Today, remote sensing (RS) can offer earth observation data with various temporal-spatial-spectral characteristics by leveraging different kinds of sensors, forming a multimodal data framework. Shifting the perspective from orbit to ground, text, points of interest (POIs), street-view images and other geospatial data can provide information related to but distinct from RS images. In order to enrich the information capacity and build a comprehensive understanding, multimodal learning in the RS field attempts to simultaneously process and apply data of various modalities. However, multimodal approaches in supervised manner often require expensive human annotation, impeding the full release of data potential. To alleviate this issue, self-supervised learning (SSL) has become an attractive way to learn from unlabeled data, which can extract meaningful representations by designing effective pretext learning objectives. The strengths of label-free, featureextraction and task-agnostic allow SSL to easily scale up the data and model size, paving the way for RS multimodal foundation model (FM). In this survey, we systematically review the evolving field of RS multimodal SSL. In terms of data modalities, this review not only covers research utilizing multimodal RS images but also includes studies that integrate RS images with other forms of geospatial data, providing a comprehensive overview of the data integration scenarios. At the methodology level, multimodal SSL requires the synergy of learning objective and data fusion module. To provide a systematical framework for understanding the trends and challenges of RS multimodal SSL approaches, we present a structured methodology taxonomy in terms of multimodal SSL objective and data fusion strategy. And for each type of method, we summarize its characteristics, key elements and common scenarios. As for the application areas, we categorize them into four classes, including image processing, image understanding, vision-language understanding, and socioeconomic prediction. In addition, we also provide a systematical review of RS multimodal FMs based on SSL. Finally, we discuss challenges and future directions of RS multimodal SSL. It is our aspiration that this review will act as a starting point for researches to examine the advancements and engage in the exploration of RS multimodal SSL studies.

Digital Twins (DTs) are dynamic digital representations of physical objects and systems, including their associated processes and environments. Using real-time data analytics, modelling, simulation and 'what-if' scenarios, they can enable valuable understanding and decision-support for managing a system. One potential application of DTs is the management of natural landscapes. However, despite the critical impact of humans on the natural environment, none of the DTs of the natural environment found in the literature include the human systems in that environment. We propose a modular framework for integrating human systems within a DT of the natural environment, to facilitate simulation and modelling of a range of management contexts, objectives and stakeholders across time and space. We then propose four principles as the theoretical basis for modelling the socio-ecological governance systems that underpin DTs of human systems for managing the natural environment. We also provide a use case related to area-wide integrated pest management as an example of the potential application of the proposed framework for collectively managing the natural landscape. The composition and characteristics of this use case as a DT and examples of user engagement at multiple spatial and temporal scales are also described. Finally, we identify some methods such as agent-based modelling, reinforcement learning, and graph neural networks that could be used to incorporate human systems in a DT, along with some examples of their use for similar tasks from the literature. Integrating human systems in DTs of the natural environment will facilitate the development of novel digital decision-support tools that provide stakeholders with different perspectives of the shared natural environment and enable them to understand the impact of various management methods and actions.

The intersection of generative AI and art is a fascinating area that brings both exciting opportunities and significant challenges, especially when it comes to identifying synthetic artworks. This study takes a unique approach by examining diffusionbased generative models in the context of Indian art, specifically focusing on the distinctive style of Jamini Roy. To explore this, we fine-tuned Stable Diffusion 3 and used techniques like ControlNet and IPAdapter to generate realistic images. This allowed us to create a new dataset that includes both real and AI-generated artworks, which is essential for a detailed analysis of what these models can produce. We employed various qualitative and quantitative methods, such as Fourier domain assessments and autocorrelation metrics, to uncover subtle differences between synthetic images and authentic pieces. A key takeaway from recent research is that existing methods for detecting deepfakes face considerable challenges, especially when the deepfakes are of high quality and tailored to specific cultural contexts. This highlights a critical gap in current detection technologies, particularly in light of the challenges identified above, where highquality and culturally specific deepfakes are difficult to detect. This work not only sheds light on the increasing complexity of generative models but also sets a crucial foundation for future research aimed at effective detection of synthetic art.

High-temperature superconductors (HTS) could enable a new paradigm for lightweight and efficient electric machine designs. While superconducting rotors have been successfully implemented, the adoption of superconducting armatures has been hindered by high AC losses, leading to impractically high cryogenic cooling demands. This paper introduces a slotted torus axial flux machine (AFM) topology to address this challenge, leveraging toroidal windings to align HTS tapes with slot leakage flux, avoiding complex end-windings while achieving AC losses at least an order of magnitude below HTS armatures with tooth-coil tape orientation. A 1-MW proof-of-concept aviation motor design demonstrates the feasibility of this approach, achieving an active torque density of 51.1 Nm/kg with HTS AC losses below 0.03 % of the output power and total cryogenic losses slightly exceeding 0.1 % when including the steel losses. The study highlights the importance of HTS tape orientation within the slot and using flux diverters for the end-windings to minimize losses. Further, it illustrates how an even higher cryogenic efficiency can be reached by lowering the electrical frequency. The promising results motivate further development and optimization of superconducting machines with this topology.

