bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stress-strain ratio on stabilization work in the saturated zone causes rapid road failure
Authors: Munirwansyah Munirwansyah 
Published: December 2024

Abstract
Road construction frequently encounters difficulties with soil with low bearing capacity, such as soil subsidence, which results in the deformation or waves of the surface road. The investigation was conducted at the Paya Tumpi quarry and quarry in Krueng Raya-Laweung, which are the providers of soil material used in road construction. These two quarries, due to their extensive clay soil criteria, are unsuitable for use as road subgrade material. Hence, to utilize this soil, it is necessary to stabilize it by incorporating volcanic ash. This will be accomplished by evaluating the shear strength of mixed soil, the optimum water content for compaction, and the stress-strain ratio’s behaviour as a result of the stabilization effect. To determine the cohesion and shear angles at a defined shear stress and the response of each test object to strain and stress, a series of experiments were carried out. The research findings indicated that the cohesion and shear angles of the soil samples were enhanced by the addition of volcanic ash in comparison to natural soil. The cohesion (c) of sample-1, sample-2, and sample-3 increased from 0.034-0.223 kg/cm2, 0.093-0.118 kg/cm2, and 0.008–0.037 kg/cm2, respectively. Similarly, the shear angles (ϕ) increase from 32.30o to 40.30o, 27.20o to 30.10o, and 27.90o to 36.20o, respectively. The stress-strain ratio is intermediate between local failure and punching.
Calibration of base saturation flow rate at pretimed signal intersection utilizing Bayesian linear regression considering morning and evening peak hour observations
Authors: Lulusi L. , Sugiarto S , Saleh S M , Isya M 
Published: December 2024

Abstract
Base saturation flow is a critical constraint used for evaluating the capacity of signalized intersections. The Indonesian Highway Capacity Manual (IHCM, 2023) is frequently used for signalized intersection planning in Indonesia. In heterogeneous traffic environments, the driver characteristics and behaviors exhibit significant variations. However, the base saturation flow was also influenced by traffic volume during the observation period. This study modeled the base saturation flow by analyzing differences in morning and afternoon peak hour data. The objective was to examine the base saturation flow at pretimed signal in Banda Aceh, with a focus on traffic volume fluctuations during peak hours. Four intersections were investigated in this study. Unmanned aerial vehicles were employed to record discharge flow pattern within targeted signalized intersection. Manual timing was conducted using stopwatches and geometric road data were collected through direct field measurements. Bayesian linear regression with a Gibbs sampling approach was utilized to recalibrate the saturation flow rate coefficients. The base saturation flow during the morning peak is 284 We, while in the afternoon peak, it is 285 We, with We representing the effective intersection approach.
Thermodynamic and Economic Analysis of a Trigenerative Adiabatic Compressed Air Energy Storage
Authors: Jenne Sunku Prasad , Li Chenghao , Zhang Xuecen , Navarro Helena , Wang Jihong , He Wei , Spencer Joe , Ding Yulong 
Published: December 2024

Abstract
Energy storage is indispensable for decarbonising power systems with high penetration of fluctuating renewable generation (e.g., wind, solar). In this study, an innovative trigeneration adiabatic compressed air energy storage (Tri-ACAES) system that produces heating, cooling, and electricity is proposed. The Tri-ACAES system can absorb more wind power than an ACAES system. The Tri-ACAES can work in heating and cooling mode when renewable generation is too low to charge the ACAES system due to the off-design characteristics of the compressor. The results reveal that the round-trip efficiency and turbine power output of the proposed Tri-ACAES system are enhanced by up to 5.8% and 33%, respectively than conventional ACAES system. Further, the proposed system reduced the cost of thermal energy storage by up to 60% than conventional ACAES system.
Digital Dynamic Agent (DDA) Driven Energy Contract Negotiation
Authors: Rezamand Milad , Carriveau Rupp , Stagner Jacqueline , Davison Matt 
Published: December 2024

