Storage Size Research Articles

A Hybrid Neural Ordinary Differential Equation Based Digital Twin Modeling and Online Diagnosis for an Industrial Cooling Fan

Digital twins can reflect the dynamical behavior of the identified system, enabling self-diagnosis and prediction in the digital world to optimize the intelligent manufacturing process. One of the key benefits of digital twins is the ability to provide real-time data analysis during operation, which can monitor the condition of the system and prognose the failure. This allows manufacturers to resolve the problem before it happens. However, most digital twins are constructed using discrete-time models, which are not able to describe the dynamics of the system across different sampling frequencies. In addition, the high computational complexity due to significant memory storage and large model sizes makes digital twins challenging for online diagnosis. To overcome these issues, this paper proposes a novel structure for creating the digital twins of cooling fan systems by combining with neural ordinary differential equations and physical dynamical differential equations. Evaluated using the simulation data, the proposed structure not only shows accurate modeling results compared to other digital twins methods but also requires fewer parameters and smaller model sizes. The proposed approach has also been demonstrated using experimental data and is robust in terms of measurement noise, and it has proven to be an effective solution for online diagnosis in the intelligent manufacturing process.

PENGARUH DURASI IRIGASI TETES TERHADAP REDUKSI LENGAS TANAH PADA LAHAN POLYBAG

Soil moisture is an important factor needed by plants for growth, and therefore the amount of soil moisture that can be stored depends on the type of soil or the composition of the soil-forming materials. Compost is one of the nutrients that is often used in the manufacture of planting media on land in the form of polybags, and the amount of compost composition on the soil is thought to greatly affect the ability to store water in the media, both the size and duration of storage. This study aims to examine the effect of using compost on storage of irrigation water in the form of soil moisture and how long it can be stored. in three-storey drip irrigation. The main data analyzed is soil moisture data (w) and the results of the analysis and discussion and conclusions are drawn descriptively. The results of the analysis show that the initial soil moisture content in the range of 19% -25%, can be given additional soil moisture by irrigation of about 12%. The average daily decrease in soil moisture that occurs in soil variation 1 (70%: 30% ) is 3% -4.4% and in soil variation 2 (50%: 50%), the average moisture reduction is around 3% -6%, soil moisture reduction in variation 2 is greater than soil variation 1 for all floors.

An analytical method for sizing energy storage in microgrid systems to maximize renewable consumption and minimize unused storage capacity

This paper presents a novel analytical method to optimally size energy storage in microgrid systems. The method has fast calculation speeds, calculates the exact optimal, and handles non-linear models. The method first constructs a temporal storage profile of stored energy, based on how storage charges and discharges in response to renewable generation and load demand. The storage is sized according to the largest cumulative charge or discharge in the profile. In essence, the storage profile represents how storage is utilized within a given system, and the method sizes optimal storage to maximize that profile, such that storage utilization is maximized, and unutilized or wasted storage is eliminated. Maximizing storage utilization also maximizes renewable consumption and minimizes load shedding, as storage utilization is the temporal transfer of energy from renewable generation to load demand. The proposed method is extended iteratively to account for storage’s energy limits, power limits, and energy leakage. Two solar–battery case studies demonstrate the method. The first study shows that optimally sized storage does not have wasted capacity due to over-sizing, nor cause energy deficits due to under-sizing. The second case study shows increasing the storage size reduces the marginal increase in energy provided by storage, indicating diminishing returns. The diminishing return thresholds are defined by the largest daily and annual storage designs. The result shows the largest daily design only requires 3% of the annual design’s storage size, but provides 80% of the energy provided by the annual design. The proposed method can be used as a decision support tool for energy analysts, to determine required storage capacity when coupled with known renewable generation and load demand.

Model Compression for Communication Efficient Federated Learning.