This study presents the fabrication and characterization of a self-powered perovskite photodetector with a fast response time. We fabricated a RbCsMAFAPb(IBr)3 perovskite photodetector in an n-i-p planar architecture and systematically analyzed its performance. The device achieves an external quantum efficiency (EQE) exceeding 55% and a peak responsivity of 0.306 A/W at 730 nm. At the same wavelength, it demonstrates a detectivity of 1.183 × 10 9 Jones. With a rise time of 0.286 ms, the detector exhibits a competitive response speed among perovskite photodetectors. The device exhibited good stability over the course of 10 days. Furthermore, we investigated the incorporation of Al2O3 nanoparticles in the electron transport layer (ETL) and their impact on the morphology of the perovskite film.

Ferrite plates are essential elements in wireless power transmission systems. Their presence is critical to achieving adequate magnetic coupling and minimizing stray magnetic field. At the same time, reducing the volume of ferrite used in the vehicle assembly (VA) offers significant benefits in terms of cost and weight reduction. Topology optimization methods are particularly effective tools for applications like Wireless Power Transfer, where the magnetic field is not confined to a well-defined volume, unlike in classical electrical machines. In the present work, the Solid Isotropic Material with Penalization (SIMP) methodology is used to achieve an optimized reduced-volume design of the ferrite plate of a SAE-compliant VA that maintains magnetic interoperability with the starting design. The optimized plate was manufactured and its performance was experimentally validated and compared to the original VA. The results show that the fabricated plate is able to maintain a mutual inductance reduction of less than 10% with a 55% reduction in material volume, thus meeting the magnetic interoperability requirements outlined in the SAE J2954 standard. The impact of the material reduction on the system's losses was also evaluated, revealing that the proposed solution results in a 2% increase in losses when tested on a 3.3 kW test bench.

Walking is commonly seen as a simple and effortless activity. In reality, it arises from a complex and intricately coordinated neuromechanical system. To effectively assist or replicate this process, artificial devices must adapt seamlessly to the dynamic goals and environments of their users. Virtual environments provide an accessible and low-risk way to accelerate the early-stage development of these technologies. To capture the challenge inherent in this task, these simulations must reproduce the complexities of movements and environments. Here, we present a locomotion synthesis system that allows us to simulate non steady-state walking on uneven surfaces and obstacles with quantitative measures of kinematics, kinetics and stability. We then applied this environment to trial a prototype compliant prosthetic foot, and compared the gait resulting from its use with that of a rigid foot. We contrasted our results with an experimental study with a real prosthesis user and corroborate potential benefits that compliance can provide during obstacle navigation. Our results demonstrate the application of simulated locomotion agents as subjects in a virtual gait lab. This approach could lower the participation burden on the patient population when iterating novel concepts for the design of prosthetic devices.

This paper introduces DetectBERT, a cuttingedge deep learning framework designed for statement-level vulnerability detection (SVD) in source code, with a focus on Python. In contrast to conventional vulnerability detection methods that rely on graph-based structures like Control Flow Graphs (CFG) or Data Dependency Graphs (DDG), DetectBERT utilizes the BERT transformer encoder to learn contextual relationships between code statements directly from the text. The model consists of two BERT-based layers: one dedicated to feature extraction and the other for classification. By eliminating the need for graph-based representations, DetectBERT offers significant advantages, such as handling the dynamic nature of programming languages and managing variations in code across different versions. This approach also reduces reliance on language-specific graph generation tools, enhancing its flexibility and scalability. Experimental results on publicly available datasets show that DetectBERT outperforms traditional graph-based methods in vulnerability detection. Moreover, the paper introduces a novel preprocessing pipeline that labels Python code by analyzing commit history, further boosting the model's accuracy and resilience. DetectBERT's design is extendable to other programming languages, and the complete implementation is made available to foster continued research and practical applications in the domain of software security.

