Local Power Research Articles

Real-Time Control of Thermal Synchronous Generators for Cyber-Physical Security: Addressing Oscillations with ANFIS

This paper introduces a novel real-time ANFIS controller, specifically designed for thermal synchronous generators, to mitigate the risks associated with cyber-physical attacks on power systems. The controller integrates the dynamic model of the turbine’s thermomechanical components, such as the boiler and heat transfer processes, within the synchronous generator. In contrast to previous studies, this model is designed for practical implementation and addresses often-overlooked areas, including the interaction between electrical and thermomechanical components, real-time control responses to cyber-physical attacks, and the incorporation of economic considerations alongside technical performance. This study takes a comprehensive approach to filling these gaps. Under normal conditions, the proposed controller significantly improves the management of industrial turbines and governors, optimizing existing control systems with a particular focus on minimizing generation costs. However, its primary innovation is its ability to respond dynamically to local and inter-area power oscillations triggered by cyber-physical attacks. In such events, the controller efficiently manages the turbines and governors of synchronous generators, ensuring the stability and reliability of power systems. This approach introduces a cutting-edge thermo-electrical control strategy that integrates both electrical and thermomechanical dynamics of thermal synchronous generators. The novelty lies in its real-time control capability to counteract the effects of cyber-physical attacks, as well as its simultaneous consideration of economic optimization and technical performance for power system stability. Unlike traditional methods, this work offers an adaptive control system using ANFIS (Adaptive NeuroFuzzy Inference System), ensuring robust performance under dynamic conditions, including interarea oscillations and voltage deviations. To validate its effectiveness, the controller undergoes extensive simulation testing in MATLAB/Simulink, with performance comparisons against previous state-of-the-art methods. Benchmarking is also conducted using IEEE standard test systems, including the IEEE 9-bus and IEEE 39-bus networks, to highlight its superiority in protecting power systems.

Testing for Equal Average Forecast Accuracy in Possibly Unstable Environments *

We consider the issue of testing the null of equal average forecast accuracy in a model where the forecast error loss differential series has a potentially non-constant mean function over time. We show that when time variation is present in the loss differential mean, the standard Diebold and Mariano (1995) test, which was proposed for evaluating forecasts in a stable environment, has an asymptotic size of zero, and, whilst consistent, can have reduced local power. This arises due to inconsistent estimation of the implicit long run variance estimator, which diverges under a time varying mean. We suggest a modified statistic that replaces the standard long run variance estimator based on full-sample demeaning of the loss differential series with one based on nonparametric local demeaning. The new long run variance estimator is consistent under both the null and alternative when the mean function is time varying or constant, and in both cases, the modified test recovers the asymptotic size and power properties associated with the original test in the constant mean case. The modified test therefore provides a robust method for testing the equal average forecast accuracy null, allowing for instability in the loss differential mean. The benefits of our test are demonstrated via Monte Carlo simulation and two empirical applications.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Neural decoding and feature selection methods for closed-loop control of avoidance behavior.

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.&#xD;Approach. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.&#xD;Main results. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring <310 ms for training, <0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.&#xD;Significance. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.&#xD;&#xD.

Indigenous Development of MIC-Based C-Band Dual Chain Down Converter Unit for GEOSAT- Spacecraft Checkout System

As part of miniaturization, the development work for the C-band dual-chain down converter, which is extensively used in TM (Telemetry) data acquisition system in GEOSAT spacecraft checkout system has been taken up in MIC (Microwave Integrated Circuit) approach using microstrip line structure. A prototype unit is realized using standard surface mount devices (SMD) and few indigenously developed MIC components namely, the directional couplers, the power dividers etc. The MIC-based down converter module, which forms the heart of the unit is housed in an Aluminium milled box having the dimension of 150 x 150 x 25 mm3. Except internal Local Oscillator (LO), RF Switches and Power supply, all other circuitry are mounted withinthis milled box which is housed in 1U chassis. It meets all the requirements of a conventional down converter. The paper describes the salient features of the unit, its detailed design approach and realization plan and finally the test and evaluation results of the prototype unit developed indigenously.