Despite the many advantages of using deep neural networks over shallow networks in various machine learning tasks, their effectiveness is compromised in a federated learning setting due to large storage sizes and high computational resource requirements for training. A large model size can potentially require infeasible amounts of data to be transmitted between the server and clients for training. To address these issues, we investigate the traditional and novel compression techniques to construct sparse models from dense networks whose storage and bandwidth requirements are significantly lower. We do this by separately considering compression techniques for the server model to address downstream communication and the client models to address upstream communication. Both of these play a crucial role in developing and maintaining sparsity across communication cycles. We empirically demonstrate the efficacy of the proposed schemes by testing their performance on standard datasets and verify that they outperform various state-of-the-art baseline schemes in terms of accuracy and communication volume.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.

Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review

As the world’s population continues to grow and the demand for energy increases, there is an urgent need for sustainable and efficient energy systems. Renewable energy sources, such as wind and solar power, have the potential to play a significant role in meeting this demand, but their intermittency can make integration into existing energy systems a challenge. Moreover, the development of sustainable energy systems has become even more critical in recent years, due to a confluence of events, including the decline in fuel prices, geopolitical conflicts, and the recent COVID-19 pandemic. The decrease in fuel prices has led to a decline in investment in renewable energy and has slowed the transition to sustainable energy systems. Additionally, geopolitical conflicts and pandemics have highlighted the need for resilient and self-sufficient energy systems that can operate independently of external factors. Also, energy storage technologies play a critical role in achieving this goal by providing reliable backup power and enabling microgrids to operate independently of the larger power grid. As such, developing efficient and effective energy storage technologies is essential for creating sustainable energy systems that can meet the demands of modern society while mitigating the impact of external factors. In this regard, this work provides an overview of microgrids’ latest energy storage technologies, including their applications, types, integration strategies, optimization algorithms, software, and uncertainty analysis. Energy storage technologies have a wide range of applications in microgrids, including providing backup power and balancing the supply and demand of energy. Different energy storage techniques have been discussed, including batteries, flywheels, supercapacitors, pumped hydro energy storage, and others. Moreover, integration strategies of energy storage in microgrids, models, assessment indices, and optimization algorithms used in the design of energy storage systems are presented in detail. The capabilities of software used in energy storage sizing are explored. Further, uncertainty analysis in modeling energy storage devices is presented and discussed. This state-of-the-art technology has been prepared to demonstrate the effectiveness of energy storage technologies in microgrids, providing valuable insights for future developments in the field.

An iterative thresholding-based algorithm for brain–computer interface (BCI) application

The EEG signals are recorded using surgical (invasive) or non-surgical (non-invasive) BCI neuroimaging modalities. Basically, we are dealing with a huge amount of data and long signal recordings.The classic solution to deal with the huge amount of data has been data compression, where the data is first collected in its original large format and then converted to a compressed format of smaller size for storage or transmission. This has become a traditional solution to the problem of handling the massive volume of data.Compressive sensing is a novel technique that senses the signal in a compressed format using a sub-Nyquist–Shannon sampling rate and reconstructs it when the data from the compressed sensed signal is required.In the context of applying CS, the first issue is how to construct the linear measurement matrix to ensure that compressive sensing meets the requirements of the application. The second issue depends on the way in which the sparse signal can be obtained from a small sample of observations. In this paper, nuclear norm minimization is proposed to enforce sparsity and low-rank structures in the reconstructed multi-channel EEG signals. To compute the optimization, we propose two new algorithms which we call: a weighted thresholding algorithm and a weighted thresholding orthogonal matching pursuit algorithm based on compressive sensing. We perform the experiments on the insomnia EEG dataset. Computer simulations provide and show the good behavior of these algorithms and the NNM norm, compared to others in the literature.

An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.

