esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on the measurement of slug bubble liquid film thickness by a PCB liquid film sensor, a new correlation for predicting liquid film thickness was proposed. In addition, the existing pressure drop correlations based on separation models were summarized and evaluated using the new data. [PERSON] et al. [44] conducted slug flow pressure experiments using air and water on a flow circuit with a diameter of one inch and a length of twenty-five feet. The pressure sensors were arranged along the pipeline direction, and the pressure response data generated by statistical analysis and regression analysis were used to characterize the slug flow. They found that the pressure drop decreased with the increase in gas flow rate. To the best of the authors' knowledge, the pressure changes in slug flow formed by liquid accumulation movement in hilly terrain pipelines under ZNLF have not been studied. In addition, the slug pressure at different moments of the slugging pipe segment has not been analyzed. Furthermore, there was no comprehensive monitoring of liquid accumulation from movement to discharge. This paper aims at filling these gaps by invoking experiments and the establishment of a slug flow model to analyze the pressure changes at slug flow that occurs in a hilly terrain pipeline under ZNLF. First, the four-stage pressure model of slug movement in the upward inclined pipe is established, and the model validity is verified. Second, by comparing the pressure difference at different times, it is concluded that its change is related to the liquid slug outflow in the pipe. Finally, the moving visual image of the slug in the pipe from entering the inclined pipe to discharging is captured. This paper is organized as follows: Section 2 presents the test rig and the procedure of the experiment. In Section 3, the slug flow pressure model built into this study is described. In Section 4, the results and discussion of the analyzed data are conveyed. Finally, the conclusions and expectations are provided in Section 5.
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods
### Hierarchical ProcessTransformer  
In this paper, a Hierarchical Process Transformer (HiP-Transformer) model is proposed for predictive business process monitoring tasks. In contrast to existing approaches that apply the Transformer model directly to business process instance data, this approach decomposes the predictive business process monitoring task at three levels and designs a specific Transformer model at each level. Firstly, to better model the activity, time, and various attribute information contained in events, the Transformer model at the first (bottom) level is designed to obtain a fine-grained event encoding representation using an event-level self-attention mechanism. The second (middle) layer of the Transformer model is designed to encode the event subsequence. The third (top) layer of the Transformer model is designed to globally encode the whole business process instance based on the multiple event subsequences. The three levels of the Transformer model can effectively fuse multiple granularities of information such as event attributes, event sequences, and complete cases to achieve a combined encoded representation of the business process case. It finally achieves accurate prediction of the next activity, remaining time, and other key states of the business process case by designing a task-related prediction network.  
The framework of the HiP-Transformer model is shown in Figure 1. The three layers of the HiP-Transformer model are described in detail at what follows.
## 5 Test Results and Discussion
### The Behavior of Total and Permanent Displacements after Traffic Loading  
It is known that the total deformation occurring under repeated traffic loads for the road fill layer is equal to the sum of permanent deformation and elastic deformation. In road design, permanent deformation is considered one of the most important parameters for the stability of the road over long periods of time. Figure 8a shows the elastic, permanent and total displacements that occurred after repeated traffic loads as a representation [36]. In Figure 8b, the percentage deformation ratio (s/D) change is given for the number of plate loading cycles from 1 to 5 (N\({}_{\text{PLT}}\) = 1-5) in the granular filled section in test series II (N\({}_{\text{traffic}}\) = 5000). The percentage deformation ratio (s/D) is determined as the displacement (s) obtained after each load stage to the loading plate diameter (D). It is seen that the increase in the percentage deformation rate gradually decreases for the number of plate loading cycles from 1 to 5 (N\({}_{\text{PLT}}\)). This percentage deformation change is expected to asymptote in progressive cyclic loads.  
Figure 7: The deformation change depending on the number of traffic passes in the test series.  
The total and permanent displacement changes that occurred due to repeated plate loading tests for sections reinforced with geosynthetics and stabilized with rock fill in test series II are compared in Figure 9. The best performance for total and permanent displacements was obtained in sections stabilized with rock fill and reinforced with geocell. It is considered that the section reinforced with geocell performs better than the section reinforced with geogrid because geocell provides better lateral confinement of granular material, especially after traffic loads [37].