Regulatory and legal regulation of the process of formation of administrative authorities and management in Subcarpathian Rus (Transcarpathia) in 1919–1926

It is indicated that the proper functioning of local executive power, the creation of an optimal system of local executive power bodies should ensure effective influence on the development of each region, especially the cross-border region. It is generally accepted that local bodies of executive power implement the functions of the state, which are close to the population. In Ukraine, a new people-centered model of activity of public administration subjects is being formed. The article examines the process of formation of administrative authorities and management in Subcarpathian Rus (Transcarpathia) during the first half of the 20s of the 20th century. It was established that the process of forming the structure of state administration in Subcarpathia began already at the beginning of 1919, and in the summer of the same year, several projects of the organization of government in the region were already developed in Prague. On the basis of the General Statute, the model of relations within the local government between the Directory, the administrator and the military leadership during 1919-1920 was analyzed. The formation of abstract departments at the Civil Administration, which was headed by an administrator, is briefly described. The importance of the changes introduced by the government decree No. 356/1920 «On amending the General Statute of Subcarpathian Rus» was revealed, according to which the functions and powers were radically redistributed in favor of the bodies that represented the central government in the region. The theory of the creation of two branches of administrative management in relation to the competence of the Civil Administration of Subcarpathian Rus is presented: autonomous institutions and institutions of the state. The description of the gubernatorial council as an advisory body under the governor is provided. It was noted that the new General Statute, in terms of the norms governing the activities of the vice-governor and the gubernatorial council, went beyond the Constitution of the Czechoslovak Republic. It is proved that the administrative reorganization in Czechoslovakia in 1925 and especially in 1927 only distanced Subcarpathian Rus from real autonomy, since the process of further centralization and unification of power and administration took place throughout the territory of the republic. Appropriate conclusions have been drawn regarding the retention and gradual incorporation (integration) of the region into the all-Czechoslovak legal system.

Medium- and Long-Term Distributed Photovoltaic Power Prediction Based on Multiple Time Series Feature and Multiple-Model Fusion

Distributed photovoltaic power stations have advantages such as local direct power supply and reduced transmission energy consumption, and whose demands are constantly being developed. Conducting research on medium- and long-term distributed photovoltaic prediction will have significant value for applications such as the electricity trade market, power grid operation, and the planning of new power stations. Due to characteristics such as long time dependence, disperse power stations, and strong randomness, making accurate and stable predictions becomes very difficult. In this research, we propose a multiple time series feature and multiple-model fusion-based ensemble learning model for medium- and long-term distributed photovoltaic power prediction (M2E-DPV). Considering the wave influence and the differences in distributions in different areas of photovoltaic power, multiple feature combinations are designed to increase feature expression ability and adaptability. Based on the boost ensemble learning model, trained on a single model of different time scale features, the optimal scoring strategy is used for multiple model fusion in the rolling prediction process, and finally, time-segmented probabilistic correction is performed. The experiment results show the effectiveness of the M2E-DPV under multiple feature combinations and multi-model fusion strategies. The average MAPE, R2, and ACC indicators are 0.15, 0.96, and 0.91, respectively. Compared with other methods, there is a significant improvement, indicating that the prediction ability of the model framework proposed in this paper is advanced and robust.

Searching for optimal reactivity management using a new burnable absorber for SCLWR with (Th, 233U)O2

This work investigates the efficiency of some new burnable absorbers (BAs) suggested to provide possible improvements in the fuel management of a supercritical light water reactor (SCLWR) assembly fueled with (Th, 233U)O2. Four BAs including gadolinium, Erbium, Iridium and Lutetium are suggested as integral burnable absorber (IBA) rods. A three-dimensional model of SCLWR has been modelled using MCNPX version 2.7. A complete neutronic analysis has been carried out for the suggested BAs and compared with the gadolinium vector which is the common BAs. The IBA rods were distributed through the SCWR fuel assembly in a way that provided a flat power distribution. Various concentrations of the suggested BAs have been investigated to obtain the optimum value that can suppress the excess reactivity at the beginning of the cycle and flatten the infinite multiplication factor. The burnup parameters including the fertile, fissile, BAs and most effective fission product concentration as a function of effective full power days (EFPDs) of the SCLWR have been examined for the suggested BAs. The reactivity temperature coefficients have been studied to ensure the viability of the suggested BAs. A further analysis is performed to study the axial, radial power distribution and local power peaking factor.