Abstract
Digital Dynamic Agent (DDA) introduces the new paradigm of digital dynamic agent-driven energy contract negotiation, which is dynamic and real-time in adjustment. Long-term static contracts normally produce one-sided traditional market outcomes. On the contrary, DDA driving will be continuous in nature, at a level of rapid negotiations over the underlying transactions. This paper proposes a new generic DDA framework that negotiates energy contracts between grid agents and renewable generation sources, especially wind farms equipped with battery storage. The agents dynamically update their strategy concerning realtime market conditions, demand, and storage capacity. By simulation over one week with real data from the wind farms, it is evident that the DDA framework is better adapted in using battery storage for significantly lesser wastage of energy. Interestingly, the system secured energy at competitive prices in both live energy and capacity markets; it showed an improvement in cost savings compared to conventional static contracts. It also allows for effective energy procurement within diverse market states, which enables greater flexibility and responsiveness in energy management through the optimization of the negotiation process. The current paper highlights the capability of the framework to enhance negotiation outcomes while allowing the broader transition toward renewable energy markets.
Simulation of a decentralized floating offshore wind hydrogen production system with hydro-pneumatic energy storage and subsea isobaric hydrogen storage
Authors: Zhao Zecheng , Xiong Wei , Wang Hu , Sant Tonio , Carriveau Rupp , Ting David S-K , Wang Zhiwen 
Published: December 2024

Abstract
Hydrogen production using offshore wind power is a promising solution for producing clean fuels in remote areas. However, the intermittency of offshore wind power poses significant challenges to Proton exchange membrane (PEM) water electrolysis systems. Besides, there is generally an imbalance between hydrogen production and hydrogen demand. In this study, hydro-pneumatic electricity energy storage and subsea isobaric hydrogen storage are integrated into the decentralized offshore wind hydrogen production system. The hydro-pneumatic energy storage unit (HPES) is used for mitigating the intermittency and fluctuation of wind power, thereby prolonging the lifespan of PEM electrolyzers. Subsea isobaric hydrogen storage unit is used for replacing conventional isochoric hydrogen storage. In this study, simulation models of a decentralized offshore wind hydrogen production system with various configurations are established with the software Simcenter Amesim 2021.1. The results show that an 83% reduction in the on/off operation can be achieved with the help of hydro-pneumatic electricity energy storage. The isobaric and isothermal storage of compressed hydrogen can be achieved with subsea isobaric hydrogen storage, saving compression energy and facilitating thermal management. In terms of the investigated decentralized offshore wind hydrogen production system, the amount of produced hydrogen is increased by less than 1% by integrating hydro-pneumatic energy storage and subsea isobaric hydrogen storage.
Mathematical comparison of classical and quantum mechanisms in optimization under local differential privacy
Authors: Yoshida Yuuya 
Published: December 2024

Abstract
Differential privacy (DP) is an influential method of protecting private data, and is a condition for a conditional probability distribution. Since we can regard DP as a condition for a tuple of probability vectors, it is natural to consider a similar condition for a tuple of density matrices as a quantum version of DP. This condition is called classical-quantum DP (CQ-DP) because it is considered in converting classical data to quantum states. In the study of DP (including CQ-DP), a positive parameter ɛ represents the privacy level to be guaranteed. In this paper, we show that CQ-DP has a quantum advantage in certain optimization problems. Moreover, we compare classical DP and CQ-DP mathematically to clarify a relation between privacy level and quantum advantage.
Towards Water Hyacinth Identification using Index Vegetation from Sentinel-1 and Sentinel-2 in Selorejo Dam
Authors: Sahda Nabila Cut , Taufik Muhammad 
Published: December 2024

