power systems. Several studies have focused on developing control strategies for GFM-VSCs to improve transient stability. These strategies include the use of current limiting algorithms and/or control of active/reactive power injections. This paper investigates the impact of fast voltage boosters (FVBs) and hybrid current limiters (HCLs) on transient stability of power systems with 100% grid-forming VSC-based generators. Short-circuit simulations and critical clearing time analysis are performed to evaluate the effectiveness of HCLs and FVBs in improving transient stability. The simulation results demonstrate the effectiveness of these approaches in avoiding the loss of synchronism. This research contributes to the current studies on transient stability in power systems and provides valuable insights into the potential of HCLs and FVBs as effective approaches to improve system stability.
PowerLine Unmanned Surfer (PLUS): Shape Adaptive Dynamics Development and Control Design
Authors: Ujjval Patel, Francis Phillips, Todd Henry, Imraan Faruque
Date: 2024-05-09 00:00:00

This paper introduces the powerline unmanned surfer (PLUS) concept to extend the limited endurance of fixed wing unmanned aerial vehicles (UAVs) via in-flight energy harvesting from overhead electrical distribution power lines, and develops the flight dynamics and control framework to support centimeter-scale longitudinal powerline frequency tracking. The dynamics framework models the UAV's shape adaptive structure, aerodynamic forces, and control inputs, and applies the coupled flight mechanics framework to low clearance tracking of powerline contours. This study develops a "trajectory to shape-adaptive UAV" controller design approach for longitudinal powerline tracking through local spatial frequency matching. The frequency-matching approach dynamically regulates the aircraft modes' frequency to the powerline catenary spatial frequency using a generalized parameter linearization approach. Performance is assessed on an example UAV implementing camber and thickness morphing by quantifying clearance distance from neighborhood to high voltage powerline environments and across span and chord combinations. This approach achieves alternating periods of low-clearance tracking and antiphase oscillation, with 34% of the powerline having tracking error less than 1m. Airfoil thickness increase has a stronger effect over Phugoid mode wavelength than thickness decrease. Wingspan, chord, and powerline parameter sensitivities are quantified, providing a foundation for near-field long-distance powerline tracking and energy harvesting.
Combined topological and spatial constraints are required to capture the structure of neural connectomes
Authors: Anastasiya Salova, István A. Kovács
Date: 2024-05-09 00:00:00

Volumetric brain reconstructions provide an unprecedented opportunity to gain insights into the complex connectivity patterns of neurons in an increasing number of organisms. Here, we model and quantify the complexity of the resulting neural connectomes in the fruit fly, mouse, and human and unveil a simple set of shared organizing principles across these organisms. To put the connectomes in a physical context, we also construct contactomes, the network of neurons in physical contact in each organism. With these, we establish that physical constraints -- either given by pairwise distances or the contactome -- play a crucial role in shaping the network structure. For example, neuron positions are highly optimal in terms of distance from their neighbors. However, spatial constraints alone cannot capture the network topology, including the broad degree distribution. Conversely, the degree sequence alone is insufficient to recover the spatial structure. We resolve this apparent conflict by formulating scalable maximum entropy models, incorporating both types of constraints. The resulting generative models have predictive power beyond the input data, as they capture several additional biological and network characteristics, like synaptic weights and graphlet statistics.
A Sharp Test for the Judge Leniency Design
Authors: Mohamed Coulibaly, Yu-Chin Hsu, Ismael Mourifié, Yuanyuan Wan
Date: 2024-05-10 00:00:00
Tasks: valid

We propose a new specification test to assess the validity of the judge leniency design. We characterize a set of sharp testable implications, which exploit all the relevant information in the observed data distribution to detect violations of the judge leniency design assumptions. The proposed sharp test is asymptotically valid and consistent and will not make discordant recommendations. When the judge's leniency design assumptions are rejected, we propose a way to salvage the model using partial monotonicity and exclusion assumptions, under which a variant of the Local Instrumental Variable (LIV) estimand can recover the Marginal Treatment Effect. Simulation studies show our test outperforms existing non-sharp tests by significant margins. We apply our test to assess the validity of the judge leniency design using data from Stevenson (2018), and it rejects the validity for three crime categories: robbery, drug selling, and drug possession.
New Business Model for Sustainable Retail Company Using Design Thinking Concept
Authors: Anton Kurniawan, Yos Sunitiyoso
Date: 2024-05-10 00:00:00

