The Geostationary Synthetic Aperture Radar (GEO SAR) concept aims at defining an Earth Observation system able to provide regional coverage with large swaths and subcontinental access with a very short revisit time by exploiting the unique characteristics of the GEO orbit. These peculiar characteristics make GEO SAR suitable for imaging and interferometry of fastevolving large-scale phenomena, such as ground motion or water vapor. The design of the orbit for a GEO SAR is discussed to provide, at one time, wide imaging capability, compensation for the huge power loss due to the distance, and short interferometric revisit. In particular, interferometry imposes constraints on the repeatability of the orbit that are far more restrictive than the control required for telecommunication SATelliteS (COMSATS). Here, two different specific control approaches are proposed to ensure small normal baselines and maximum angular band overlap between subsequent acquisitions., one based on the conventional COMSATS control, and a second one that aims to reduce the number of maneuvers and, ultimately, the amount ∆V .

Data anonymization is an essential prerequisite that enables data sharing in a privacy-preserving manner. However, anonymization affects the quality of the data and thus might affect the performance of later conducted data analysis. In this work, we investigate the influence of different image-based subject anonymization methods on the performance of the three common computer vision (CV) tasks keypoint detection, instance segmentation, and face detection. We compare the anonymization methods on how much they affect the performance of the CV tasks, as well as their degree of anonymization. Furthermore, a re-identification attack scenario is applied. The study findings indicate a clear trade-off between increased privacy results in decreased CV performance. Furthermore, DeepPrivacy2 (DP2) shows best results on keeping the performance of face detection high, while leading to a low recognition rate. However, the retrieval rate of our attack scenario which measures the reidentification rate remains high for all anonymization methods, which contrasts with respective low level of recognition.

Blockchain, a groundbreaking technology known for its distributed ledger system, records digital transactions and information in a decentralized manner. Broadcast protocols, fundamental components of computer networks, play a crucial role in disseminating information effectively. Blockchain networks often exhibit lower throughput rates compared to centralized payment systems due to the intricate node verification process during data propagation. Improving blockchain performance requires mitigating transaction and block propagation delays within network dissemination processes. This study delves into the development and significance of blockchain broadcast protocols, exploring their applications within blockchain networks. We introduce various broadcast protocols designed to optimize performance metrics such as energy efficiency, fault tolerance, and consistency. A comprehensive analysis, including detailed comparisons through tables, sheds light on the strengths and weaknesses of these protocols. Lastly, we address potential future challenges and directions for the evolution of broadcast protocols in the blockchain ecosystem.

The topic of encryption backdoors has become a polarizing issue in the fields of cybersecurity and cryptography. On the one hand, governments and law enforcement agencies argue that backdoors are necessary to monitor and prevent criminal activities, especially in cases involving encrypted communications (Schneier, 2015). On the other hand, privacy experts and technology companies argue that encryption backdoors, by their very nature, weaken security and introduce vulnerabilities that could be exploited by malicious actors (AyrA_ch, 2022). Bitcoin, the world’s leading decentralized blockchain, represents an interesting case in this debate. Unlike other blockchains that may be influenced by centralized entities, Bitcoin relies heavily on its cryptographic foundations to ensure security and trust. However, questions arise: could Bitcoin contain an encryption backdoor? Is it possible that from the very beginning, a vulnerability was introduced into its cryptographic algorithms, either deliberately or by accident? This paper explores these questions by drawing on existing research and examining the case for and against backdoors in Bitcoin’s cryptographic structure.

An Alternative Energy Harvesting (AEH) system has been developed to explore the potential of thin-film PZT cells in multi-layered configurations for roadway power generation. This system incorporates a Smart Charging System (SCS) to manage energy storage in lithium batteries efficiently. Each layer of the composite thin-film PZT cells was evaluated for its unique characteristics and energy generation potential. The 2-layer AEH (PZT2L) produced up to 53.28 Wh per day under continuous operation, while the 3-layer AEH (PZT3L) achieved 106.8 Wh per day. In comparison, silicon-based PV panels generated between 19.16 and 38.32 Wh per square foot daily, but with limited operational hours. Despite challenges due to the lower and variable energy output of the PZT cells, the SCS optimized the process for reliable storage. Various analytical techniques are used to assess key parameters such as energy generation capacity, pressure, speed, and overall system performance. These evaluations aim to enhance the efficiency and reliability of multilayered PZT energy harvesting and compare its potential with that of solar cells. The results show promise for the PZT technology as a viable alternative. The ultimate goal is to develop a sustainable energy-generating pad for road infrastructure, potentially transforming future approaches to alternative energy generation.