We propose a new high-capacity photonic Ising machine (PIM) that employs ultrafast coherent optical pulse propagation in a 50 km-long passive recirculating fiber loop and analyze its performance including a new bifurcation mechanism. Although analog optical phenomena (laser oscillation) such as those provided by a degenerate optical parametric oscillator (DOPO) and an opto-electric oscillator (OEO) have been used in conventional PIMs to generate a third-order polynomial bifurcation, in other words a Van der Pol type bifurcation with the rotating wave approximation, we newly adopted a passive recirculating fiber loop and digital feedback nonlinearity for the generation of a pitchfork-type bifurcation. The present PIM has no laser oscillation. The generation of the bifurcation can be very stably realized by introducing a digital nonlinearity, for example a tanh function, which enabled us to solve a max-cut problem of 100,000 nodes. We further describe the first result obtained for a 1 million node max-cut problem by employing the present PIM software in a high-performance computer (HPC).

A fundamental objective of the forthcoming sixthgeneration wireless networks is to concurrently serve a vast array of devices many of which, such as Internet-of-Things (IoT) sensors, are projected to have low power requirements or even operate in a battery-free manner. To achieve this goal, nonorthogonal multiple access (NOMA) and ambient backscatter communications (AmBC) are regarded as two pivotal and promising technologies. In this work, we present a novel analytical framework for studying the reliability and security of uplink NOMA-based AmBC systems. Specifically, closed-form analytical expressions for both NOMA-users' and IoT backscatter device's (BD's) outage probabilities (OPs) are derived for both cases of perfect and imperfect successive interference cancellation (SIC). In addition, assuming that one NOMA-user transmits an artificial noise in order to enhance system's security, the physical layer security (PLS) of the system is investigated by extracting analytical expressions for NOMA-users' and BD's intercept probabilities (IPs). To gain insightful understandings, an asymptotic analysis is carried out by focusing on the high signal-to-noise (SNR) regime, which reveals that NOMA-users and BDs face outage floors in the high SNR regime as well as that IPs reach constant values at high SNR. Additionally, practical insights regarding how different system parameters affect these OP floors and IP constant values are extracted. Numerical results verify the accuracy of othe developed theoretical framework, offer performance comparisons between the presented NOMAbased AmBC system and a conventional orthogonal multiple access-based AmBC system, and reveal the impact of different system parameters on the reliability and security of NOMA-based AmBC networks.

Over the past few decades, elderly care has become an increasingly concerning problem across the world. As humans age, health can deteriorate severely, leaving the elderly population prone to suffering from various diseases. The lack of proper resources and effective services has further exacerbated this crisis. Existing solutions to this problem are not sustainable or practical. This research paper examines the growing need for effective elderly care solutions due to an aging global population. It analyzes the current market, highlighting the shortcomings of existing robotic assistive devices like Robear and HUG. The paper proposes improvements for future robotic designs, emphasizing autonomous operation, AI integration, enhanced mechanical design, and cost reduction to create more effective and widely accessible solutions. The substantial market potential for these improved robotic systems is also discussed, citing projected market growth and the increasing number of elderly individuals worldwide. Finally, the paper concludes by stressing the need for safer, more adaptable, and affordable robotic aids to enhance the quality of life for both elderly individuals and their caregivers.  

Future wireless networks are expected to utilise reconfigurable intelligent surfaces (RISs) to enhance coverage and communication quality. However, to ensure efficient RIS deployment and operation, it is crucial to understand the operational costs of RIS-assisted communication systems. This study aims to investigate the potential of RIS integration to improve the sustainability and energy efficiency of wireless communication networks, focusing on quantifying and minimising the operational cost of RIS-assisted communications. We present an in-depth analysis of power dissipation in RIS circuitry, emphasising both static and dynamic elements and offering a thorough breakdown of the operational costs associated with RISassisted communications. Our study examines control circuit components, control boards, and other factors contributing to dynamic power dissipation. We also develop a precise optimisation problem that addresses RIS power dissipation, aiming to minimise total power consumption in RIS-assisted communication systems while meeting essential communication constraints. In particular, we consider the rate requirements and the phase shift constraints, which enables our optimisation approach to be both theoretically valid and applicable to practical network scenarios. To address the optimisation problem effectively, we have developed a robust algorithm based on the particle swarm optimisation approach (PSO). This algorithm chooses the optimum number of RIS elements and adjusts the phase shifts matrix to reduce power dissipation while maintaining the channel gain required to achieve service requirements. Also, we present a comparison with active solutions in terms of power requirements, spectral efficiency, and environmental impact. Our analysis provides valuable insights into the advantages of RIS deployment, especially at higher data rates. It also highlights the importance of considering alternative relay techniques when tailoring wireless communication systems to specific rate requirements and operating conditions. INDEX TERMS Smart radio environment, reconfigurable intelligent surfaces, relay networks, passive beamforming, power dissipation, energy efficiency.

