esa-sceva/satcom-chunk-collection huggingface dataset:
# Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modelling
###### Abstract  
Steam huff and puff injection is one of the thermal EOR methods in which steam is injected in a cyclical manner alternating with oil production. The cost and time inefficiency problem of reservoir simulation persists in the design of a steam huff and puff injection scheme. Building predictive proxy models is a suitable solution to deal with this issue. In this study, predictive models of the steam huff and puff injection method were developed using two machine learning algorithms, comprising conventional polynomial regression and an artificial neural network algorithm. Based on a one-well cylindrical synthetic reservoir model, 6043 experiment cases with 28 input parameter values were generated and simulated. Outputs from the results such as cumulative oil production, maximum oil production rate and oil rate at cycle end were extracted from each simulation case to build the predictive model. Reservoir properties that could change after an injection cycle were also modeled. The developed models were evaluated based on the fitting performance from the R-square value, the mean absolute error (MAE) value and the root mean square error (RMSE) value. Then, Sobol analysis was conducted to determine the significance of each parameter in the model. The results show that neural network models have better performance compared to the polynomial regression models. Neural network models have an average R-square value of over 0.9 and lower MAE and RMSE values than the polynomial regression model. The result of applying the Sobol analysis also indicates that initial reservoir water saturation and oil viscosity are the most important parameters for predicting reservoir production performance.  
artificial neural network; enhanced oil recovery; polynomial regression; proxy model; reservoir simulation; steam huff and puff
# Fault Estimation for Semi-Markov Jump Neural Networks Based on the Extended State Method
###### Abstract  
This paper addresses fault estimation in discrete-time semi-Markov jump neural networks (s-MJNNs) under the Round-Robin protocol and proposes an innovative extended state observer-based approach. Unlike studies considering only constant transition rates, this work investigates s-MJNNs with time-varying transition probabilities, which more closely reflect practical situations. By incorporating actuator and sensor faults as augmented state variables, an extended state observer is proposed to estimate system states and faults simultaneously. To alleviate network congestion and optimize communication resources, the Round-Robin protocol is adopted to schedule data transmission efficiently. By constructing a Lyapunov-Krasovskii functional and applying the discrete Wirtinger inequality, sufficient conditions are derived to ensure the mean square exponential stability and dissipative performance of the system. The observer gain parameters are computed using the linear matrix inequality (LMI) method. Numerical simulations validate the effectiveness and performance of the proposed fault estimation method.  
fault estimation; semi-Markov jump system; extended state observer; neural networks; discrete-time systems Article
## 3 Results and Discussion
### GFET DC Measurement  
Electrical measurements of fabricated GFET are routinely done at room temperature. Figure 3 shows the input and output characteristics of a flexible GFET comparing both model and measured data. The extracted carrier mobility of electrons is 9050 cm\({}^{2}\)/V\(\cdot\)s and that of holes is 3000 cm\({}^{2}\)/V\(\cdot\)s. Graphene FETs show velocity saturation. We attribute this to the smooth surface (surface roughness \(<\)1 nm) on polyimide substrate (with liquid polyimide curing) and high K gate dielectric (Al2O3) which will give rise to well-behaved linear current profile followed by saturation [16].