Content toxicity has become a pressing issue in the deployment of artificial intelligence systems that interact with users through text, necessitating robust strategies to mitigate the generation of harmful language. Employing game-theoretic frameworks to address this challenge presents a novel and significant approach, as it allows for the strategic modeling of interactions between AI systems and adversarial entities, leading to the development of adaptive and resilient response mechanisms. The study demonstrates that conceptualizing the generation of toxic content as a strategic game, involving an AI language model and an adversarial prompt generator, can effectively reduce the occurrence of harmful outputs. Through the implementation of Nash equilibrium and other equilibrium concepts, the research illustrates how AI can be guided towards stable, non-toxic behavior, even when confronted with sophisticated adversarial tactics. Experiments conducted under various scenarios revealed that AI systems equipped with gametheoretic strategies are capable of dynamically adjusting to new threats, maintaining ethical standards in real-time interactions. The integration of continuous feedback mechanisms further enhances the system's ability to learn from past interactions, refining its strategies to minimize the likelihood of generating offensive or inappropriate language. This innovative approach not only addresses the immediate concerns of content toxicity but also offers a framework that can be extended to other domains requiring ethical and safe AI interactions.

The ongoing energy transition introduces challenges in designing energy systems, particularly at the low-voltage level, where additional loads like electric vehicle charging points and heat pumps are predominantly connected. Obtaining accurate grid data for modeling and analysis is hindered by availability and confidentiality concerns, especially for low-voltage distribution grids. Unlike high voltage grids, low-voltage grids lack standardized handling due to their large size and heterogeneity.This research addresses an essential gap in the availability of grid data, offering a methodological base to generate synthetic low-voltage grids based on geospatial data. This includes the consideration of multiple feeders per transformer, greenfield and brownfield transformer positioning, and variable dimensioning of equipment components. Based on data clustering methods and an algorithmic graph-based approach, the tool provides a scalable solution that is applicable to large areas such as cities, states, or even countries with hundreds of thousands of grids.The derived representative synthetic grid topologies support the development of more resilient and efficient future energy systems by enabling research on specific issues like grid planning and reinforcement while considering regional constraints, such as grid congestion or voltage violations. This supports a comprehensive understanding of low-voltage energy systems in the context of the energy transition.

This paper presents an innovative, parallel implementation of the Small BAseline Subset (SBAS) approach to automatically and efficiently process large volumes of multitemporal Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) interferograms generated at the native full spatial resolution of the SAR images. The starting point of the developed Full-Resolution Parallel-SBAS (FR P-SBAS) technique involves some algorithmic extensions for improving the quality of the DInSAR time series to effectively analyze extended deformations and localized displacement phenomena affecting single buildings and infrastructures. A key point of the work is the efficient and scalable FR P-SBAS processing chain implementation, focused on exploiting GPU architectures. The presented scalability analysis demonstrates the GPU capability to efficiently generate full-resolution displacement time series starting from large DInSAR datasets. Moreover, the processing solution easily allows us to deal with SAR data acquired through the Stripmap and TOPS modes. To assess the quality of the generated DInSAR products, an extensive experimental analysis is also shown, based on long sequences of X-Band COSMO-SkyMed Stripmap and C-Band Sentinel-1 TOPS acquisitions relevant to the Campi Flegrei Caldera (Southern Italy), which is monitored through a dense GNSS network. The presented results demonstrate the effectiveness of the FR P-SBAS processing chain in retrieving multi-frequency and multi-platform displacement time series at the full spatial resolution with sub-centimetric accuracy and in very short time frames, from a few hours for the COSMO-SkyMed datasets up to some tens of hours for the Sentinel-1 one.

A family-based behavioral model represents the behavior of a software product line (SPL) as a whole. A well-known type of these models is the Featured Finite State Machine (FFSM), which is an FSM whose states and transitions are annotated with feature expressions. FFSM Diff is a model merging algorithm that integrates the FSMs of SPL products and constructs an FFSM. In many cases, due to the large number of SPL products, it is practically impossible to analyze all products. Therefore, the FFSM is constructed using a subset of products, called a sample. The FFSM constructed by model merging of products in a sample only includes the behavior of the sample products. The goal is to construct an FFSM which represents the approximate behavior of the out-of-sample products as well. In this paper, a method is presented to generalize the FFSMs constructed using FFSM Diff. When generalizing a transition, the literals of features whose presence does not affect that transition are removed from feature expressions. We also incorporate the effect of feature interactions in the provided method. When generalizing the transitions, it is possible to introduce nondeterminism in the models derived from the generalized FFSM. To reduce this nondeterminism, we introduce a BFS-based similarity metric which is used during model merging. We also equip the basic generalization method with a lookahead mechanism to reduce the nondeterminism in the generalized model. We use the presented generalization methods on three different subject system, and evaluate the accuracy in terms of Precision, Recall, and F1. The results show that in all three subject systems, with sufficient sample size, the provided generalization methods can result in F1 values equal to or higher than 95 percent for out-of-sample products.