# Facile Preparation of Multilayered Graphene with CO\({}_{2}\) as a Carbon Source
## 4 Conclusions  
In conclusion, we presented a facile and controllable approach to prepare multilayered graphene with CO\({}_{2}\) as a carbon source. It is a facile and controllable route for fabricating multilayered graphene, which demonstrates that greenhouse gas can be utilized as a source of carbon for fabricating multilayered graphene. The optimum condition for the production of multilayered graphene in this study was obtained by investigating the effect of the velocity of CO\({}_{2}\), reaction temperature, reaction time, and sample post-processing conditions on the reaction of metal Mg (Mg ribbon and Mg powder) with CO\({}_{2}\). The results show that with the increasing of velocity of CO\({}_{2}\), the yield of multilayered graphene first increased and then remained at a certain level. Owing to the sphere-like structure, Mg powder is easier to react with CO\({}_{2}\), and has a higher yield compared with Mg ribbon in the above conditions. Simultaneously, below 600 \({}^{\circ}\)C, the reaction of metal Mg with CO\({}_{2}\) did not take place; from 600 to 700 \({}^{\circ}\)C, the reaction of metal Mg with CO\({}_{2}\) did take place, and the yield increased obviously due to the fact that the rising temperature can improve the reaction activity; over 700 \({}^{\circ}\)C, the yield did not increase, but slightly decreased for the reaction of CO\({}_{2}\) with C, while the metal Mg even evaporated at a temperature of 1107 \({}^{\circ}\)C. Meanwhile, with the increase of the reaction time, the yield of multilayered graphene first increased and then remained at a certain level. Finally, the optimum solution of 1.0 M HCl was obtained in this study for sample post-processing. Based on the above study, the yield rate of multilayered graphene almost reached 94.2%. Therefore, this research provides a new method for a controllable way to produce multilayered graphene with gaseous CO\({}_{2}\) as a carbon source, and we believe that this work may provide a new proof-of-principle that can be applied in large-scale preparation of multilayered graphene.  
**Author Contributions:** Validation, L.Z.; Writing--original draft, H.D.; Writing--review & editing, S.G.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by Science and Technology Major Project of Shanxi (20181101018) and Key R & D Project of Shanxi Province (201703D111008).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Influence of Construction Sequence
### Influence on Internal Support Axial Force  
Figure 9 shows the maximum axis force of internal support in each foundation pit under different construction sequences. It can be seen from the figure that the construction sequence of the foundation pit group has the greatest impact on the maximum axial force in Pit A. In Scenario 1, the maximum axial force in Pit A is 13,350 kN, and the minimum is 7045 kN in Scenario 6, with a difference of 89.5%. Therefore, the excavation of Pit B and Pit C has the most obvious impact on the maximum axial force in Pit A, while the excavation of Pit D, Pit E, and Pit F has little impact.  
In Scenarios 3, 4, and 6, the maximum axial force in Pit B is almost the same, which is greater than that in Scenarios 1, 2, and 5. This shows that the excavation of Pit A before Pit B will cause the maximum axial force in Pit B to increase, while the excavation of Pit D, Pit E, and Pit F will have negligible impact. In Scenarios 2, 5, and 6, the maximum axial force in Pit C is almost the same, which is less than that in Scenarios 1, 3, and 4, indicating that the excavation of Pit D, Pit E, and Pit F increases the maximum axial force in Pit C. However, the impact of Pit A excavation can be ignored.  
The maximum axial force of support in Pit D and Pit E is almost the same in Scenarios 2, 5, and 6, and almost the same in Scenarios 1, 3, and 4, as well. The former is greater than the latter, which means that the excavation of Pit B and Pit C will increase the maximum axial force in Pit D and Pit E, while the impact of the excavation of Pit A can be ignored. The maximum axial force in Pit F is about 1260 kN in Scenarios 1 and 3 and about 3700 kN in Scenarios 5 and 6, with a difference of 193.6%. Therefore, the maximum axial force in Pit F is greatly affected by the construction sequence. The earlier Pit F is excavated, the more conducive it will be to controlling the maximum axial force.  
In general, the maximum axial force of support in Pit A is more affected by the excavation of Pit B and Pit C. The maximum axial force in Pit B is mainly affected by the excavation of Pit A. The maximum axial force in Pit C is mainly affected by the excavation of Pit D and Pit E. The maximum axial force in Pit D and E is mainly affected by the excavation of Pit B and Pit C. In particular, the maximum axial force in Pit F is mainly affected by the excavation of Pit A, Pit B, and Pit C. From the perspective of reducing the axial force of support, the construction sequence of the foundation pit group should focus on reducing the axial force in Pit A. Therefore, Scenarios 3, 4, and 6 are optional for foundation pit group construction.
