CubeSat systems have the potential to make significant contributions to multiple areas of astrophysics research, including detection of gravitational wave counterparts, Gamma Ray Bursts (GRBs) and GRB afterglow, X-ray and ultraviolet astrophysics, exoplanet studies, solar, ionospheric and space physics, and variable star astrophysics including pulsar detection, Active Galactic Nuclei (AGN) physics, transient, time domain, and multi-messenger astrophysics. Several of these areas of astrophysical research utilize the radio frequency (RF) spectrum between 10 MHz and 10 GHz. This spectrum accounts for significant opportunities for research with space-based CubeSat systems and networks. While other frequency ranges such as millimeter-wave bands are important for research, the 10 MHz to 10 GHz frequency bands are optimum for CubeSat applications. Given this wide frequency range, the question becomes how to incorporate this capability in small form factor CubeSats. Swarms or constellations of CubeSats can be configured to perform multi-aperture Very Long Baseline Interferometry (VLBI), which is a fundamental technique for radio astronomy. These long apertures required for VLBI make it difficult, if not impossible, to synchronize the timing, position, navigation, and communications among the individual interferometer elements. Since the CubeSat interferometer will be operating in deep space at, for example, the second Lagrange Point (LP2) at a distance of 1.5 million kilometers, interface with near earth systems such as GPS will not be possible. One solution to this is to use nature's clocks, i.e., pulsars, to provide accurate timing signals which can be utilized in turn for position, navigation, and communications of the individual CubeSat elements as well as the entire interferometer array. Two types of pulsars can be utilized; one which emits an electromagnetic radio frequency or one that produces X-rays. While both techniques will be addressed, the electromagnetic radio frequency pulsar case will be discussed in detail.

This paper presents a comprehensive review of the advancements in Optical Music Recognition (OMR) driven by Deep Learning (DL) techniques. OMR aims to digitize music scores, transforming them into structured formats to enhance accessibility, facilitate preservation, and enable automated analysis. While early methods were based on heuristic approaches, the adoption of DL has revolutionized the field, achieving remarkable improvements in tasks such as layout analysis, symbol recognition, and music transcription. This survey offers a detailed examination of recent DL-based approaches, providing an in-depth analysis of datasets, evaluation metrics, methodologies, and results. Additionally, it explores emerging trends, identifies key challenges, and proposes future directions to advance OMR research. Despite the substantial progress achieved, critical challenges remain, highlighting the need for continued innovation and positioning this review as a valuable reference for both new and experienced researchers in OMR.

The advent of sixth generation of cellular network technology (6G) technology fosters Healthcare 4.0, including advanced predictive diagnostics and elevated patient convenience. With the resource-constrained Internet of Things (IoTs), Electronic Health Records (EHR) are mandatory for Healthcare 4.0 patients. Transparency in transactions encourages the utility of decentralized architecture for attaining security and privacy in Healthcare 4.0 by using the IOTA Tangle. Limited IoTs demand storage efficiency while achieving security and privacy. The Healthcare 4.0 use case diminishes storage efficiency in IoTs as the conventional Merkle Tree implementation directs super-exponential growth. We exposition the growth rate of the Merkle tree for a use case adhering to Healthcare 4.0 and identify the dynamic growth on the left side with varying vertical levels. We introduce pruning on the dynamic side to balance the growing tail of the tree, presenting the dynamic balancing hybrid Merkle tree (DBHMT). We analyze its morphological and security characteristics and lay down closed-form expressions. As there is an optimal value between security and storage efficiency, we formulate the optimization problem for reducing the storage cost that is NP-Hard and non-convex in nature, so we apply the gradient descendant optimization algorithm by incorporating Adam's update mechanism to find the optimal pruning factor. We evaluate the behaviour of Merkle Tree, DBHMT without pruning and DBHMT in IOTA Tangle for 1 million transactions and found that DBHMT reduces the storage cost by the ratio of four in comparison with the conventional Merkle tree, and the trend continues for growing transactions. Finally, we discuss future research directions by examining prevailing problems.