## 3 Model Building
### Mathematical Model of Ion Leaching and Diffusion in Fly Ash Micro-Nano Pores
Using the separation of variables method,  
\[u=R(r)T(t) \tag{7}\]  
Substituting Equation (7) into the first equation in Equation (6),  
\[R(r)T^{\prime}(t)=DT(t)R^{\prime\prime}(r) \tag{8}\]  
Separating the variables in Equation (8),  
\[\frac{T^{\prime}(t)}{DT(t)}=\frac{R^{\prime\prime}(r)}{R(r)}=-\lambda^{2} \tag{9}\]  
According to Equation (9),  
\[R^{\prime\prime}(r)+\lambda^{2}R(r)=0 \tag{10}\]  
\[T^{\prime}(t)+\lambda^{2}DT(t)=0 \tag{11}\]  
By solving Equation (10) and taking into account the two boundary conditions in Equation (6), Equation (12) can be derived. Similarly, by solving Equation (11), Equation (13) can be derived. \[\left\{ \begin{array}{l}R(r)=Asin(\lambda r)+Bcos(\lambda r)\\ R^{\prime}(0)=R(r_{0})=0\end{array} \right. \tag{12}\]  
\[T(t)=\gamma exp\Big{(}\lambda^{2}Dt\Big{)} \tag{13}\]  
In Equations (12) and (13), \(A\), \(B\), \(\gamma\), and \(\lambda\) are undetermined constants. By substituting the second boundary condition from Equation (12) into the first equation of Equation (12), Equation (14) can be derived. \[\left\{ \begin{array}{l}R^{\prime}(0)=\lambda Asin(0)+\lambda Bcos(0)=0\\ R(r_{0})=Asin(\lambda r_{0})+Bcos(\lambda r_{0})=0\end{array} \right. \tag{14}\]  
According to Equation (14),  
\[\left\{ \begin{array}{l}A\
eq 0,B=0\\ sin(\lambda r_{0})=0\Rightarrow\lambda=\frac{n\pi}{r_{0}},n=1,2,3\ldots \infty\end{array} \right. \tag{15}\]  
By substituting \(B\) and \(\lambda\) from Equation (15) into (7), the nth special solution Equation (16) can be derived. \[u_{n}=c_{n}sin\bigg{(}\frac{n\pi}{r_{0}}r\bigg{)}exp\Bigg{(}\bigg{(}\frac{n \pi}{r_{0}}\bigg{)}^{2}Dt\Bigg{)} \tag{16}\]  
In the equation, \(c_{n}=B_{n}\gamma_{n}\), the general solution of Equation (6) is obtained by applying the superposition principle. \[u=\sum_{n=1}^{\infty}c_{n}sin\bigg{(}\frac{n\pi}{r_{0}}r\bigg{)}exp\Bigg{(} \bigg{(}\frac{n\pi}{r_{0}}\bigg{)}^{2}Dt\Bigg{)} \tag{17}\]  
By substituting the initial conditions from Equation (6) into Equation (17),  
\[((C_{0}-C_{a})r=\sum_{n=1}^{\infty}c_{n}sin\bigg{(}\frac{n\pi}{r_{0}}r\bigg{)} exp\Bigg{(}\bigg{(}\frac{n\pi}{r_{0}}\bigg{)}^{2}Dt\Bigg{)} \tag{18}\]  
Figure 4d illustrates that, under the same conditions, the results calculated by the model align with the experimental findings, confirming that the model effectively predicts the evolution of alkaline ion concentration during the dissolution and diffusion process of fly ash. A comparison and verification were performed among the single-scale model, the multi-scale model, and the experimental data. The results show that the single-scale model fails to accurately match the experimental results, indicating its inability to properly calculate and describe the evolution of ion concentration in the dissolution and diffusion process of fly ash.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 3 Preliminaries
### Bilinear Pairing  
Let \(\mathsf{q}\) be a prime, \(\mathbb{G}_{1}\) and \(\mathbb{G}_{2}\) be two cycle groups of prime order \(\mathsf{q}\), on which the operations are addition and multiplication, respectively. The bilinear mapping \(e:\mathbb{G}_{1}\times\mathbb{G}_{1}\rightarrow\mathbb{G}_{2}\) from \(\mathbb{G}_{1}\) to \(\mathbb{G}_{2}\) satisfies the following properties:  
1. Bilinearity: For all \(P,Q,R\in\mathbb{G}_{1}\) and \(a,b\in Z\), there are \(e(aP,bQ)=e(P,Q)^{ab}\), or \(e(P+Q,R)=e(P,R)\cdot e(Q,R)\) and \(e(P,Q+R)=e(P,Q)\cdot e(P,R)\) then the mapping is said to be bilinear.