A method for structural framework variation of a multilayer field associated with complicated geology

Background. During the probabilistic resource base estimation, the largest uncertainties are associated with the wide range of the gross rock volume. In the course of the formation area work, the resulting maps of gross thickness are constructed incorrectly. Objective. To take into account facies zoning when varying the structural framework if working with the Achimov strata of a multilayer field. Materials and methods. The applied approach implies accounting of the errors in structural constructions in proportion to the power depending on facies zones. The authors proposed an additional stage of mapping using proportion maps to avoid obtaining geologically unrealistic local power anomalies. Results. A set of modified maps of the total thickness of clinocyclites was obtained, reflecting the key features of the geological structure of the modeling object, particularly facies differentiation and related lateral heterogeneity. Conclusions. The chosen approach to constructing maps of gross thickness allowed to obtain geologically realistic maps of effective thickness, which, in turn, formed the basis for mapping of productive volume.

A Multi-Layer Techno-Economic-Environmental Energy Management Optimization in Cooperative Multi-Microgrids with Demand Response Program and Uncertainties Consideration.

This paper presents a multi-layer, multi-objective (MLMO) optimization model for techno-economic-environmental energy management in cooperative multi-Microgrids (MMGs) that incorporates a Demand Response Program (DRP). The proposed MLMO approach simultaneously optimizes operating costs, MMG operator benefits, environmental emissions, and MMG dependency. This paper proposed a new hybrid ε-lexicography-weighted-sum that eliminates the need to normalize or scalarize objectives. The first layer of the model schedules MMG resources with DRP to minimize operating costs (local generation and power transactions with the utility grid) and maximize MMG profit. The second layer achieves the environmental operation of the MMG, while the third layer maximizes MMG reliability. This paper also proposed a new application of a recently developed enhanced equilibrium optimizer (EEO) for solving the three-layer EM problem. In addition, the uncertainties of solar power generation, wind power generation, load demand, and energy prices are considered based on the probabilistic 2m + 1 Point estimation method (PEM) approach. Three case studies are presented to verify the proposed MLMO approach on an MMG test system. In Case I, a deterministic EM is solved to simulate the MMG as a single layer to minimize costs and maximize benefits through DRP, while Case II solves the MLMO optimization problem. Simulation results show that the proposed MLMO technique reduces environmental emissions by 2.45% and 3.5% in its optimization layer and at the final layer, respectively. The independence index is also enhanced by 2.49% and 4.8% in its layer only and as a total increase, respectively. Case III is for the probabilistic EM simulation; due to the uncertain variables effect, the mean value in this case is increased by about 2.6% over Case I.

Comparative Life Cycle Assessment and Carbon Footprint of Typical Hydrogen Energy Products

To compare the environmental impact and carbon footprint of gray hydrogen, blue hydrogen, and green hydrogen, inventories were obtained through literature research. Some inventories that were not available in China were obtained through foreign inventories combined with localized power conversion. The localized end-point destructive life cycle impact assessment method was used to calculate the environmental impact potential of the raw material acquisition, transportation, and hydrogen production stages of five hydrogen products. The carbon footprint was calculated, and the sensitivity analysis and uncertainty analysis were carried out and compared with the ReCiPe method. The results showed that： ① The environmental impact from large to small was： gray hydrogen （coal） （1 203 mPt·kg-1） &gt; blue hydrogen （coal） （876 mPt·kg-1） &gt; gray hydrogen （gas） （492 mPt·kg-1） &gt; green hydrogen （323 mPt·kg-1） &gt; blue hydrogen （gas） （252 mPt·kg-1）. The environmental impacts of gray hydrogen and blue hydrogen were mainly concentrated in climate change, fine particulate matter formation, and fossil fuels. The environmental impacts of green hydrogen were mainly concentrated in climate change, fine particulate matter formation, fossil fuels, and mineral resources. ② The carbon footprint from large to small was： gray hydrogen （coal） （23.79 kg·kg-1, measured by CO2eq, the same below） &gt; blue hydrogen （coal） （11.07 kg·kg-1） &gt; gray hydrogen （gas） （10.97 kg·kg-1） &gt; blue hydrogen （gas） （3.47 kg·kg-1） &gt; green hydrogen （1.97 kg·kg-1）. Direct carbon emissions in the production process of gray hydrogen and blue hydrogen accounted for the largest proportion, whereas that of green hydrogen accounted for a large proportion of power input. ③ Measures to reduce environmental impact and carbon emissions include reducing direct emissions of pollutants and greenhouse gases, reducing power consumption, and strengthening raw material substitution and reduction.