The diverse range of shapes enabled by modern nanofabrication techniques makes it challenging to identify the most optimal design for a desired optical response. While deep learning-based approaches are being increasingly explored for inverse design (especially, for Optical Metasurfaces), they have mostly been limited to small design subsets which constrain the shapes, thicknesses or parameters like pitch, incident angle etc. Scalability of such techniques to the full design space accesible to modern nanofabrication remains a challenge due to the difficulty of training models which retain acceptable generalization across wider design spaces. We explore the possibility of using Active Learning techniques for sample-efficient model training and surrogate-driven inverse design. Specifically, we consider the inverse design of periodic optical metasurfaces with 36 diverse shape classes and broad variations in thickness and pitch. Our results demonstrate that an active learning-driven approach to inverse design can give comparable performance to random-dataset trained models with a substantial (up to 80%) reduction in training dataset generation. The inverse design capability is seen across diverse spectral filter optimization tasks with the added benefit of providing multiple solutions in a single run. Being a modelagnostic and training-paradigm-agnostic dataset reduction technique, our work presents the possibility of extending deep learning-based inverse design to cover increasingly larger design spaces.

The short-term uncertainties associated with renewable energy resources lead to imbalances in the power systems, posing major challenges for operators. Energy storage systems, such as batteries, are increasingly considered a promising solution to mitigate these concerns. Thus, co-located wind and battery hybrid power plants (HPPs) operating with a single grid connection are poised to play a vital role in achieving balance in future energy systems. In addition, batteries can potentially improve revenue generation for HPP owners in the electricity markets. However, assessing the profitability of HPPs across different electricity markets such as spot market and balancing market to offset initial investment costs requires a thorough investigation, making optimal HPP sizing crucial. Optimal decisions are also critical for battery operation in various electricity markets, necessitating the modeling of HPP energy management system (EMS) for market participation. Hence, this work proposes a novel EMS that captures spot and balancing markets and performs efficiently within an outer non-linear sizing optimization, aiming to determine the plant’s optimal bidding strategy in different electricity markets and optimal plant design for higher returns on investment. Simulations for a wind-dominated region in Northern Europe show significant potential to enhance HPP profitability up to 4 times through strategic multi-market participation and optimal plant sizing.

Low-code and no-code platforms have revolutionized software development by leaps and bounds. These tools have enabled individuals with minimal coding experience to create and deploy applications quickly and efficiently. These platforms offer ready to use plugins to accelerate development along with options such as drag-and-drop components, pre-built templates to ease accessing various libraries. The tools help software developers to create software programs at speed unlike traditional methods of manually writing every piece of code. This paper outlines the impact of lowcode/no-code platforms, including their function in speeding innovation and reducing development costs, as well as addressing the growing demands of digital transformation. At the same time, it looks at the challenges and obstacles from scalability through security to customization that organizations need to consider when implementing these platforms. Furthermore, the potential future of low-code/no-code development in forging collaboration between technical and non-technical teams in software development. Ultimately, these platforms will bridge the gap between company needs and IT solutions much faster. However, their integration into enterprise settings must be carefully managed so as to ensure sustainability as well as efficacy.

Customer support process has seen a transformational shift in recent years. With the rapid proliferation of Artificial Integrating (AI), the human driven customer support has leapfrogged into automated customer services & support. The field of Artificial Intelligence (AI) has seen phenomenal rise in the development of agents' capability right from autonomous decision-making and problem-solving abilities to adapt to changing environments. Their proficiency in learning from experience and adaptation greatly influences their effectiveness. They both drive improvement over time and insure response to dynamic customer needs and conditions. Adaptation and learning also permit strategies to be revised in light of new data provided to them. This paper explores the bases and techniques of adaptation and learning in AI agents, focusing on reinforcement learning, supervised learning, unsupervised learning, and the combination. In addition, it also addresses challenges like overfitting, exploration-exploitation tradeoffs and computational economics. The role of these processes in various AI applications, including robotics, natural language processing and autonomous systems help industry reduce dependency on manual effort and improve efficiency to drive growth.

Recent advancements in log classification have been significantly influenced by the growing adoption of artificial intelligence (AI) and machine learning (ML) techniques. The integration of large language models (LLMs) has further enhanced the efficiency and accuracy of log classification across various domains. This paper explores the strategies and aggregates a new frontier in log classification: Adaptive Log Classification [1], which builds on these innovations by incorporating dynamic, AI/ML-driven methods to continuously optimize log classification processes. Unlike traditional static approaches, Adaptive Log Classification adjusts to the evolving requirements of systems and environments, making it particularly well-suited for diverse use cases such as system, software, and network logs. This adaptive methodology is context-aware, tailoring the classification process to the specific needs of each environment. The dynamic nature of this approach is especially beneficial in industries where data landscapes are rapidly changing, such as cybersecurity, where log data may shift in response to new threats or attack patterns. The paper explores the application of Adaptive Log Classification in the domains of Edge AI and Internet of Things (IoT) log management, highlighting the role of autonomous agents in facilitating real-time adaptation. By leveraging AI-powered agents, this approach aims to enhance the efficiency, accuracy, and responsiveness of log classification, ultimately improving system performance and security in complex, evolving environments.