This paper presents the integration of Intel Software Guard Extensions (SGX) with the Confidential Measurement Control (CMC) framework to improve the process of remote attestation in Trusted Execution Environments (TEEs). While existing solutions like Intel's SGX provide hardware-based security, the CMC framework enhances the flexibility and reliability of report generation and validation. Our approach focuses on incorporating the CMC as a program library within SGX enclaves, addressing challenges related to software manifests, CRL updates, and measurement verification. The report generation and verification processes are detailed, alongside an analysis of performance and security. We explore various verification steps, from certificate chain validation to integrity checks on hardwarebased measurements, and highlight optimizations that reduce overhead during attestation. Finally, the paper discusses the potential integration of emerging cryptographic techniques like homomorphic encryption and multi-party computation for future scalability and confidentiality improvements in remote attestation.

In the era of rapid digital communication and exponential data growth, ensuring the confidentiality and integrity of transmitted information is paramount. This paper addresses the need for robust and efficient cryptographic solutions by optimizing authenticated encryption schemes. We present a comprehensive study on enhancing the performance of ChaCha20 and Blake3, focusing on high-performance, parallel implementations suited for large-scale data handling. Our ChaCha20 implementation leverages vectorization and parallelization to achieve up to 55x speedup, surpassing existing libraries like libsodium. For Blake3, we explore both divide-and-conquer and stack-based approaches, demonstrating a scalable improvement in performance. The divide-and-conquer method achieves a peak throughput of 39-50 GB/s, while the stack-based approach shows substantial efficiency gains. This work contributes valuable insights into the optimization of authenticated encryption, highlighting significant advancements in cryptographic performance for contemporary applications.

Fast and accurate estimation of quantiles on data streams coming from communication networks, Internet of Things (IoT), and alike, is at the heart of important data processing applications including statistical analysis, latency monitoring, query optimization for parallel database management systems, and more. Indeed, quantiles are more robust indicators for the underlying distribution, compared to moment-based indicators such as mean and variance. The streaming setting additionally constrains accurate tracking of quantiles, as stream items may arrive at a very high rate and must be processed as quickly as possible and discarded, being their storage usually unfeasible. Since an exact solution is only possible when data are fully stored, the goal in practical contexts is to provide an approximate solution with a provably guaranteed bound on the approximation error committed, while using a minimal amount of space. At the same time, with the increasing amount of personal and sensitive information exchanged, it is essential to design privacy protection techniques to ensure confidentiality and data integrity. In this paper we present the following differentially private streaming algorithms for frugal estimation of a quantile: DP-FRUGAL-1U-L, DP-FRUGAL-1U-G, DP-FRUGAL-1U-ρ and DP-FRUGAL-2U-SA. Frugality refers to the ability of the algorithms to provide a good approximation to the sought quantile using a modest amount of space, either one or two units of memory. We provide a theoretical analysis and extensive experimental results, in which we also compare DP-FRUGAL-1U-L with LDPQ, a recent state of the art algorithm, and show that DP-FRUGAL-1U-L outperforms LDPQ in both accuracy and speed.

In this work, we present a quasi-Helmholtz projector-based preconditioning and stabilization of the time domain electric field integral equation when discretized with high-order basis functions and high-order convolution quadrature schemes. The proposed approach effectively cures the ill-conditioning at large time steps that the original scheme suffers from along with the late time instabilities caused by the static solenoidal nullspace. This stabilization is achieved through a generalization of the quasi-Helmholtz projectors adapted to the high-order space basis functions and the high-order implicit schemes used by the convolution quadrature method.

This study introduces a novel AI microcontroller optimized for costeffective, battery-powered edge AI applications. Unlike traditional single bit/cell memory configurations, the proposed microcontroller integrates zero-standby power weight memory featuring standard logic compatible 4-bits/cell embedded flash technology tightly coupled to a Near-Memory Computing Unit. This architecture enables efficient and low-power AI acceleration. Advanced state mapping and an overstress-free word line (WL) driver circuit extend verify levels, ensuring robust 16 state cell margin. A ping-pong buffer reduces internal data movement while supporting simultaneous multi-bit processing. The fabricated microcontroller demonstrated high reliability, maintaining accuracy after 160 hours of unpowered baking at 125℃.

Over time, quantum technology has evolved, and innovations in this technology occur every year. Many people mostly realize we are reaching the limitations of classical computers and their CPUs & hardware components. So, according to the current leadership of different leading organizations & world leaders of every progressive nation, they are willing to move towards a new quantum enablement to unlock new computing power from emerging technologies eg. quantum computing. Fractional gates in quantum computing can be implemented using photonics by leveraging continuous transformations in optical systems.