Assessing Nitrogen Dioxide in the Highveld Troposphere: Pandora Insights and TROPOMI Sentinel-5P Evaluation

Nitrogen oxides, particularly NO2, are emitted through a variety of industrial and transport processes globally. The world’s continuous economic development, including in developing countries, results in an increasing concentration of those gases in the atmosphere. Yet, there is scant information on the current state and recent evolution of these atmospheric pollutants over a range of spatial and temporal scales, especially in Africa. This, in turn, hinders the assessment of the emissions and the evaluation of potential risks or impacts on societies and their economies, as well as on the environment. This study attempts to fill the gap by leveraging data from a Pandora-2S ground-based, column-integrating instrument located in Wakkerstroom in the Mpumalanga Province of South Africa and space-based remote sensing data obtained from the TROPOMI instrument onboard the ESA Sentinel-5P satellite. We compare these two spatially (horizontal) representative data sets using statistical tools to investigate the concentrations of emitted and transported NO2 at this particular location, expecting that a significant positive correlation between the NO2 tropospheric vertical column (TVC) data might justify using the TROPOMI data, available globally, as a proxy for tropospheric and boundary layer NO2 concentrations over the Highveld of South Africa more generally. The data from the two instruments showed no significant difference between the interannual mean TVC-NO2 in 2020 and 2021. The seasonal patterns for both instruments were different in 2020, but in 2021, both measured peak TVC-NO2 concentrations in late winter (week 34). The instruments both detected higher TVC-NO2 concentrations during transitions between seasons, particularly from winter to spring. The TVC-NO2 concentrations measured in Wakkerstroom Mpumalanga are mostly contributed to by the emission sources in the low troposphere, such as biomass burning and emissions from local power stations.

Evaluation of transition to 100% electric vehicles (EVs) by 2052 in the United States

With the rising need to transition from fossil fuel consumption to renewables, the transportation industry is foreseeing large-scale adoption of electric vehicles (EVs). According to various studies, the petroleum resources will last until or around 2052. Utilizing the US Federal Highway Administration (FHWA) published data on the number of registered vehicles by each state, the profile of vehicles by 2052 was forecasted using a time series. The vehicle profile comprised vehicles by each type classified as automobiles, buses, trucks, and motorcycles. This future profile became the basis of the quantification of total EVs by the end of 2052. Based on the available fuel economy data published by the US Department of Energy, the average energy consumption per mile (kWh/mile) of each vehicle type was factored into the energy demand calculation for 100% electrification by 2052. A roof-top solar photovoltaic system is easy to install on unoccupied roof space for US house owners. Obtaining the capacity of such a roof-top solar PV system acts as a good decision-making criterion for both house owners and developers. However, for city-dwellers living in multi-family residential buildings, the potential of utilization of roof-top solar PV becomes a subject of future work when effective sharing of resources for local power generation by renewables and development of community-shared EV charging infrastructure is concerned. How well EVs performed against internal combustion engine (ICE) vehicles in terms of fuel efficiency and reduced carbon emissions is a driving factor for this transition. For energy demand calculations, a base model was framed, and results were obtained by utilizing mathematical and statistical tools. After accounting for the effects of improved fuel efficiency or reduced energy consumption by EVs over time, the model was modified to obtain revised energy demand and, thus, effective energy generation required. Accordingly, the capacity of EV charging infrastructure required by each state determined the need for preparation by utility companies and local jurisdictions. With limitations on battery size and thus increased frequency for drivers to return for charging, additional fast chargers (level 3) at existing gasoline stations become an option that requires further assessment.