This review covers the multidisciplinary conundrum shaping hydropower development within the broader energy transition. Classical hydropower generation facilities are transformed toward more flexible operations to adapt and facilitate the integration of intermittent renewable energy sources. As a result, their operating modes will be far from their designed rated point. This review explores this paradigm shift in three perspectives: machine design considerations, power plant operation, and transmission system operator (TSO) mandates. It also present the implementation of innovative analysis techniques to give a deeper understanding of the energy transition's associated challenges and its possible solutions. In particular, machine design challenges include magnetic saturation, efficiency estimation and mechanical wear and tear. Moreover, power plant operators needs to consider the effects of flexible power production caused by variable renewable, while in contrast grid regulators needs to strengthen reactive power mandates and system-bearing services to ensure stability.

Over the last decade, large hydropower plants have shifted their operational regimes from nominal conditions to more diversified load cycles owing to the increasing share of intermittent renewables that have necessitated more flexible hydropower operations. However, increased load variability has made the hydrogenerator efficiency a more important economic metric. This paper proposes a method for redesigning the hydrogenerator rotor pole and refurbishing the field winding to enhance economic performance. The effectiveness of these changes is evaluated by estimating the levelized cost of non-optimal operation across various operating regimes. We present two alternative rotor redesigns for a 103-MVA hydrogenerator that indicates significant economic benefits for a diverse set of load cycles. Generally, the net present value of these benefits is found to be four to six times greater than the material costs incurred during the refurbishment phase.

Electrical impedance tomography (EIT) is a low cost, radiation-free and non-invasive imaging technique. It provides high temporal resolution but relatively low spatial resolution. This paper proposes a novel method to enhance the spatial resolution of EIT images using Cycle generative adversarial networks (cycleGAN). The method consists of multiple steps including traditional image reconstruction, custom postprocessing of time series EIT images to reduce noise, CT image preprocessing to reduce unnecessary feature entanglement, EIT-to-CT transformation, and finally an anatomically constrained EIT image reconstruction. The EIT-to-CT transformation consists of a cycleGAN model trained on unpaired EIT and CT lung images in which we incorporate the Mutual Information (MI) constraint. This EIT-to-CT step provides a structurally aligned high resolution CT image inferred from the lower resolution EIT image. The subsequent EIT reconstruction uses this high resolution image as a constraint to provide physiologically relevant EIT images with enhanced resolution. The proposed method is validated with 235 EIT images acquired from over 10 healthy subjects. It is compared to the traditional image reconstruction method using the Intersection over Union (IOU) and Normalized Mutual Information (NMI) metrics, giving 0.7 versus 0.4 and 0.49 versus 0.45,p < 0.001 respectively, suggesting that the proposed method is better. While paired testing of EIT and CT images will further corroborate the results, the initial results hold promise. Finally, this pipeline of feedback oriented reconstruction based on an alignment goal inferred through cycleGAN, can potentially transfer to and aid in different medical imaging reconstruction modalities.

In this research work, a new approach to compensate for both the response In this paper, we highlight the crucial role of substation digitalization in identifying the origins of harmonic disturbances within iron and steel plants connected to the point of common coupling (PCC). Our method capitalizes on the benefits of a digital substation infrastructure by utilizing merging units that operate at a 28.8 kHz sampling rate, in line with the IEC 61850-9-2 standard. This setup allows for the real-time measurement of voltage and current from all feeders, streaming these high-resolution data directly to substation servers. This digital approach not only facilitates precise and timely data acquisition but also enhances the overall reliability and efficiency of the monitoring process. By digitalizing the substation, we enable a more dynamic response to power quality issues and improve the capacity for preventive maintenance. Integrating our method into existing digital substations supports a robust framework for managing and mitigating the effects of current harmonics and interharmonics, showcasing the practical benefits of advanced digital infrastructure in critical utility operations. We propose attention based long short-term memory as a novel method for determining the harmonic responsibility of each plant without the need for instantaneous impedance measurements. This method relies on the correlation of time-synchronized voltage and current measurements taken from all feeders at the PCC. The approach is first validated using synthetic data generated in a PSCAD/EMTDC simulation environment, as well as real-world field data. Further verification is conducted through measurements of voltage and current waveforms using mobile Power Quality (PQ) measurement systems at one of the PCCs in the electricity transmission system. Our results demonstrate that the harmonic responsibility of each IS plant can be accurately determined for each harmonic order. Unlike existing methods, our proposed approach eliminates the need for online system impedance measurements or in-plant measurements.