Abstract
Water hyacinths have become a significant issue at the Selorejo Dam in recent years. The invasive nature of this plant and the dam’s location, surrounded by rice fields, make predicting the growth of water hyacinths at the Selorejo Dam challenging. Several studies have applied passive remote sensing satellite technology, such as Sentinel-2A/B, to observe water hyacinth growth as a substitute for in-situ observations, which are limited by time and area coverage. However, considering that rainfall in the Selorejo Dam area can reach up to 3000 mm/year, passive satellites alone may not sufficiently accommodate the need for comprehensive water hyacinth monitoring. The first successful research utilizing Sentinel-1 for monitoring water hyacinths was conducted in 2021 at Vembanad Lake, India. This research addresses the limitations of passive satellites in identifying water hyacinths due to weather conditions. It integrates passive satellite data from Sentinel-2 and active satellite data from Sentinel-1 to provide a more complete identification of water hyacinths in the Selorejo Dam. The results of this research indicate that both Sentinel-1 and Sentinel-2 can differentiate between vegetation and water areas using DPSVIm for Sentinel-1, with an overall accuracy of 0.8, and NDVI for Sentinel-2, with an overall accuracy of 0.84. These findings represent a preliminary step in the process of identifying water hyacinths in the Selorejo Dam.
Deep Learning Mask R-CNN and Template Matching Algorithm For Tree Counting Analysis of Oil Palm Trees (Case Study: East Tanjung Jabung District, Jambi Province)
Authors: Cahyono A.B , Ristawan S.H 
Published: December 2024

Abstract
Indonesia is one of the largest producers and exporters of palm oil in the world. According to the Central Bureau of Statistics (BPS) in 2022 palm oil production has increased to 46.82 million tons. Palm oil has great potential; thus, technology is needed to analyze its productivity. Therefore, calculating palm tree percentages is an important aspect of land monitoring, plant maintenance, and efficient production planning. However, manual palm tree counting is time-consuming and labor-intensive and tends to be less efficient in large plantations. In addition, the lack of accuracy in estimating the number of trees and the age of crops can affect land planning and management. Algorithm development in geomatics science can automatically calculate oil palm trees using aerial photo data combined with template matching and deep learning methods, which are expected to provide efficient and accurate solutions. In this study, 620 samples of oil palm trees were trained. From the visual interpretation results, 6212 trees were obtained; from the processing results using the deep learning method, 6359 trees were obtained, and using the template matching method, 6756 trees were obtained. Through the validation test using the confusion matrix, the overall accuracy of the deep learning method was 95.15%, and the overall accuracy obtained using the template matching method was 87.17%.
Using Sentinel-2 Imagery and Machine Learning Algorithms to Estimate Deforestation (Case Study: a district in Malang Regency)
Authors: Maheswara S D A , Nurwatik Nurwatik 
Published: December 2024

Abstract
Forests are an inseparable element of human life. As time passes, the population of humans and other living beings will continue to increase which may induce the conversion of forest land will be widespread. This land conversion activity causes deforestation. In 2019, protected forest land in Sumbermanjing Wetan District, Malang Regency was reduced by approximately 200 Ha. To achieve Sustainable Development Goal (SDG) number 15 on sustainable forest management, a deforestation analysis is conducted to assess the rate of forest land reduction and predict forest land reduction from 2024 to 2028. This research classifies the rest land using Sentinel-2 satellite imagery from 2019 to 2023. Based on this classification, this research estimates the rate of reduction and prediction of forest area using the polynomial regression during 2024 – 2028. This research applies machine learning algorithms; random forest, SVM, and CNN as the classification method. The dataset is split into training and testing data with a ratio of 70:30. The results witness that the best algorithm for classifying forest areas is a random forest with an average accuracy of 0,957. The forest area based on the classification results using random forest from 2019 to 2023 is consecutively 14342,98 Ha, 12243,44 Ha; 13033,36 Ha; 14677,75 Ha; and 15347,88 Ha. This research concludes that the forest in Sumbermanjing Wetan District has no deforestation, with an average rate of forest addition of 438.084 Ha/year and the prediction of forest area in the period of 2024 – 2028 is consecutively 15228,73 Ha; 15666,814 Ha; 16104,898 Ha; 16542,981 Ha; and 16981,065 Ha.
Palm Oil Tree Canopy Identification Using Deep Learning Approach (Case Study: Tanjung Gusta District, North Sumatera)
Authors: Fitri Alya Nurul , Hapsari Handayani Hepi , Fuad Rachmadi Reza , Nabila Rizki Ramadhani Anisa , Ihsan Muhammad , Mangopo Mikael 
Published: December 2024