To evaluate the performance of two lightweight neural network models in the diagnosis of common fundus diseases and make comparison to another two classical models. A total of 16,000 color fundus photography were collected, including 2000 each of glaucoma, diabetic retinopathy (DR), high myopia, central retinal vein occlusion (CRVO), age-related macular degeneration (AMD), optic neuropathy, and central serous chorioretinopathy (CSC), in addition to 2000 normal fundus. Fundus photography was obtained from patients or physical examiners who visited the Ophthalmology Department of Beijing Tongren Hospital, Capital Medical University. Each fundus photography has been diagnosed and labeled by two professional ophthalmologists. Two classical classification models (ResNet152 and DenseNet121), and two lightweight classification models (MobileNetV3 and ShufflenetV2), were trained. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the performance of the four models. Compared with the classical classification model, the total size and number of parameters of the two lightweight classification models were significantly reduced, and the classification speed was sharply improved. Compared with the DenseNet121 model, the ShufflenetV2 model took 50.7% less time to make a diagnosis on a fundus photography. The classical models performed better than lightweight classification models, and Densenet121 showed highest AUC in five out of the seven common fundus diseases. However, the performance of lightweight classification models is satisfying. The AUCs using MobileNetV3 model to diagnose AMD, diabetic retinopathy, glaucoma, CRVO, high myopia, optic atrophy, and CSC were 0.805, 0.892, 0.866, 0.812, 0.887, 0.868, and 0.803, respectively. For ShufflenetV2model, the AUCs for the above seven diseases were 0.856, 0.893, 0.855, 0.884, 0.891, 0.867, and 0.844, respectively. The training of light-weight neural network models based on color fundus photography for the diagnosis of common fundus diseases is not only fast but also has a significant reduction in storage size and parameter number compared with the classical classification model, and can achieve satisfactory accuracy.

Analysis of the discharging process of latent heat thermal energy storage units by means of normalized power parameters

Many efforts are being made to mitigate the main disadvantage of most phase change materials – their low thermal conductivities – in order to deliver latent heat energy storage systems (LHESS) with adequate performance. However, the effect of applied methods is difficult to compare as they are mostly tested for different storage types and sizes and/or different boundary and initial conditions, which hinders rapid progress in the optimization of these approaches. In this work, a previously developed method for comparing the performance of LHESS is applied to experimental results of different storage systems under different conditions and subsequently analyzed and further refined. The main idea of the method is to normalize the power with the volume and a reference temperature difference and compare its mean value plotted over the normalized mean capacity flow of the heat transfer fluid (HTF). This enables the presentation of the results in a compact and easily comparative way. Attention has to be paid when it comes to the choice of the reference temperature difference, the reference volume and the method for calculating the mean value. Two variants of calculating the mean value (time-weighted and energy-weighted) and two variants of reference temperatures for determining the temperature difference to the inlet temperature of the HTF (initial temperature and melting temperature) are applied and discussed in detail. While the method significantly increases the comparability of results, none of the options listed above are without drawbacks. Approaches are shown to reduce or eliminate these drawbacks in the future. The recommendation for comparing different LHESS under different conditions is to use the method described here and clearly state the chosen reference temperature, reference volume and method for calculating the mean value.

Power availability of PV plus thermal batteries in real-world electric power grids

As variable renewable energy sources comprise a growing share of total electricity generation, energy storage technologies are becoming increasingly critical for balancing energy generation and demand.In this study, a real-world electricity system was modeled rather than modeling hypothetical future electric power systems where the existing electricity infrastructure are neglected. In addition, instead of modeling the general requirements of storage in terms of cost and performance, an existing thermal energy storage concept with estimated capital cost that are sufficiently low to enable large-scale deployment in the electric power system were modeled. The storage unit is coupled with a photovoltaic (PV) system and were modeled with different storage capacities, whereas each storage unit had various discharge capacities.The modeling was performed under a baseline case with no emission constraints and under hypothetical scenarios in which CO2 emissions were reduced. The results show that power availability increases with increasing storage size and vastly increases in the hypothetical CO2 reduction scenarios, as the storage unit is utilized differently. When CO2 emissions are reduced, the power system must be less dependent on fossil fuel technologies that currently serve the grid, and thus rely more on the power that is served from the PV + storage unit.The proposed approach can provide increased knowledge to power system planners regarding how adding PV + storage systems to existing grids can contribute to the efficient stepwise decarbonization of electric power systems.