Leveraging large language models (LLMs) to performs direct inference or build training datasets has becoming an important method for natural language processing (NLP) applications. However the quality of the training datasets or inference results of LLMs exist some noise data. In this paper, we propose our self-eval-drop method to clean the datasets by LLMs API. We first sort the to-clean dataset by LLMs API and then sample a seed dataset from the to-clean dataset. Then we manually evaluate the seed dataset and find badcases in the seed dataset. Then we loop the badcases to find the (most) similar data in the to-clean dataset for each badcase. Then we remove the found similar data of badcases from the to-clean dataset. The manual evaluation results show that our simple self-eval-drop method can improve the accuracy of the to-clean dataset to more than 97% by only sampling and evaluating about 1% of the to-clean dataset as the seed dataset.

Phase-locked loop (PLL) impacts the small-signal and transient stability of grid following (GFL) inverters in power networks. Extensive analysis of their impact on system stability subject to faults has been presented in literature while the impacts of malicious attacks on PLL have not been well understood. This letter reveals the interaction between grid forming and GFL inverters in a microgrid when PLL has been maliciously attacked. The analysis reveals the risk of system blackout due to voltage collapse triggered by inverter current limitation. Reduced mathematical model has been derived and extensive analysis undertaken to demonstrate the phenomenon.

This study addresses the substantial gap in the academic literature on blockchain technology literacy by proposing a Blockchain Technology Literacy Test (BTLT). Through a comprehensive literature review of eight databases, only nine publications with limited focus on blockchain technology literacy were identified. The inconsistency in definitions and predominant emphasis on cryptocurrency literacy further complicate the development of standardized assessments. Based on the results of the literature review and the development of the BTLT, with a focus on terms and technological aspects, an updated list of cryptocurrency literacy questions was proposed as the Cryptocurrency Literacy Test (CLT) to avoid duplications and redundancy in the assessment of relevant blockchain technology and cryptocurrency-related knowledge. The BTLT and CLT aim to distinguish between blockchain technology and cryptocurrency literacy, thereby ensuring comprehensive and accurate assessments. The findings emphasize the need for clear definitions and frameworks within the blockchain ecosystem and call for expanded research to include emerging applications, such as DeFi, DeSci, DAOs, and Web3. This study provides a foundation for future educational efforts and literacy assessments in blockchain technology and cryptocurrencies.

The efficient operation of cellular networks requires precise tuning of configuration parameters such as the antenna downtilt or the transmit power. Here, data-driven methods, which can integrate feedback from monitoring data, are promising but face challenges related to sample efficiency, scalability, and safety --- limiting their real-world application. In this work, we introduce an innovative online coverage and capacity optimization framework that combines model-free exploration using Monte Carlo tree search with a safe, model-based baseline derived from a probabilistic differentiable network twin. We formulate the optimization task as a sequential tree search problem, developing specialized policies to guide the exploration of the configuration space. The differentiable network twin aids both the selection policy, by pruning the search space with a prior action distribution, and the rollout policy, enabling domain knowledge-based selection of remaining actions. Our results demonstrate that the proposed approach effectively guides the optimization process, outperforming both purely model-free and model-based methods. Our solution improves safety by reducing the risk of testing poorly performing configurations, enhances the model-based solution, and can also compensate for severe model mismatches in the digital twin. Hence, the framework addresses a common obstacle in applying data-driven optimization to real-world network deployments.

This study examines the fundamental theory necessary for comprehending Variational Autoencoders (VAEs) and generative latent time-series models. We will also explain how these models extend the principles of VAEs to the domain of time-series data by incorporating temporal dependencies into the latent space. By leveraging the probabilistic nature of VAEs and the temporal dependencies captured by generative latent time-series models—researchers and practitioners can generate synthetic data for various applications, ranging from image generation to time-series forecasting. Through experiments and examples, we showcase the efficacy of these models in generating synthetic data that closely resembles the characteristics of the original dataset.