# Road Infrastructure Investment Limits Based on Minimal Accidents Using Artificial Neural Network
## 3 Results and Discussion
### Relative Influence of Predicting Variables  
The relative importance and ranking of the predicting variables of the ANN model were estimated according to Equations (7) and (8) using the data in Table 4. Similarly, the relative influence for the regression model predictors was also determined. These relative importance results for the predicting variables in the ARA model are presented in Table 5. The results obtained using the relative weight analysis for the regression showed that CNVR, ERMI per Capita, and Proportion of Work Group contributed to explaining R\({}^{2}\) the least in the ARA. However, these variables were found to have a certain level of relevance according to the weight contribution analysis of the ANN model. This can be associated with the different structure, accuracy, theory, method of relative importance estimation, etc., of the models. This also highlights how almost all the predicting variables included in the ANN model played some role in the model performance. Although the relative importance does not represent the level of statistical significance, it certainly can reveal high-priority variables. The relative influence results of the variables were utilized to rank them, as presented in the last column of Table 5. The #1 rank represents the variable with the highest relevance, while the rank of #11 represents the variable with the least relative influence. The overall ranking was obtained based on averages of relative importance of each variable across the different methods. This overall ranking was necessary because the various models and relative importance estimation methods did not yield similar results. Variation in the results was anticipated, but since [PERSON]'s method is still widely utilized and [PERSON]'s method was supported with compelling evidence, both results were included. The relative weight analysis of the regression has strong fundamental theoretical bases but is still affected by all the limitations of multiple regression [46]. As such, the average or the overall ranking is considered to be much closer to the fact. Overall, the variables in decreasing order of influence on ARA are total NRII (#1), population of work group (#2), total ERMI (#3), freight movement (#4), NRII/capita (#5), population (#6), ERMI/capita (#7), passengers (#8), GDP (#9), proportion of work group (#10), and CNRV (#11). The set of road infrastructure investment variables can be seen to be in the top levels with regard to the influence on ARA. Being the lowest in the ranking does imply insignificance to the dependent variable. It only means lower relative linear, nonlinear, or both influence the dependent variable with respect to the other variables.
# Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines
###### Abstract  
In this paper, a high-power amplifier integrated circuit (IC) in gallium-nitride (GaN) on silicon (Si) technology is presented at a W-band (75-110 GHz). In order to mitigate the losses caused by relatively high loss tangent of Si substrate compared to silicon carbide (SiC), low-impedance microstrip lines (20-30 \(\Omega\)) are adopted in the impedance matching networks. They allow for the impedance transformation between 50 \(\Omega\) and very low impedances of the wide-gate transistors used for high power generation. Each stage is matched to produce enough power to drive the next stage. A Lange coupler is employed to combine two three-stage common source amplifiers, providing high output power and good input/output return loss. The designed power amplifier IC was fabricated in the commercially available 60 nm GaN-on-Si high electron mobility transistor (HEMT) foundry. From on-wafer probe measurements, it exhibits the output power higher than 26.5 dBm and power added efficiency (PAE) higher than 8.5% from 88 to 93 GHz with a large-signal gain >10.5 dB. Peak output power is measured to be 28.9 dBm with a PAE of 13.3% and a gain of 9.9 dB at 90 GHz, which corresponds to the power density of 1.94 W/mm. To the best of the authors' knowledge, this result belongs to the highest output power and power density among the reported power amplifier ICs in GaN-on-Si HEMT technologies operating at the W-band.  