The waste problem is still becoming a big concern in Indonesia. Waste, especially plastic waste comes from single-use packaging of daily necessities such as personal care and home care. PT. Siklus Refil Indonesia or Siklus, a retail company, comes to offer a sustainable solution of buying daily necessities by refill method. Since April 2020, Siklus has operated in the Greater Jakarta area and already impacted 20,000 customers. However, Siklus must change its new business model due to regulation from the Food and Drug Supervisory Agency (BPOM) that warned the company not to sell personal care who come in direct contact with skin. The warning impacted the decreasing customers, sales, and profit of Siklus. This research has the purpose of determining the new business model of Siklus using the design thinking concept. By this concept, this research empathizes with customers, defines customer needs, and ideates a business model. This research continues to decide the new business model by creating a matrix of stepwise selection. Then this research does a prototype business model and tests the new business model. After doing the process, Return from Home is selected as the new business model for Siklus.
The geometry of consumer preference aggregation
Authors: Fedor Sandomirskiy, Philip Ushchev
Date: 2024-05-09 00:00:00
Tasks: Discrete Choice Models

We revisit a classical question of how individual consumer preferences and incomes shape aggregate behavior. We develop a method that applies to populations with homothetic preferences and reduces the hard problem of aggregation to simply computing a weighted average in the space of logarithmic expenditure functions. We apply the method to identify aggregation-invariant preference domains, characterize aggregate preferences from common domains like linear or Leontief, and describe indecomposable preferences that do not correspond to the aggregate behavior of any non-trivial population. Applications include robust welfare analysis, information design, discrete choice models, pseudo-market mechanisms, and preference identification.
Fire in SRRN: Next-Gen 3D Temperature Field Reconstruction Technology
Authors: Shenxiang Feng, Xiaojian Hao, Xiaodong Huang, Pan Pei, Tong Wei, Chenyang Xu
Date: 2024-05-10 00:00:00

In aerospace and energy engineering, accurate 3D combustion field temperature measurement is critical. The resolution of traditional methods based on algebraic iteration is limited by the initial voxel division. This study introduces a novel method for reconstructing three-dimensional temperature fields using the Spatial Radiation Representation Network (SRRN). This method utilizes the flame thermal radiation characteristics and differentiable rendering in graphics, and combines it with a multi-layer perceptron to achieve a functional representation of the flame temperature field. The effectiveness of SRRN is evaluated through simulated temperature field reconstruction experiments with different levels of complexity. The maximum root mean square error is 10.17, which proves the robustness of the algorithm to Gaussian noise and salt-and-pepper noise. We conducted a butane flame temperature field reconstruction experiment, and the maximum relative error between the reconstruction result and the thermocouple measurement value was 4.86%, confirming that the algorithm can achieve accurate reconstruction.
Multidimensional empirical wavelet transform
Authors: Charles-Gérard Lucas, Jérôme Gilles
Date: 2024-05-10 00:00:00

The empirical wavelet transform is a data-driven time-scale representation consisting of an adaptive filter bank. Its robustness to data has made it the subject of intense developments and an increasing number of applications in the last decade. However, it has been mostly studied theoretically for signals and its extension to images is limited to a particular generating function. This work presents a general framework for multidimensional empirical wavelet transform based on any wavelet kernel. It also provides conditions to build wavelet frames for both continuous and discrete transforms. Moreover, numerical simulations of transforms are given.
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium
Authors: Haris Shuaib, Gareth J Barker, Peter Sasieni, Enrico De Vita, Alysha Chelliah, Roman Andrei, Keyoumars Ashkan, Erica Beaumont, Lucy Brazil, Chris Rowland-Hill, Yue Hui Lau, Aysha Luis, James Powell, Angela Swampillai, Sean Tenant, Stefanie C Thust, Stephen Wastling, Tom Young, Thomas C Booth
Date: 2024-05-07 00:00:00
Tasks: Deep Learning, Diffusion  MRI, Scheduling

Objective: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. Methods: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. Results: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. Conclusion: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. Advances in knowledge: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.
Performance of UAV-based Cell-free mMIMO ISAC Networks: Tethered vs. Mobile
Authors: Xavier A. Flores Cabezas, Isabella W. G. da Silva, Markku Juntti
Date: 2024-05-10 00:00:00
Tasks: Integrated sensing and communication, ISAC, Position