The role of state-of-charge management in optimal techno-economic battery energy storage sizing for off-grid residential photovoltaic systems

With increased photovoltaic (PV) penetration in residential areas, an off-grid PV system is a sustainable solution to meet the zero net emissions goal by 2050. However, an off-grid PV system has a significant technical issue: low power supply reliability. Typically, battery energy storage (BES) is necessary to overcome the technical issues associated with PV systems. Conventionally, BES is operated to maintain a power balance between generation and load. However, techno-economic feasibility is a crucial aspect of BES adoption. Although BES can effectively support off-grid PV systems, it may not be available when its state-of-charge (SoC) reaches the minimum allowable limit, creating power supply failures (outages). Therefore, this study proposes, for the first time, a BES power control strategy that collaborates with SoC management to improve the reliability of an off-grid residential PV-BES system. A simulation study is performed using a residential load profile from the urban area of Bangkok, Thailand, obtained from the Metropolitan Electricity Authority (MEA). A comparative study is conducted to compare the proposed strategy’s performance with that of the conventional control strategy. Following the comparative study, the role of SoC management in BES sizing is quantitatively illustrated. Moreover, the impacts of SoC management parameter setting (i.e., SoC restoration threshold and maximum SoC restoration power) are evaluated using techno-economic performance indicators. The simulation results reveal that BES power management with SoC management could reduce BES capacity and levelized cost of energy (LCOE) by 28% and 26.28% compared with the conventional strategy.

Optimal sizing of battery energy storage in solar microgrid considering peak load shaving

The power system faces a lot of challenges due to incorporation of a large number of renewable energy sources. Here, Battery Energy Storage System (BESS) can be used effectively to deal with the grid’s rising peak demands without the necessity of upgrading the grid capacity. An energy management strategy is considered based on peak load shaving combined with transformer power limit and BESS State of Charge (SOC). Cooperation Search Algorithm (CSA) is utilised to determine the number of batteries in BESS and Photovoltaic (PV) panels with the goal of maximising economic benefits. The optimisation model of solar microgrid energy storage is established and solved for three scenarios of the grid only (Base Case), BESS without PV (Case-A) and BESS with PV (Case-B). From the result, it is verified that in Case-B, the customer will pay 31% and 34% less compared to the Base Case and Case-A, respectively, towards energy costs in a day.

A Stackelberg Game-based robust optimization for user-side energy storage configuration and power pricing

With the rapid development of demand-side management, battery energy storage is considered to be an important way to promote the flexibility of the user-side system. In this paper, a Stackelberg game (SG) based robust optimization for user-side energy storage configuration and basic electricity price decisions is proposed. Firstly, this paper put forward a two-stage energy management framework considering the interactive relationship between the supplier-side system and the user-side system. Secondly, based on the two-part electricity price mechanism, a bi-level optimal sizing of user-side energy storage is established in which robust dispatching is considered to deal with the uncertainty of renewable energy. Thus, a three-layer optimization model of “pricing on the power supply side–basic scenario configuration on the user side–worst-case scenario scheduling on the user side” is formulated. Through relaxing the state variables of energy storage in the configuration and scheduling models and combining Karush-Kuhn-Tucher conditions, the user-side model is transformed into a single-layer problem. A distributed algorithm based on the method of bisection is used to solve the two-stage SG problem. The simulation results demonstrate the basic electricity price and energy storage configuration suggestions and prove the superiority of the proposed method.

The hydrogen storage challenge: Does storage method and size affect the cost and operational flexibility of hydrogen supply chains?

Hydrogen is seen as a key energy vector in future energy systems due to its ability to be stored in large volumes for long periods, providing energy flexibility and security. Despite the importance of storage in hydrogen's potential role in a zero-carbon energy system, many techno-economic analyses fail to adequately model different storage methods in hydrogen supply chains, often ignoring storage requirements altogether. Therefore, this paper uses a data-driven techno-economic analysis (TEA) tool to examine the effect of storage size and cost on three different 2030 hydrogen supply chain scenarios: wind-based, solar-based, and mixed-source grid electrolysis. For varying storage sizes and specific capital costs, the overall levelised cost of hydrogen (LCOH), including production, storage, and delivery to a constant demand, varies significantly. The LCOH ranges from €3.90–12.40/kgH2, €5.50–12.75/kgH2, and €2.80–15.65/kgH2 for the wind-based, solar-based, and mixed-source grid scenarios respectively, with lower values for scenarios with low-cost storage. This highlights the critical role of low-cost hydrogen storage in realising the energy flexibility and security electrolytic hydrogen can provide.