2. Non-degeneracy: If \(P\) is the generator of \(\mathbb{G}_{1}\), then \(e(P,P)\) is the generator of \(\mathbb{G}_{2}\).
3. Computability: For all \(P,Q\in\mathbb{G}_{1}\), there is an efficient algorithm for calculating \(e(P,Q)\).
# Breast Cancer Survival Analysis Model
## 2 Materials and Methods
### Kaplan-Meier Survival Analysis  
Kaplan-Meier survival analysis considers the survival rate at each point in time, and this is displayed simultaneously in a step-shaped function graph. Categorical variables  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variables** & **Value (\%)** & **Variables** & **Value (\%)** \\ \hline Gender & & Behavior code & \\ \hline Male & 6 (0.3\%) & Carcinoma in situ & 340 (16.3\%) \\ Female & 2083 (99.7\%) & invasive Carcinoma & 1749 (83.7\%) \\ \hline Age & & Graded differentiation & \\ \hline \(>\)45 years & 1644 (78.7\%) & Well differentiated & 257 (12.3\%) \\ \(<\)45 years & 445 (21.3\%) & moderate differentiation & 844 (40.4\%) \\  & & Poor differentiation & 775 (37.1\%) \\  & & Unknown & 213 (10.2\%) \\ \hline Clinical Stage & & Pathological stage combination & \\ \hline
0 & 315 (15.1\%) & 0 & 343 (16.4\%) \\
1 & 464 (22.2\%) & 1 & 432 (20.7\%) \\
1A & 371 (17.8\%) & 1A & 323 (15.0\%) \\
2A & 640 (30.6\%) & 2A & 500 (23.9\%) \\
2B & 160 (7.7\%) & 2B & 201 (9.6\%) \\
3A & 64 (3.1\%) & 3A & 159 (7.6\%) \\
3B & 26 (1.2\%) & 3B & 15 (0.7\%) \\
3C & 13 (0.6\%) & 3C & 79 (3.8\%) \\
4 & 36 (1.7\%) & 4 & 37 (1.8\%) \\ \hline Surgical Treatment & & Radiotherapy & \\ \hline No & 52 (2.5\%) & No & 1040 (49.8\%) \\ Yes & 2037 (97.5\%) & Yes & 1027 (49.2\%) \\ \hline Chemotherapy & & Hormone Therapy & \\ \hline No & 908 (43.5\%) & No & 578 (27.7\%) \\ Yes & 1179 (56.4\%) & Yes & 1506 (72.1\%) \\ unknown & 2 (0.1\%) & unknown & 5 (0.2\%) \\ \hline Estrogen Receptor Test (ERA) & & Progesterone Receptor Test (PRA) & \\ \hline (\(-\)) Negative & 532 (25.5\%) & (\(-\)) Negative & 488 (23.4\%) \\ (\(+\)) low positive & 222 (10.5\%) & (\(+\)) low positive & 480 (23.0\%) \\ (\(+\)) medium Positive & 297 (14.2\%) & (\(+\)) medium Positive & 365 (17.5\%) \\ (\(+\)) High Positive & 829 (39.7\%) & (\(+\)) High Positive & 542 (25.9\%) \\ (\(+\)) Positive unknown & 13 (0.6\%) & (\(+\)) Positive unknown & 16 (0.8\%) \\ Non-breast cancer patient & 184 (8.8\%) & Non-breast cancer patient & 185 (8.8\%) \\ Condition unknown or undetected & 12 (0.6\%) & Condition unknown or undetected & 13 (0.6\%) \\ \hline Survival rate & & & \\ \hline Died & 53 (2.5\%) & & \\ Survived & 2036 (97.5\%) & & \\ \hline \end{tabular}
\end{table}
Table 1: Basic attributes of breast cancer patients.  
such as gender, treatment, and age were used to calculate the cumulative survival rate and then plot the survival curve separately, followed by comparing the survival rate of patients with BC who received surgery with those who did not [12].