HEMT; GaN-on-Si; IC; millimeter-wave; power amplifier; W-band Article Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines  
[PERSON], [PERSON], [PERSON], [PERSON], W. Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines. _Appl. Sci._**2021**, _11_, 0017. [[https://doi.org/10.3390/app1199017](https://doi.org/10.3390/app1199017)]([https://doi.org/10.3390/app1199017](https://doi.org/10.3390/app1199017))
## 3 Proposed MPPT Control Method
### Analyzing the Thermoelectric Power Generation System
We approximate these equations as follows  
\[v_{T}\big{[}\big{(}D_{1}+D_{2}\big{)}T_{s}\big{]}-v_{T}\big{(}D_{1}T_{s}\big{)} =-D_{2}T_{s}\,\frac{v_{T}\big{(}0\big{)}}{R_{M}C_{I}}+D_{2}T_{s}\,\frac{v_{M} \big{(}0\big{)}}{R_{M}C_{I}}-D_{2}T_{s}\,\frac{i_{T}\big{(}0\big{)}}{C_{I}} \tag{17}\]  
\[i_{T}\big{[}\big{(}D_{1}+D_{2}\big{)}T_{s}\big{]}-i_{T}\big{(}D_{1}T_{s}\big{)} =D_{2}T_{s}\,\frac{v_{T}\big{(}0\big{)}}{L}-D_{2}T_{s}\,\frac{r_{L}+r_{s}}{L} \,i_{T}\big{(}0\big{)}-D_{2}T_{s}\,\frac{v_{O}\big{(}0\big{)}}{L} \tag{18}\]  
\[v_{O}\big{[}\big{(}D_{1}+D_{2}\big{)}T_{s}\big{]}-v_{O}\big{(}D_{1}T_{s}\big{)} =-D_{2}T_{s}\,\frac{i_{T}(0)}{C_{O}}+D_{2}T_{s}\,\frac{v_{O}\big{(}0\big{)}}{R _{O}C_{O}} \tag{19}\]  
During the MODE 3, \(i_{L}=0\). Figure 5c illustrates the equivalent circuit in MODE 3. From Figure 5c, the state equation of the MODE 3 is given as follows  
\[\frac{dv_{T}}{dt}=-\frac{v_{T}}{R_{M}C_{I}}+\frac{v_{M}}{R_{M}C_{I}} \tag{20}\]  
\[\frac{di_{T}}{dt}=0 \tag{21}\]  
\[\frac{dv_{O}}{dt}=\frac{v_{O}}{R_{O}C_{O}} \tag{22}\]  
From these equations, the matrix is given by  
\[\frac{d\textbf{x}}{dt}=\textbf{A}_{3}\textbf{x}+\textbf{b}_{3}V_{M} \tag{23}\]  
where \(\textbf{A_{3}}\) and \(\textbf{b}_{3}\) are given by  
_Appl. Sci._**2013**, \(3\)  
\[\mathbf{b}=\mathbf{b}_{1}D_{1}+\mathbf{b}_{2}D_{2}+\mathbf{b}_{3}D_ {3}\] \[= \left( \begin{array}{c}\dfrac{1}{R_{M}C_{t}}\\ 0\\ 0\\ \end{array} \right)\]  
where \(D_{12}=D_{1}+D_{2}\). Equation (31) corresponds with Equations (27), (28) and (29). Therefore, the average value is derived from the parameters multiplied by the duty factors. The following equation can be written in the steady state  
\[\dfrac{d\mathbf{\overline{x}}}{dt}=0 \tag{35}\]  
Therefore \(V_{T}\), \(I_{L}\) and \(V_{O}\) in the steady state can be derived as  
\[\left( \begin{array}{c}V_{T}\\ I_{L}\\ V_{O}\end{array} \right)=\dfrac{V_{M}}{R_{O}D_{2}^{2}+R_{M}D_{12}^{2}+r_{L}D_{1 2}+r_{S}D_{1}+r_{D}D_{2}}\cdot\left( \begin{array}{c}R_{O}D_{2}^{2}+r_{L}D_{ 12}+r_{S}D_{1}+r_{D}D_{2}\\ D_{12}\\ R_{O}D_{2}D_{12}\end{array} \right) \tag{36}\]  
The average value \(I_{L}\) of \(i_{L}\) is given as follows  
\[I_{L}=\dfrac{D_{12}}{2}I_{LP} \tag{37}\]  
where \(I_{LP}\) is the peak value of \(i_{L}\)[6]. The internal voltage \(V_{M}\) of the TM in the steady state is given by  
\[V_{M}=\dfrac{I_{L}}{D_{12}}\left(R_{O}D_{2}^{2}+R_{M}D_{12}^{2}+r_{L}D_{12}+r_ {S}D_{1}+r_{D}D_{2}\right) \tag{38}\]  
The output voltage \(V_{T}\) of the TM can be derived as  
\[V_{T}=\dfrac{I_{L}}{D_{12}}\left(R_{O}D_{2}^{2}+r_{L}D_{12}+r_{S}D_{1}+r_{D}D_ {2}\right) \tag{39}\]  
The output current \(I_{T}\) of the TM can be expressed as  
\[I_{T}=\dfrac{V_{M}-V_{T}}{R_{M}} \tag{40}\]  
Therefore \(V_{M}\), \(V_{T}\) and \(I_{T}\) can be estimated by detecting the inductor current \(i_{L}\) and the duty ratio.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background
#### 2.1.2 Blockchain Technology  
The development of blockchain technology in the healthcare domain focuses on applying blockchain strategies to ensure EHRs privacy and security in various healthcare applications. Blockchain technology was initially used in Bitcoin and was created for the cryptocurrency Bitcoin by [PERSON] in 2008 [4]. Blockchain technology offers an opportunity for recording and sharing data in a decentralized network. Blockchain can provide trust in peer-to-peer networks. The core elements of blockchain technology are a distributed ledger, consensus mechanisms and public-key encryption [2]. Any block in the blockchain consists of at least one transaction, a block validator signature and a reference of block headers to the previous block. Blockchain data structures facilitate the generation and sharing of different transactions between nodes in a peer-to-peer network inside a digital ledger. Blockchain technology allows participants to store and exchange sensitive data in real-time without exposing communication networks to malicious intent, forgery, or theft. Each transaction in the blockchain is associated with a hash, which is used to create the binary Merkle tree. The timestamp and identifier of the preceding block are stored in the block header along with the binary tree. The term \"chain\" refers to the blocks that are linked in chronological order and serve as a record of the ledger's state changes [14]. As a result, if a hacker tries to change the records in the blockchain, he must change not only the hash of that particular block but also the hash of any subsequent block, which is virtually impossible to do [15].
# Enhancing Healthcare Security: A Unified RBAC and ABAC Risk-Aware Access Control Approach
## 4 Implementation of Proposed Model in Healthcare Scenario
### Attribute-Based Access Control Implementation
#### 4.4.1 Policy Enforcement Point (PEP)  
The PEP acts as the entry point of the entire evaluation process to intercept the incoming request and forward it to the PDP [38]. The system is enhanced by adding extra logic to the AOP layer from the RBAC to replace the PEP, as they both serve a common purpose. Once the user successfully completes the identity validation process, the AOP will forward the request to the PDP to await a final decision instead of immediately granting access.  
To implement the PEP, we integrate additional logic into the AOP layer from the RBAC to substitute the PEP to intercept and forward user requests to the PDP. This was achieved by invoking the PDP to forward the request and await a conclusive decision (true or false).
## 3 Results
### Departmental Decision-Making Module
#### 3.1.5 Summary of Class-Wise Performance Across Departments  
To provide a concise and visual overview of the departmental modules' performance, we generated a class-wise heatmap of F1-scores. For this visualization, the original classification labels used by each expert were consolidated into three unified categories: OK/SOLD, OTHER, and SCRAP. The OK/SOLD category includes labels such as OK in technical departments and SOLD in the commercial module, representing products deemed acceptable. The SCRAP category corresponds to defective materials identified for rejection. The intermediate group, OTHER, aggregates all other possible labels used by the experts, such as BLOCK or ALARM, which generally reflect borderline or uncertain cases that do not clearly belong to the acceptable or defective classes. This regrouping allows for consistent cross-departmental comparison. The heatmap shown in Figure 11 summarizes the classification results of the four departmental modules, capturing both predictive accuracy and potential difficulties in replicating expert decisions.  
This heatmap highlights several important trends. As expected, the OK/SOLD class consistently achieves the highest F1-scores across all departments, with the sales module reaching 96.01%. Conversely, the OTHER class presents greater classification challenges, with the lowest score observed in the sales module (63.68%). The SCRAP class, although more critical due to its implication in quality loss, also shows strong performance in the rolling mill module (80.05%). These results support the conclusion that the system is particularly effective in identifying clear-cut cases, such as acceptable or scrap-quality material, while additional refinement may be needed for ambiguous or intermediate classifications. This visualization serves as a synthesis of the detailed metrics presented earlier and confirms the consistency and reliability of the departmental decision models.