This article expands upon previous peer-reviewed research, presenting novel evidence of Particle 11's stabilization at room temperature—a groundbreaking discovery in particle physics. By leveraging sophisticated computational models and comprehensive signal analysis, this article details the experimental setups, methodologies, and the particle's potential in energy harvesting, alongside broader implications for quantum computing and global energy solutions.

Tremor is the most common movement disorder and a prevalent symptom of neurodegenerative conditions such as Parkinson's disease (PD). Given the limitations of medication, which may not effectively treat tremor, and the limited availability of surgical treatments such as deep brain stimulation, there is a pressing clinical need for non-invasive therapeutic alternatives, including peripheral electrical stimulation. The high variability of PD tremor poses a challenge to such therapies and calls for patient-specific stimulation parameters. This study presents the design and validation of an adaptable algorithm for online tracking of Parkinsonian rest tremor phase, integrated into a wrist-worn system for use in triggering phase-specific peripheral electrical stimulation, delivered non-invasively to a nerve in the wrist. The system dynamically adapts to tremor variability, including shifts in the axis of maximum excursion and changes in center frequency. Validation was first performed offline, followed by online tests with a vibration plate providing stable frequencies, trials involving a healthy experimenter emulating tremor in different orientations while wearing the device, and evaluations with three individuals with PD. Results show accurate and robust phase estimation accounting for changes in tremor dominant axis and center frequency, underscoring the system's adaptability. This work provides a robust platform for research and a foundation for developing personalized, non-invasive tremor management strategies.

Automatic detection of early-stage electrical faults in motors is challenging because physical signals, such as temperature or vibrations, are often linked to regular machine operation. Inter-turn short-circuit (ITSC) faults in the stator winding are a leading cause of irreversible damage. Given industrial demands, there is an ongoing need for innovative approaches that reduce dimensionality and expedite feature extraction from three-phase current signals. This work presents an ITSC multi-fault classification algorithm by modeling the behavior of three-phase stator currents in a complex space. To achieve this, two geometric-based and two optimization-based approaches are employed to parameterize the shape of the complex signal. Rather than directly extracting features from the raw signals, these parameters capture the similarities among incipient failures. The algorithm is evaluated using machine learning classifiers trained on a dataset generated from an experimental test bench and a publicly available dataset. The proposed method achieved an accuracy of 95.30% across 13 categories, demonstrating its robustness and reliability and positioning it as a highly competitive alternative to state-of-the-art techniques.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellitebased image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem's nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

Multi-beam systems are a key technology for the highspeed links of the next-generation communication standards. Due to the stringent space constraints for allocating antennas on a platform, it is of paramount importance to assess-with respect to the physical sizethe multi-beam performance of the antenna in terms of the maximum number of simultaneous orthogonal beams. This is done by resorting to the concept of the observable field, which is here extended to arbitrary geometries, with a particular focus on planar domains. Then, this concept is used to assess the multi-beam performance of a wideband phased array prototype, developed for mobile communications. The Signal-to-Interference Ratio (SIR), computed from the measured radiation patterns of the prototype, is analyzed versus the frequency and the number of beams, and compared to the benchmark case of an ideal antenna radiating the observable field.

Sparse arrays offer a promising solution for reducing the data volume required to reconstruct ultrasound images, making them well-suited for portable and wireless devices. However, the quality of images beamformed from a limited number of transducer elements is significantly degraded. This study proposes a deep learning-based inpainting technique to estimate complete radio frequency (RF) signals (before beamforming) from downsampled RF signals obtained using a subset of transducer elements. This approach allows for quality enhancement without the need for beamforming images, offering flexibility particularly beneficial for applications such as speed-of-sound estimation algorithms. We introduce a model-based loss function that combines the signal and image domains by incorporating a measurement model associated with image reconstruction. We then compare this loss with another that accounts solely for the RF signal domain. Additionally, we train our network exclusively in the RF image domain, mapping images beamformed from downsampled RF signals to those from complete signals. We compare these approaches qualitatively and quantitatively, with all enhancing image quality. The proposed method with the modelbased loss achieves superior detail and quality metrics. Although trained on downsampled RF signals simulating sparse arrays in reception, all methods-especially our inpainting approach with the model-based loss-demonstrate strong adaptability to ultrafast acquisitions with reduced transducer elements in both transmission and reception. This highlights their potential to reduce the number of transducer elements in ultrasound probes. Furthermore, the proposed method exhibits superior generalization performance when evaluated on a different probe than the one used to acquire the training dataset.