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 2 Access Sharing Models
### Local Loop Unbundling (LLU)
#### 2.1.2 Full Unbundling  
In the full unbundling architecture, copper pairs connecting the Main Distribution Frame (MDF) in the Central Office (CO) to the subscribers are leased to the OLO by the incumbent. When a customer requests to avail of its services, the OLO takes over the entire physical link from the MDF to the customer, as shown in Figure 0(b).  
With full unbundling, OLOs have full flexibility in deciding physical layer parameters, including transmission technology, spectrum used, etc. As the incumbent leases the full physical medium, it cannot offer any service to that customer, until the line is released by the OLO (i.e., if the customer moves back to the incumbent operator) [4].  
One technical issue made more complex by network sharing is the handling of cross talk. Cross talk is the mutual interference between copper pairs that are tightly packed into high-density cables. The energy radiated from each pair is received by all other pairs, generating interference both towards the same direction where the signal was generated (called Near End Cross Talk--NEXT) and towards the opposite direction (called Far End Cross Talk--FEXT). Since the interfering signals are all known to the DSLAM and the interference matrix between pairs can be approximated, it is possible to reduce the cross talk considerably using a technique known as \"Vectoring\" [5]. However, when copper cables are shared across multiple operators, such as in a full unbundling scenario, the copper lines will terminate at different DSLAMs. Since each DSLAM can only apply interference cancellation to the copper pairs it terminates, the system performance deteriorate compared to the case where a DSLAM terminates all copper pairs.  
One possible solution for such shared access case is correlating DSLAMs from different operators. For example, Cross-DSLAM Vectoring performs vectoring operations across multiple DSLAMs by coordinating them so that the vector group spans lines that terminate on different vectored DSLAMs [6]. However, this itself raises security problems because of shared management network [7]. In [8] the authors propose a sub-band vectoring solution for Multi-Operator environments which provides fair resource sharing between operators and meanwhile is quasi-optimal in terms of data-rate and  
Figure 1: Unbundling Techniques.  
does not impose the drawbacks of the Multi-Operator Vectoring architectures, such as deployment compatibility issues.  
An alternative sharing technique is based on the concept of \"Virtual Unbundling\", like bitstream access and Virtual Unblunded Local Access (VULA), discussed in the next sections, which can reduce crosstalk as well as OLO's initial deployment costs [7].
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology  
An end-to-end, automated GNSS IoT system has been developed in Australia to deliver near-real-time insights into ground surface movement and structural stability through high-precision GNSS positioning techniques [25; 26]. While the primary design objective of this system is to monitor long-term three-dimensional displacement using static GNSS processing, this study adapts the processing mode to kinematic to estimate WSE. Additionally, a suite of integrated sensors is employed to enhance the understanding of short-term WSE variations and environmental influences.