The employment of unmanned aerial vehicles (UAVs) aligned with multistatic sensing in integrated sensing and communication (ISAC) systems can provide remarkable performance gains in sensing, by taking advantage of the cell-free massive multiple-input multiple-output (mMIMO) architecture. Under these considerations, in this paper, the achievable sensing signal-to-noise-plus-interference ratio (SINR) of a cell-free mMIMO ISAC UAV-based network is evaluated for two different deployments of UAVs, namely, mobile and tethered. In both scenarios, a transmit precoder that jointly optimizes the sensing and communication requirements subjected to power constraints is designed. Specifically, for the scenario with mobile UAVs, beyond the transmit precoding, we also optimize the position of the transmit UAVs through particle swarm optimization (PSO). The results show that, although tethered UAVs have a more efficient power allocation, the proposed position control algorithm for the mobile UAVs can achieve a superior gain in terms of sensing SINR.
Electromagnetic Property Sensing in ISAC with Multiple Base Stations: Algorithm, Pilot Design, and Performance Analysis
Authors: Yuhua Jiang, Feifei Gao, Shi Jin, Tie Jun Cui
Date: 2024-05-10 00:00:00
Tasks: Compressive Sensing, Integrated sensing and communication, ISAC, Material Classification

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for future wireless systems. In this paper, we develop a novel scheme that utilizes orthogonal frequency division multiplexing (OFDM) pilot signals to sense the electromagnetic (EM) property of the target and thus identify the materials of the target. Specifically, we first establish an EM wave propagation model with Maxwell equations, where the EM property of the target is captured by a closed-form expression of the channel. We then build the mathematical model for the relative permittivity and conductivity distribution (RPCD) within a predetermined region of interest shared by multiple base stations (BSs). Based on the EM wave propagation model, we propose an EM property sensing method, in which the RPCD can be reconstructed from compressive sensing techniques that exploits the joint sparsity structure of the EM property vector. We then develop a fusion algorithm to combine data from multiple BSs, which can enhance the reconstruction accuracy of EM property by efficiently integrating diverse measurements. Moreover, the fusion is performed at the feature level of RPCD and features low transmission overhead. We further design the pilot signals that can minimize the mutual coherence of the equivalent channels and enhance the diversity of incident EM wave patterns. Simulation results demonstrate the efficacy of the proposed method in achieving high-quality RPCD reconstruction and accurate material classification.
Building Trust in AI-Driven Decision Making for Cyber-Physical Systems (CPS): A Comprehensive Review
Authors: Rahul Umesh Mhapsekar, Muhammad Iftikhar Umrani, Malik Faizan, Omer Ali, Lizy Abraham
Date: 2024-05-10 00:00:00
Tasks: Decision Making

Recent advancements in technology have led to the emergence of Cyber-Physical Systems (CPS), which seamlessly integrate the cyber and physical domains in various sectors such as agriculture, autonomous systems, and healthcare. This integration presents opportunities for enhanced efficiency and automation through the utilization of artificial intelligence (AI) and machine learning (ML). However, the complexity of CPS brings forth challenges related to transparency, bias, and trust in AI-enabled decision-making processes. This research explores the significance of AI and ML in enabling CPS in these domains and addresses the challenges associated with interpreting and trusting AI systems within CPS. Specifically, the role of explainable AI (XAI) in enhancing trustworthiness and reliability in AI-enabled decision-making processes is discussed. Key challenges such as transparency, security, and privacy are identified, along with the necessity of building trust through transparency, accountability, and ethical considerations.
Performance Analysis of Uplink/Downlink Decoupled Access in Cellular-V2X Networks
Authors: Luofang Jiao, Kai Yu, Jiacheng Chen, Tingting Liu, Haibo Zhou, Lin Cai
Date: 2024-05-10 00:00:00

This paper firstly develops an analytical framework to investigate the performance of uplink (UL) / downlink (DL) decoupled access in cellular vehicle-to-everything (C-V2X) networks, in which a vehicle's UL/DL can be connected to different macro/small base stations (MBSs/SBSs) separately. Using the stochastic geometry analytical tool, the UL/DL decoupled access C-V2X is modeled as a Cox process, and we obtain the following theoretical results, i.e., 1) the probability of different UL/DL joint association cases i.e., both the UL and DL are associated with the different MBSs or SBSs, or they are associated with different types of BSs; 2) the distance distribution of a vehicle to its serving BSs in each case; 3) the spectral efficiency of UL/DL in each case; and 4) the UL/DL coverage probability of MBS/SBS. The analyses reveal the insights and performance gain of UL/DL decoupled access. Through extensive simulations, \textcolor{black}{the accuracy of the proposed analytical framework is validated.} Both the analytical and simulation results show that UL/DL decoupled access can improve spectral efficiency. The theoretical results can be directly used for estimating the statistical performance of a UL/DL decoupled access C-V2X network.
Cooperative ISAC Networks: Opportunities and Challenges
Authors: Kaitao Meng, Christos Masouros, Athina P. Petropulu, Lajos Hanzo
Date: 2024-05-10 00:00:00
Tasks: ISAC

