Ethereum Layer 2 (L2) rollups improve scalability but expose a trade-off between fast sequencer soft finality and slow Layer 1 (L1) settlement finality, limiting latency-sensitive applications that require timely and durable guarantees on transaction ordering. We introduce a Byzantine Fault Tolerant (BFT) finality layer that extends existing rollup architectures without requiring L1 or rollup protocol changes. This layer provides deterministic transaction-level finality ahead of L1 settlement by committing to the sequencer's transaction order, bridging the gap between soft and hard finality. At its core, the layer uses a 1-chain variant of the Jolteon consensus protocol, adapted to the rollup setting where a single sequencer determines transaction ordering. Experiments show sub-second committee finalization latency and stable performance under Byzantine faults.
Well construction in the oil and gas industry generates substantial emissions, necessitating precise tracking to meet environmental regulations and sustainability targets. This paper explores an innovative approach combining numerical modeling with distributed ledger technology (DLT) to monitor and manage emissions throughout the well construction process. Unlike traditional methods, which often rely on retrospective data collection, this method leverages real-time simulations and a decentralized data framework to provide actionable insights. By focusing on predictive modeling and data integration, we propose a system that enhances emissions accountability and supports operational efficiency. Case studies demonstrate its practical application, while the discussion addresses implementation challenges and future potential.
The Ethereum Improvement Proposal 3675 (EIP-3675) marks a significant shift, transitioning from a Proof of Work (PoW) to a Proof of Stake (PoS) consensus mechanism. This transition resulted in a staggering 99.95% decrease in energy consumption. However, the transition prompts two critical questions: (1). How does EIP-3675 affect miners' dynamics? and (2). How do users determine priority fees, considering that paying too little may cause delays or non-inclusion, yet paying too much wastes money with little to no benefits? To address the first question, we present a comprehensive empirical study examining EIP-3675's effect on miner dynamics (i.e., miner participation, distribution, and the degree of randomness in miner selection). Our findings reveal that the transition has encouraged broader participation of miners in block append operation, resulting in a larger pool of unique miners ($\approx50\times$ PoW), and the change in miner distribution with the increased number of unique small category miners ($\approx60\times$ PoW). However, there is an unintended consequence: a reduction in the miner selection randomness, which signifies the negative impact of the transition to PoS-Ethereum on network decentralization. Regarding the second question, we employed regression-based machine learning models; the Gradient Boosting Regressor performed best in predicting priority fees, while the K-Neighbours Regressor was worst.
New offshore oil recourses that are developed are more difficult to drill and increase the requirements to the technology in the offshore industry. A relatively new technology is Managed Pressure Drilling where a choke topside is used to control the bottom hole pressure. The bottom hole pressure measurement is unreliable, which motivates the need for an observer. Different methods for estimation will be presented and compared in this thesis. Proofs of convergence are outlined for the Stamnes observer, derived for the Grip observer and some stepping stones for further work are presented for the Optimal Polynomial Filter. Each observer is simulated with a simple step in the mud pump to verify the estimation laws. The results show that all observes estimated the bottom hole pressure correctly for this simple case. A more realistic case, a pipe connection, is also simulated for each observer. The case includes zero flow from the mud pump, which reveals that all observers miss the estimation convergence in this case, but that the estimate converges when there is flow from the mud pump. One of the states that affects the bottom hole pressure is the pressure loss due to friction in drill string and annulus. Earlier work modeled these losses as quadratic with respect to the flow through the bit, which are simplifications. To improve the estimate of the bottom hole pressure, new and better friction models are needed. Measurement data from Gullfaks C are analyzed to get new knowledge of friction loss in the drilling string and annulus. For the drill string the quadratic friction model is found to be good enough, catching the main behavior. On the other hand, the friction loss in the annulus is a more complicated function of flow. The annulus friction is approximated with sets of basis functions and the weighted sum of these functions gives an approximation to the friction curve. Each weight is estimated to get the friction loss estimate. The use of the weighted sum of four 1st-order b-spline functions give a good approximation to the real friction curve, and the weight for each basis function is estimated. This is tested in simulations both with a simple case and the pipe connection case. The simulations show that the annulus friction loss and the bit pressure are estimated correctly.