# Targeted Delivery Strategies for Hydrophilic Phytochemicals
## 3 Hydrophilic Phytochemicalemicals
### Luteolin  
#### 3.3.1 Structural Properties and Biological Functions  
Luteolin is a flavone with a \(3^{\prime}\!,\!A^{\prime}\!,\!5\),\(7\)-tetrahydroxyl flavone structure (Figure 3b), containing four hydroxyl groups [119; 123]. Although slightly less hydrophilic than quercetin, its polarity remains substantial, affecting its solubility and biological interactions. The highest solubility of luteolin is observed in polar protic solvents--especially mixtures of water with methanol or ethanol [99; 127]. Luteolin exhibits broad biological activity, including antioxidant, anti-inflammatory, and anticancer effects [128; 129]. As an aglycone, however, it suffers from limited bioavailability due to its moderate solubility in water and vulnerability to degradation. Glycos like luteolin-7-O-glucose exhibit significantly improved solubility and chemical stability, making them more effective formulations [115; 117]. Despite the advantages of glycosylation, biotransformation of luteolin into its glycoside form remains inefficient, with a yield of approximately 8%, lower than that of other flavonoids [115]. According of [PERSON] 2021, luteolin may inhibiting protein kinase activity and reducing expression of pro-inflammatory enzymes such as cyclooxygenase-2 (COX-2) [7; 130]. Luteolin reduces the expression of pro-inflammatory cytokines (IL-6, IL-8, TNF-\(\alpha\)) and the activity of COX-2 and iNOS, alleviating psoriatic skin [30]. In nanosuspensions and bilosomes, it accumulates more effectively in the skin, showing stronger anti-inflammatory and photoprotective activity [90]. Formulas containing Pluronic F127 and alginate improve its physicochemical stability and skin permeation [131]. Recent studies have demonstrated that luteolin exhibits anti-photoaging properties by inhibiting  
Figure 3: Chemical structure of high hydrophilic flavonoids; (**a**) Quercetin (**b**) Luteolin (**c**) Apigenin.  
the MAPK pathway in UVB-exposed skin cells, thereby mitigating oxidative stress and inflammation, which supports its dermatological application [132].
# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 3 Accurate Registration
### Transform Matrix Calculation  
In each iteration, a threshold is required when building links between points with the help of features. If the Euclidean distance between two points is less than the threshold, the link between the two points will be preserved. We propose to set the adaptive threshold to speed up the iteration. The threshold is reduced after each iteration. The RANSAC algorithm is used to further remove incorrect links. In the process of finding the optimal transformation through the corresponding relation, we use the algorithm proposed by [PERSON] (2007) to find the transformation matrix. When \(\theta\approx 0\), there is sin\(\theta\approx 0\), cos\(\theta\approx 1\). According to this, when \(\alpha\), \(\beta\), \(\gamma\approx 0\), we have:  
\[M=T\big{(}t_{x},t_{y},t_{z}\big{)}\cdot R(\alpha,\beta,\gamma)\approx T\big{(} t_{x},t_{y},t_{z}\big{)}\cdot\hat{R}(\alpha,\beta,\gamma)=\hat{M} \tag{7}\]  
where \(T\) is the transformation matrix, \(R\) is the rotation matrix, \(\hat{R}\) is the approximate rotation matrix, \(M\) is the 3D rigid transformation matrix and \(\hat{M}\) is the approximate 3D rigid transformation matrix.  
After the above approximation, the original nonlinear problem is converted into a linear problem. The linear least-squares method can then be used to calculate the optimal transformation matrix by using the following equation:  
\[M_{opt}=\text{argmin}_{\hat{M}}\underset{i}{\sum}\big{(}\big{(}\hat{M}\cdot s _{i}-d_{i}\big{)}\cdot n_{i}\big{)}^{2} \tag{8}\]  
where \(M\omega_{pt}\) and \(\hat{M}\) are the 4 \(\times\) 4 transformation matrixes, \(S_{i}\) is the source point, \(d_{i}\) is the target point, and \(n_{i}\) is the normal vector of the target point.
# Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
## 4 Quantitative Experiment
### Croatia 2020
#### 4.3.1 \(500\times 300\) km  
Figure 12 showcases the EMF intensity activity across a \(500\times 300\) km area before and after the seismic event. Each data point represents the mean intensity value per day, calculated using Equation (10). The figure consists of two sub-figures (as in Section 4.2.1): Figure 12(a),b. In Figure 12(a), the unmodified time-series obtained from the \(1000\times 800\) km grid is depicted. It is noticeable that approximately 1 year prior to the event date, the values obtained for Beta exhibit errors, showing zero values for \(b_{x}\), \(b_{y}\), and \(b_{z}\). To enhance visual clarity, Figure 12(b) displays only the time-series of Alpha and Charlie. It is important to highlight that, as in Section 4.2.1, though we modified the plot in Figure 12(b) to exclude Beta, we did not exclude Beta from our analysis.  