The integration of sensing and communication (ISAC) emerges as a cornerstone technology for the forth upcoming sixth generation era, seamlessly incorporating sensing functionality into wireless networks as a native capability. The main challenges in efficient ISAC are constituted by its limited sensing and communication coverage, as well as severe inter-cell interference. Network-level ISAC relying on multi-cell cooperation is capable of effectively expanding both the sensing and communication (S&C) coverage and of providing extra degrees of freedom (DoF) for realizing increased integration gains between S&C. In this work, we provide new considerations for ISAC networks, including new metrics, the optimization of the DoF, cooperation regimes, and highlight new S&C tradeoffs. Then, we discuss a suite of cooperative S&C architectures both at the task, as well as data, and signal levels. Furthermore, the interplay between S&C at the network level is investigated and promising research directions are outlined.
Near-Field Channel Characterization for Mid-band ELAA Systems: Sounding, Parameter Estimation, and Modeling
Authors: Wei Fan, Zhiqiang Yuan, Yejian Lyu, Jianhua Zhang, Gert Pedersen, Jonathan Borrill, Fengchun Zhang
Date: 2024-05-10 00:00:00
Tasks: parameter estimation

6G communication will greatly benefit from using extremely large-scale antenna arrays (ELAAs) and new mid-band spectrums (7-24 GHz). These techniques require a thorough exploration of the challenges and potentials of the associated near-field (NF) phenomena. It is crucial to develop accurate NF channel models that include spherical wave propagation and spatial non-stationarity (SnS). However, channel measurement campaigns for mid-band ELAA systems have rarely been reported in the state-of-the-art. To this end, this work develops a channel sounder dedicated to mid-band ELAA systems based on a distributed modular vector network analyzer incorporating radio-over-fiber (RoF), phase compensation, and virtual antenna array schemes. This novel channel-sounding testbed based on off-the-shelf VNA has the potential to enable large-scale experimentation due to its generic and easy-accessible nature. The main challenges and solutions for developing NF channel models for mid-band ELAA systems are discussed, including channel sounders, multipath parameter estimation algorithms, and channel modeling frameworks. Besides, the study reports a measurement campaign in an indoor scenario using a 720-element virtual uniform circular array ELAA operating at {16-20} GHz, highlighting the presence of spherical wavefronts and spatial non-stationary effects. The effectiveness of the proposed near-field channel parameter estimator and channel modeling framework is also demonstrated using the measurement data.
Human Skin Permittivity Characterization for Mobile Handset Evaluation at Sub-THz
Authors: Bing Xue, Katsuyuki Haneda, Clemens Icheln, Juha Ala-Laurinaho
Date: 2024-05-09 00:00:00

This manuscript proposes a method for characterizing the complex permittivity of the human finger skin based on an open-ended waveguide covered with a thin dielectric sheet at sub-terahertz frequencies. The measurement system is initially analyzed through full-wave simulations with a detailed finger model. Next, the model is simplified by replacing the finger with an infinite sheet of human skin to calculate the forward electromagnetic problem related to the permittivity characterization. Following this, a radial basis network is employed to train the inverse problem solver. Finally, the complex permittivities of finger skins are characterized for 10 volunteers. The variations in complex relative permittivity across different individuals and skin regions are analyzed, revealing a deviation of $<\pm 1.5$ for both the dielectric constants and loss factors across 140 to 220 GHz. Repeated measurements at the same location on the finger demonstrate good repeatability with a relative estimation uncertainty $<\pm 1.5\%$.
Optimal epidemic control by social distancing and vaccination of an infection structured by time since infection: the covid-19 case study
Authors: Alberto D'Onofrio, Mimmo Iannelli, Piero Manfredi, Gabriela Marinoschi
Date: 2024-05-10 00:00:00

Motivated by the issue of COVID-19 mitigation, in this work we tackle the general problem of optimally controlling an epidemic outbreak of a communicable disease structured by time since exposure, by the aid of two types of control instruments namely, social distancing and vaccination by a vaccine at least partly effective in protecting from infection. Effective vaccines are assumed to be made available only in a subsequent period of the epidemic so that - in the first period - epidemic control only relies on social distancing, as it happened for the COVID-19 pandemic. By our analyses, we could prove the existence of (at least) one optimal control pair, we derived first-order necessary conditions for optimality, and proved some useful properties of such optimal solutions. A worked example provides a number of further insights on the relationships between key control and epidemic parameters.