Sizing, economic, and reliability analysis of photovoltaics and energy storage for an off‐grid power system in Jordan

AbstractRemote areas in Jordan often rely on expensive and polluting diesel generators to meet their electricity demand. This study investigates 100% renewable solutions to supply the electricity demand of off‐grid energy systems through optimal sizing of photovoltaics and energy storage systems. A linear programming approach is proposed to minimise the annualised cost of electricity supply including capital costs of equipment and their operation and maintenance costs. The optimisation determines the size of photovoltaics and energy storage required to satisfy electricity demand at every hour of a selected year. A Jordan campsite was used as a case study to assess and compare the performance of PV‐battery storage and PV‐hydrogen storage systems from economic and reliability perspectives. The results show that hydrogen storage was more economical for a 100% renewable energy system. However, introducing some diesel generation gave the battery system a significantly lower annualised cost of energy.

Design of Energy Recovery Control for General Virtual Synchronous Machines Based on Various Forms of Energy Storage

The reduced inertia in the power system due to renewable energy integration introduces operation challenges in frequency stability and control. The current options for virtual inertia and frequency support are limited by the energy resources and the power electronic interface. Considering the demand on response speed and energy capacity, a general virtual synchronous machine (VSM) control based on various forms of energy storage systems (ESS) is proposed. The steady-state energy variation of energy storage is found to be proportional to the virtual damping or governor gain, while inversely proportional to the integral gain of system frequency control. It is found that the size of energy storage can be at the second time scale (for example, 6.8 p.u.·s) for VSM implementation, which is significantly smaller than the conventional hour-scale energy storage in the power system. Based on energy dynamic analysis, stability requirement, and bandwidth separation rules, an energy recovery control is designed to maintain constant state of charge (for example, 50%) while avoiding conflicts with frequency regulation. The time scale of the designed energy recovery control loop (for example, hundreds of seconds) is longer than the secondary frequency control. The effectiveness of the proposed control is verified through comprehensive case studies.

A heterogeneous online/offline signcryption scheme for Internet of Vehicles

With the rapid development of wireless communication and autonomous driving technologies, the Internet of Vehicles (IoV) technology has emerged to build a large network that supports interactive communication between vehicles, roadside infrastructure and the surrounding environment. This allows vehicle sensors to collect massive amount of data. However, data is at risk of attack. In order to ensure data security, in this paper, we design a heterogeneous online/offline signcryption (HGOOSC) scheme. This construction allows a sender in Certificateless Cryptography (CLC) to send messages to a receiver in Public Key Infrastructure (PKI). CLC has neither key escrow problem in Identity-Based Cryptography (IBC) nor public key certificate management problem in PKI. PKI is widely used in the Internet server to ensure its security. For signcryption, the online/offline technology can save a lot of time compared with the ordinary one. It can preprocess complex operations without the message and the receiver's identity in the offline phase. While in the online phase, it just needs some light operations with the message and the receiver's identity. In addition, the proposed scheme can provide the confidentiality, authentication, integrity and non-repudiation in the random oracle model under the Elliptic Curve Discrete Logarithm Problem (ECDLP). Comprehensive analysis shows that our design is the most efficient. Compared with other three schemes, for offline storage and ciphertext size, our scheme is optimal. For computational cost, our scheme reduces by 23%, 17% and 17%, respectively. Therefore, our design is suitable for the IoV application.

Fuzzy and Machine Learning based Multi-Criteria Decision Making for Selecting Electronics Product

In this study, we have considered electronics product as laptop one of the essential items in digital era. The decision-making and buying processes for laptops are time consuming and fraught with competing priorities. Furthermore, machine learning is used to pick and purchase laptops using a variety of strategies. Through a questionnaire that provided them with many choices for the newest features and essential components they desire in their devices, the participants' replies were sought. The participants' responses were elicited from eighteen independent input variables: processor, ram capacity, gpu, graphics card, laptop brand, type of storage, storage size, ports, screen size, backlit keyboard, pc body, category, screen display, weight, webcam, battery life, operating system, and price range. Each of the input variables was quantified using a scale using the terms very low, low, medium, high, and very high. Five input and one output processes were designed using the Mamdani technique, a conventional fuzzy reasoning tool (FLC). To arrive at a more precise knowledge of the procedure for choosing a laptop in accordance with the user's requirements, standard fuzzy systems were employed.