Abstract
The palm oil plantation industry in Indonesia has growing rapidly as demand for palm oil increases globally. This needs to be supported by technological innovation to increase palm oil production. One of them is to integrate the power of artificial intelligence technology. This research aims to develop a robust and accurate method for segmenting oil palm trees in plantation areas. Leveraging deep learning algorithms techniques, the research explores the potential of SAM in accurately delineating individual oil palm trees derived from aerial imagery data. The study also involves the development of a comprehensive and versatile labelled dataset to support the training and validation of the deep learning models for oil palm tree counting and segmentation. The performance of the proposed approach is evaluated and discussed critically. This research demonstrates the potential of deep learning algorithms for large-scale mapping and accurate counting of oil palm trees in plantation areas. The author hopes that the result and analysis of this research will give insight and improvement in detecting oil palm trees using automatic method.
Study of Orthophotos Spatial Quality from Integrated Physical Data Collection in Samarinda City
Authors: Buyung Permadi Fajar , Widjajanti Nurrohmat , Ariefiansyah Singagerda Fierman , Riswanto Totok 
Published: December 2024

Abstract
Integrated physical data collection as a mapping instrument and an up-to-date and comprehensive base map have been carried out on a massive scale by the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency throughout Indonesia. There are facts in the field that the results of orthophoto products still have a spatial quality that could be more optimal. In Samarinda City, this activity’s subdistrict was indicated to have a geometric accuracy of above 0.5 m. This condition does not meet the accuracy tolerances stated in the Service Level Agreement (SLA) or relevant regulations. Therefore, alternative solutions are needed to improve the quality of orthophotos by paying attention to economic value. This research aims to evaluate, and improve the spatial quality of orthophotos resulting from integrated physical data collection in 2023 in Samarinda City without re-acquisition. In other words, this research aims to determine alternative low-cost and efficient solutions to improve orthophotos’ spatial data quality. Data in the form of orthophotos and a 2023 Ground Control Point (GCP) coordinates list in Samarinda City. The approach method is the addition of GCP in the reprocessing of orthophotos. The study area is hilly and flat terrain. Results analysis uses geometric accuracy (horizontal and vertical) and semantic accuracy tests. The geometric accuracy test results before reprocessing had a CE90 value of 0.438 m based on the SLA attachment. However, they exceeded the CE90 threshold of ≥ 0.5 m based on the results of field suppletion measurements. The results of the semantic accuracy test have a National Imagery Interpretability Rating Scale (NIIRS) level 6, which means that orthophotos can make it easier to interpret geographical objects in detail. reprocessing with the addition of GCP can increase geometric accuracy with a CE90 value of 0.240 m and LE90 1.01 m. The spatial quality of orthophotos before and after reprocessing has a relatively significant difference. Adding GCP followed by reprocessing can be used as an alternative solution to improve the quality of orthophotos at a low cost.
Fill Volume Calculation Analysis of The Meninting Dam Project Based on Terrestrial and Photogrammetry Measurement Data Using The Cross Section Method
Authors: Ardiansyah Muhammad , Sania Irbah Nafisatus 
Published: December 2024