Estimation of tidal energy potential in the Vietnam East Sea: A comprehensive analysis using semi-empirical tide models

This study assesses the tidal energy potential in the Vietnam East Sea (VES), a region with complex hydrodynamics and significant interactions with the Western Pacific Ocean. A rigorous validation of six prominent semi-empirical global ocean tide models (FES2014, DTU16, EOT20, GOT4.10c, HAMTIDE12, OSU12.V1) was conducted against an extensive network of 34 tide gauge stations in the VES region. The validation identified FES2014 as the superior model, exhibiting the closest agreement with observations due to its advanced data assimilation techniques, high-resolution grids, and robust hydrodynamic representation influenced by the complex VES bathymetry. Leveraging the FES2014 model, the influential role of the semi-diurnal component (M2) in dictating energy distribution is prominently evident. Evaluation results of tidal amplitudes revealed three emerging hotspots: the Batang Lupar estuary (Malaysia), Anpu Gang (China), and Bac Lieu (Vietnam), with tidal amplitudes up to 326.2 cm. The Anpu Gang exhibited the highest theoretical potential tidal energy of 14.90 Wh/m2, making it the most promising site for localized tidal power development. Although moderate compared to globally renowned sites, the identified VES hotspots merit consideration for small-to-medium scale projects tailored to local conditions. While limited to potential energy assessments, this study provides a crucial baseline for the VES region, highlighting opportunities for sustainable tidal energy exploitation.

Enhancing bioenergy and feed production in Southern Thailand: An approach through Leucaena cultivation and hydrothermal carbonization

This study addresses sustainability challenges in Southern Thailand, particularly the scarcity of biomass fuel and animal feed. It investigates the integration of Leucaena leucocephala cultivation with hydrothermal carbonization. The research compares the biomass yield and economic feasibility of growing Leucaena as a sole crop versus intercropping it with Para rubber trees. Sole cropping Leucaena produces higher biomass yields and is more economically viable. The wood stem of Leucaena is competitive with other biomass fuels used in local power plants, while its leaves, with over 14 % protein content, meet local animal feed market standards. Additionally, branches, which constitute 15.15 % to 30.58 % of the total biomass, are usually left as residue but can be used for hydrochar production. The study examines the effects of temperature (235 °C and 265 °C) and retention time (1, 2, and 3 hours) on hydrochar properties. Optimal condition (265 °C for 1 hour) produces hydrochar with high heating value and energy yield. Using these branches for hydrochar can significantly boost total revenue, with hydrochar contributing 54.9 % to overall revenue (4522.00 USD/ha). Integrating Leucaena cultivation with hydrothermal carbonization offers a sustainable solution, enhancing revenue, supporting local energy and feed needs, and promoting environmental sustainability.

A conditional distribution function-based measure for independence and [formula omitted]-sample tests in multivariate data

We introduce a new index to measure the degree of dependence and test for independence between two random vectors. The index is obtained by generalizing the Cramér-von Mises distances between the conditional and marginal distribution functions via the projection-averaging technique. If one of the random vectors is categorical with K categories, we propose slicing estimators to estimate our index. We conduct an asymptotic analysis for the slicing estimators, considering both situations where K is fixed and where K is allowed to increase with the sample size. When both random vectors are continuous, we introduce a kernel regression estimator for the proposed index, demonstrating that its asymptotic null distribution follows a normal distribution and conducting a local power analysis for the kernel estimator-based independence test. The proposed tests are studied via simulation, with a real data application presented to illustrate our methods.

Chemical characterization and source apportionment of atmospheric fine particulate matter (PM2.5) at an urban site in Astana, Kazakhstan