On the potential of temporally resolved GHG emission factors for load shifting: A case study on electrified steam generation

The integration and efficient utilization of fluctuating renewable energies are crucial for the decarbonization of energy systems. The flexibilization of industrial processes can play a key role in achieving this objective. Previous research mainly focusses on price-based demand side management. However, recent research shows that electricity prices and respective GHG emissions do not always correlate. In this study, we carry out emission-based load shifting of an electrified steam process from the pet food industry for the German electricity in 2017 to 2019. The steam system is equipped with a 130 m3 steam storage tank to enable load shifting. We analyse the impact on annual GHG emissions utilizing quarter-hourly and hourly EFs and compare the results to price-based load shifting. Further, we conduct a multi-level sensitivity analysis: We perform the analysis for each of Germany’s four transmission zones in order to analyse the effects on different electricity mix compositions. Further, we investigate steam storage size variations (65, 195, 260 and 300 m3) and carry out Monte Carlo Simulations. The Monte Carlo Simulations serve to represent real-world conditions by randomizing model input according to probability distributions.The case study results demonstrate that emission-based load shifting with hourly emission factors significantly reduces emissions by 7.1% in 2017, 8.2% in 2018, and 9.6% in 2019. In parallel, this scenario lowers electricity costs by 5.3, 6.1, and 9.6%, respectively. Price-based load shifting yields maximum financial savings (24.0% in 2017, 19.6% in 2018, and 21.7% in 2019). However, it fails to reduce emissions in 2017 and 2019, and instead causes a slight increase in 2018 (+0.1%). The analysis of the four transmission systems reveals a positive correlation between the share of RES and nuclear in the electricity mix and the potential reduction of GHG emissions until a certain threshold. Expanding storage capacity results in increased GHG savings in emission-based load shifting, while price-based load shifting exhibits only marginal changes. The highest emission reductions are achieved applying emission-based load shifting combined with maximum storage volume (10.8% in 2017, 11.5% in 2018 and 14.3% in 2019). The Monte Carlo analysis confirms the reliability of these results.Emission-based load shifting offers higher GHG reduction potentials. However, price signals are easily available and go hand in hand with business activities to reduce costs. Thus, there is an urgent need for further research to align electricity prices with corresponding time resolved emission factors, in order to raise emission reduction potential of price-based load shifting.

VESPo: Verified Evaluation of Secret Polynomials (with application to dynamic proofs of retrievability)

Proofs of Retrievability are protocols which allow a Client to store data remotely and to efficiently ensure, via audits, that the entirety of that data is still intact. Dynamic Proofs of Retrievability (DPoR) also support efficient retrieval and update of any small portion of the data. We propose a novel protocol for arbitrary outsourced data storage that achieves both low remote storage size and audit complexity. A key ingredient, that can be also of intrinsic interest, reduces to efficiently evaluating a secret polynomial at given public points, when the (encrypted) polynomial is stored on an untrusted Server. The Server performs the evaluations and also returns associated certificates. A Client can check that the evaluations are correct using the certificates and some pre-computed keys, more efficiently than re-evaluating the polynomial. Our protocols support two important features: the polynomial itself can be encrypted on the Server, and it can be dynamically updated by changing individual coefficients cheaply without redoing the entire setup. Our methods rely on linearly homomorphic encryption and pairings, and our implementation shows good performance for polynomial evaluations with millions of coefficients, and efficient DPoR with terabytes of data. For instance, for a 1TB database, compared to the state of art, we can reduce the Client storage by 5000x, communication size by 20x, and client-side audit time by 2x, at the cost of one order of magnitude increase in server-side audit time.