Abstract
Meninting Dam is a construction included in National Strategic Project (PSN) to support food and water security, especially in Eastern Indonesia, and also serves as a construction project for a dam located on Lombok Island. The earthwork becomes a crucial in construction work. It is necessary to calculate the volume to monitor the progress of the work. Measurements for volume calculation can be done with terrestrial methods and photogrammetric method. Therefore, it is necessary to compare the two measurement methods. The results of the total fill volume calculation were 1,699,589.02 m3 for drone measurements, 1,695,142.13 m3 for GNSS-RTK measurements, and 1,688,170.66 m3 for total station measurements. From these results, there is a difference of 4,446.89 m3 between drone measurements and GNSS-RTK and 6,971.47 m3 between total station measurements and GNSS-RTK. Based on the paired sample t-test (statistical test), at the 95% confidence level, the difference in area’s profile per STA and volume per partition of total station and drone measurements is not very significant to the data that is considered correct, which is the GNSS-RTK measurement. ASTM standard test, related to precision with precision error test, drone measurement is more precise than total station measurement. Regarding accuracy with the volume error test, drone measurements are more accurate than total station measurements. The results of this research can be used to support SDGs activities in infrastructure development and water resources management.
Localities of ID-74 to DGN95 Coordinate Transformation Parameters
Authors: Sidiq Teguh P. , Purwanto Heri , Gumilar Irwan , Retno Atik , Prijatna Kosasih , Nurwinas Dzun , Hibatul Wafi Hudaya , Dirgaputera Trizky 
Published: December 2024

Abstract
As can be referred to almost all regulation in Ministry of Energy and Mineral Resources, all spatial information used in the oil and gas industry in Indonesia, should be referenced to DGN95. While it seems that the datum is now deprecated, the use of DGN95 in many data, especially seismic data collected in 1970 to 1990 are still used and maintain in many companies to avoid huge data transformation work if it is applied nationwide. Some work in finding coordinate transformation has been done since early 2000, but it produces a different set of parameters. In this paper, we collect some published ID74 to DGN95 coordinate transformation parameters and compare to our own results. We measure 20 common points on oil and gas fields using the GNSS method. However, only three points were found consistent and suitable to be the common points. Using the Molodensky-Badekas method, we generate a set of coordinate transformation parameters to be used in the fields. There are 10 parameters to be estimated, three translations, three rotations, one scale and coordinate of center if rotation parameters. Again, there are discrepancies between our results and other published parameters. This shows that the transformation parameters are usually exclusive to the field or region. Our study suggests that coordinate transformation parameters are best to calculate for each specific concession area.
Analysis of Semeru Volcano Deformation Using Static and Kinematic Solutions with GPS Data 2015-2023
Authors: Yuki Tamim Bayoumi Ilham , Maulida Putra , Haerani Nia 
Published: December 2024

Abstract
Semeru Volcano is the highest peak on the island of Java. Semeru is characterized by continuous volcanic and strombolian eruptions that produce eruptive material which might result in casualties and infrastructure damage. One of them is the eruption on December 4, 2021. Therefore, to reduce the impact of future disasters, deformation monitoring using GPS is needed. In this study, we utilize GPS observation data located on Semeru Volcano and process it using GAMIT/GLOBK and TRACK. Processing is done with static solutions, kinematic solutions, and strain to determine the deformation pattern before, during, and after the December 4, 2021, eruption. The processing results of statis solution shows the pattern of displacement to the southeast in accordance with the direction of movement of the Sunda Block, with a range of 21.49 mm/year to 30.96 mm/year. The inSluence of the Sunda Block and other subduction-induced deformations were removed by referenced to CLUM station located 25 km from the volcano. The value of local velocity experienced a velocity of 1.81 mm/year to 7.83 mm/year with varying directions of movement. Kinematic processing using TRACK succeeded in showing the deformation pattern that occurred in the eruption of Semeru Volcano on December 4, 2021. Time Series in the Sirst phase (07:46:59 UTC–07:51:10 UTC) shows an inSlation and extension pattern of 0.054 m at the LEKR and 0.055 m at the SAWR station, followed by a deSlation and compression pattern of −0.018 m at the LEKR and −0.031 m at the SAWR in the second phase (07:51:10 UTC–07:56:00 UTC). The third phase (07:56:00 UTC– 08:03:00 UTC) showed deSlation and compression patterns with values of −0.058 m at the LEKR and −0.003 m at the SAWR. While using strain calculations, in the phase before the eruption, the strain was dominated by compression on the entire mountain body. Meanwhile, the phase after the eruption has a pattern that is divided into two, namely compression on the east side of the mountain and compression-extension on the southeast side of the mountain. These results are expected to contribute to understand the characteristics Semeru Volcano eruption in the future based on geodetic data.