PM2.5 is a significant air quality concern in urban areas globally. PM2.5 in Astana exceeded the US EPA standard for annual mean PM2.5 EPA with an average PM2.5 mass concentration of 23.43 ± 11.50 μg/m3 and a maximum daily value of 63.8 μg/m3 in January (heating season). The lowest 3.7 μg/m3 was observed in September (non-heating season). However, source apportionment in Kazakhstan has not been done because of a lack of PM2.5 composition measurements. This study presents the first analysis of chemical composition of PM2.5 and source apportionments in Kazakhstan. It comprehensively examined Astana's PM2.5 chemical characteristics and source apportionment over ∼ two years (November 2019 to August 2021). Using elements, water-soluble inorganic ions, black carbon (BC), and estimated unmeasured mass (UMM), source apportionment was obtained using Positive Matrix Factorization (PMF) supported by conditional bivariate probability function (CBPF) analyses. Average PM2.5 contributions of the eight sources were ‘Spark Ignition’ (30.3%), ‘Coal Flyash’ (17.1%), ‘Secondary Nitrate’ (15.1%), ‘Primary Sulfate-Fuel Combustion’ (9.9%), ‘Secondary Sulfate-Coal Combustion’ (8.5%), ‘Soil/Road Dust’ (7.9%), ‘Diesel’ (7.1%), and ‘Local Power Plant(s)’ (4.2%). Local power plants burn high-ash coal and fuel oil year-round. The major contributions of heating/power plants, private residential heating systems, autonomous boilers, vehicles, asphalt pavement mixing facilities, and local construction activities. This study's source apportionment analysis provides critical insights for developing targeted air quality management strategies in Kazakhstan. Findings highlight the need for improved controls on vehicular emissions and heat/power generation sources and for the implementation of measures by local governments to effectively reduce PM2.5.

Sky localization of space-based detectors with time-delay interferometry

The accurate sky localization of gravitational wave (GW) sources is an important scientific goal for space-based GW detectors. The main differences between future space-based GW detectors, such as Laser Interferometer Space Antenna (LISA), Taiji, and TianQin, include the time-changing orientation of the detector plane, the arm length, the orbital period of the spacecraft and the noise curve. Because of the effects of gravity on three spacecraft, it is impossible to maintain the equality of the arm length, so the time-delay interferometry (TDI) method is needed to cancel out the laser frequency noise for space-based GW detectors. Extending previous work based on equal-arm Michelson interferometer, we explore the impacts of different first-generation TDI combinations and detector's constellations on the sky localization for monochromatic sources. We find that the sky localization power is almost unaffected by the inclusion of the TDI Michelson (X, Y, Z) combination in the analysis. We also find that the variation in the sky localization power for different TDI combinations is entirely driven by the variation in the sensitivities of these combinations. For the six particular TDI combinations studied, the Michelson (X, Y, Z) combination is the best for source localization.

Millimeter-Wave Far-Field Range Antenna Measurement System for a W-Band Monopulse Antenna

In this study, a millimeter-wave far-field range antenna measurement system is proposed for W-band monopulse antenna measurements. Since the W-band monopulse antenna installed in radar systems has many input/output ports, multiple calculations are required to measure all the ports. We propose a system structure capable of the simultaneous measurement of multiple channels to quickly and accurately measure the performance of a monopulse antenna. The proposed measurement system includes a multi-channel rotary joint, a pair of diplexers, and multiple vector network analyzer extenders. In addition, an RF sub-system is implemented to reliably measure the phase of the W-band radiation pattern. It utilizes a shared local oscillator source and external power amplifiers for the transmit and receive paths. Employing the proposed multi-channel antenna measurement system, the sum/difference channels of a monopulse antenna were simultaneously measured according to the azimuth angle. In addition, a comparison of the far-field measurements provided by the proposed system and the near-field measurements was conducted.

The Phenomenon of Difficult Enforcement in the Circuit Trial System in China from the Perspective of Power Operation

The phenomenon of "difficulty in enforcement" is prevalent in the judicial practice of China and poses similar challenges to circuit trials. This paper, based on Su Li's premise of the shifting local power dynamics in rural China, utilizes Foucault's theory of micro-power and Geertz's theory of local knowledge to explore how these misalignments in local power relationships affect the enforcement work in circuit trials in China, and provides suggestions on how to alleviate this issue. In the power relationship of enforcement within circuit trials, the relatively weaker state power struggles to support the compulsory execution of judgments, while the relatively stronger individual power resists the enforcement of judgments. This leads to a reliance on the recognition and cooperation of the judgment debtor, as well as the assistance of local power as a third party. However, the lack of local knowledge among judges makes it difficult for "legally sound but unreasonable" judgments to gain the recognition and cooperation of the parties involved, resulting in challenges to enforcement. Therefore, it is necessary to strengthen cooperation with local powers and other state agencies, seeking a multi-faceted approach.