Figure 12 shows the plots obtained from processing the data shown in Figure 12(a) using the Martingale and \(z\)-Martingale systems for Alpha, Beta, and Charlie EMF time-series produced over a \(500\times 300\) km grid centered on the epicenter of the 2020 Croatian earthquake. Figure 12(a) shows the results of using the Martingale system to detect anomalies on the EMF data. It is observable that no anomalies are detected by the Martingale through the entire time-series for Alpha; the Beta time-series produces anomalies at roughly 1 month post the event date; and Charlie produces some anomalies roughly 4 months prior to the event date. It is noteworthy to highlight the waveform variations that occur through the plotting.  
Figure 12: Comparison of Martingale and \(z\)-Martingale values within the \(500\times 300\) km grid for the Croatia case study. The color coding is Alpha (red), Beta (blue), and Charlie (green). (**a**) Martingale value data plotted within the \(500\times 300\) km grid obtained using the original Swarm satellite data alongside Equation (3). It is observable that this approach detects fewer anomalies compared to the \(z\)-Martingale (**b**) method. (**b**) \(z\)-Martingale value data plotted within the \(500\times 300\) km grid obtained using the original Swarm satellite data alongside Equation (7). It is observable that the \(z\)-Martingale scores produce more anomaly detections than the regular Martingale method (**a**) method.  
Figure 13: _Cont._  
Figure 12b shows the anomalies detected using the \(z\)-Martingale system. Alpha produces some anomalies ahead of the event date, in contrast with the plot in Figure 12a; notably, anomalies are detected at roughly 1 year and 4 months prior to the event date. Beta produces anomalies at 1 year and 5 months prior to the event date and 1 month following. Charlie produces anomalies at roughly 1 year and four months.
# Advances in the Synthesis and Physiological Metabolic Regulation of Nicotinamide Mononucleotide
## 3 Biosynthesis of NMN
### ATP Regeneration Systems  
The synthesis of NMN by any biological enzyme requires a relatively costly substrate, ATP. Consequently, the ratio of ATP consumption to the production of NMN is crucial for the cost of the catalytic process. Therefore, the recycling of ATP, as well as the high activity and stability of each enzyme, are crucial for efficient and low-cost production of NMN. It has been reported that the introduction of an ATP regeneration system based on polyphosphate kinase (PPK) can significantly reduce ATP consumption, thereby reducing the cost of NMN production [76]. PPK facilitates the reversible transfer of a phosphate group between inorganic polyphosphate (PP) and ATP [77]. Given the abundance and low cost of PP, PPK-based ATP regeneration systems have garnered increasing interest. [PERSON] et al. [78] developed an artificial in vitro multienzyme cascade biocatalytic system and conducted an analysis of PPK/PP and PK/PEP regeneration modules [79]. The findings demonstrated that the PPK/PP module may not be an optimal choice for ATP regeneration in the biocatalytic NMN production system. The system utilizing PPK/PP exhibited extremely low NMN yields, which were attributed to the potent inhibitory effect of PP on ribose-phosphate diphosphokinase (PRS), hindering the conversion of ribose 5-phosphate (R5P) into the crucial substrate PRPP. However, engineering a PRS variant insensitive to PP inhibition could facilitate the introduction of cost-effective PP or pyrophosphate (PPi) into the biological system for ATP regeneration, thereby reducing costs. In the NAMPT-catalyzed NMN synthesis pathway, the substrate PRPP is converted into the by-product PPi, which promotes the hydrolysis of ATP and significantly enhances the substrate affinity of NAMPT, resulting in an 1100-fold increase in the catalytic efficiency of NMN synthesis [80]. Moreover, the addition of pyrophosphatase (PPase) to degrade PPi into phosphate can increase the yield of NMN by approximately 50%. This enhancement may be attributed to the promotion of the reaction process through the degradation of the by-product [76].