Constrained by weak signal strength and significant inter-cell interference, users located at the cell edge in a cellular network suffer from inferior service quality. Recently, cell-free massive MIMO (CFmMIMO) has gained considerable attention due to its capability to offer uniform quality of service, alleviating the cell-edge problem. In contrast to previous studies focused on narrow-band CFmMIMO systems, this paper studies wideband CFmMIMO communications against channel frequency selectivity. By exploiting the frequency-domain flexibility offered by orthogonal frequency-division multiplexing (OFDM), and leveraging a particular spatial characteristic in the cell-free structure-namely, the near-far effect among distributed access points (APs)-we propose an opportunistic approach to boost spectral efficiency. The core concept lies in opportunistically activating nearby APs for certain users across their assigned OFDM subcarriers while deactivating distant APs to prevent power wastage and lower inter-user interference. Furthermore, this approach enables the use of downlink pilots by reducing the number of active APs per subcarrier to a small subset, thereby substantially improving downlink performance through coherent detection at the user receiver. Verified by numerical results, our proposed approach demonstrates considerable performance improvement compared to the two benchmark approaches.

The search for a deeper understanding of Dark Matter and Baryonic Matter interactions, has led to the experimental observations of Dark Matter and Baryonic Matter relationships, characteristics, and parameters using Quantum-Wave Computation. With the repeated observation of these interactions, we sought to gain a deeper understanding of the visual underpinning of Dark Matter, to attempt novel approaches at conceptualizing Dark Matter, as well as possible interactions with Baryonic Matter for the human-eye. This effort led to unique experiments covering various attempts, failures, and success, exploring cutting-edge methodologies towards uncovering imagery of dark matter. The photography of the quantum-level interaction, is a repeatable dimensionality of reality, in all its complexity and simplicity, as we expose its ways in the following experimental results.

Building effective semantic consistency feature space is important in vision-language alignment learning. To do this cross-modal task, previous works train coarse-grained internetscale data and design complicated embedding feature-cross networks. These methods often logically ignore bridging the finegrained relevance between vision and language. In this paper, we propose a structured semantic representation that contains fine-grained semantic alignment information. This information consists of four semantic elements: Time, Location, Object, and Object State. Semantic elements in each video frame can be organized into a hierarchical graph. Furthermore, this paper introduces the semantic element consistency learning network (SECNet), which facilitates fine-grained semantic alignment for vision-languagerelated tasks. SECNet consists of several semantic element consistency modules and a video-text alignment module. The former learns fine-grained semantic element consistency across video frames. The latter learns coarse-grained alignment from video-text pairs. To train our network, we build a video-text dataset VSET. VSET contains fine-grained semantic alignment information and question-answer(QA) pairs based on DiDeMo. Specifically, there is 594k corresponding fine-grained semantic alignment information, which consists of 4k semantic elements, and 148k QA pairs. Our experiments demonstrate that SEC-Net achieves better performance than the other state-of-the-art methods. Code and the sample data of VSET can be seen in the supplementary file.

Especially in noisy intermediate-scale quantum (NISQ) devices, fault-tolerant quantum computing cannot be realized without quantum error correction. High error rates in quantum systems stemming from decoherence and gate defects call for effective and flexible QEC systems. This paper presents a novel FPGA-based architecture using dynamic reconfiguration to maximize hardware resources in response to real-time error patterns. Supporting bit-flip, phase-flip, and depolarizing faults among several error correction techniques guarantees high accuracy and little hardware overhead. Scaled for surface code distances up to and beyond, the design combines modular pipelines, adaptive hardware allocation, and error pattern detection. Designed on a Xilinx XC7A50T-CPG236 FPGA, the system showed notable gains over static architecture, averaging 30% lower latency and 20% less resource use overall. Low latency and high throughput are maintained while this dynamic technique enables flexible adaptability to several quantum error models. The outcomes show how well this structure may improve quantum error-correcting systems and enable the incorporation of QEC into useful quantum computer configurations.