esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time
## 4 Results and Discussion
### Field Applications Using the Novel Model \(\text{TI}_{\text{m}}\)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Effect of Using the Novel TI\({}_{\text{m}}\) Model on the Performance of Well-A**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **\(\mathbf{\mathcal{M}}\)** \\ **(Output)** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Items** \\ **(Output)** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Minimum** \\ **\end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Maximum** \\ **Maximum** \\ \end{tabular} } & \multirow{2}{*}{**\
\begin{tabular}{c} **Average** \\ **(X6000)** \\ \end{tabular} } & \multirow{2}{*}{**
\begin{tabular}{c} **Statement** \\ **(X6000)** \\ \end{tabular} } \\ \hline
1 & TI\({}_{\text{m}}\) & 1.4 & 4 & 2.4 & Effective borehole cleaning \\
2 & ROP & 120 & 280 & 209 &
\begin{tabular}{c} Optimized ROP by 56\% due to \\ the effective borehole cleaning \\ \end{tabular} \\
3 & TRQ & 5 & 18 & 9.6 & \begin{tabular}{c} Decreased TRQ by 44\% due to \\ the effective borehole cleaning \\ \end{tabular} \\ \hline \hline \multicolumn{6}{c}{**Effect of Using the Novel TI\({}_{\text{m}}\) Model on the Performance of Well-B**} \\ \hline \multirow{2}{*}{\begin{tabular}{c} **\(\mathbf{\mathcal{M}}\)** \\ **(output)** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Items** \\ **(output)** \\ \end{tabular} } & \multirow{2}{*}{**\begin{tabular}{c} **Minimum** \\ **Maximum** \\ \end{tabular} } & \multirow{2}{*}{**\
\begin{tabular}{c} **Maximum** \\ **Average** \\ \end{tabular} } & \multirow{2}{*}{**
\begin{tabular}{c} **Average** \\ **Statement** \\ \end{tabular} } \\ \hline
1 & TI\({}_{\text{m}}\) & 0.47 & 1.4 & 0.79 &
\begin{tabular}{c} Insufficient borehole cleaning \\ Lower ROP due to \\ insufficient borehole cleaning \\ Higher TRQ due to the \\ insufficient borehole cleaning \\ and cutting accumulation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effect of using the novel TI\({}_{\text{m}}\) model on well performance. As seen in Table 8, the results indicate that the average value of the \(\text{TI}_{\text{m}}\) model was 0.56, which suggests that the well was drilled with poor borehole cleaning due to the accumulation of cuttings. This resulted in an average ROP of 93.3 ft/h, which represents a significant reduction in drilling efficiency and effectiveness. Additionally, the average torque value in Well C was 12.5 klbs-ft, indicating a stuck pipe incident. This highlights the potential consequences of poor borehole cleaning and the importance of utilizing the \(\text{TI}_{\text{m}}\) model to optimize drilling performance and prevent issues such as stuck pipes.
# Comparison Study on Coarseness Index and Maximum Diameter of Rock Fragments by Linear Cutting Tests
## 2 Experimental Setup and Procedures  
### Linear Cutting Test  
#### 2.1.1 Linear Cutting Machine  
Linear cutting tests were conducted using a linear cutting machine (LCM), shown in Figure 2, which was divided into a cutting system and a control system.  
The control system is driven by servomotors in the x-, y-, and z-directions. The servomotors control the moving distance more precisely than hydraulic cylinders. The x- and y-direction servomotors adjust the cutting spacing and cutting length by moving the rock in the bucket, respectively, and have a speed limit of 100 mm/s. The z-direction servomotor moves the cutting tool to adjust the penetration depth.  
The reaction forces generated during cutting were measured by the installed load cells in three axes up to 50 Hz (Figure 2b). The installed 25-ton load cell measured the cutting (\(F_{c}\)) and normal (\(F_{n}\)) forces in the y- and z-directions, respectively. Moreover, a load cell with a 15-ton capacity was installed in the z-direction to measure the side force (\(F_{s}\)).  
The control system consisted of a computer and accessories, and was used to set the cutting conditions (spacing and penetration depth), cutting speed, and measurement period. In addition, data measured could be verified in real time using the display.
## 3 Materials and Methods
### Position Algorithm Calculation
#### 3.1.4 Step 4: Weight Calculation  
There are obvious errors caused by various reasons in the process of positioning measurement or position estimation. The distance error calculated by the ranging model and the positioning error of the positioning result will result in low positioning accuracy, affecting the performance of the localization algorithm. Therefore, after the vehicle position is preliminarily estimated, the obviously abnormal coordinate data need to be corrected to reduce the error.  
When the vehicle receives signals from three or more base stations, a weighted calculation is designed for the coordinates of trilateral positioning, least-squares positioning, and centroid positioning. Here, \((x_{c},y_{c})\), \((x_{d},y_{d})\), and \((x_{e},y_{e})\) are the location estimation coordinates of the trilateral location, the least-squares location, and the centroid location, respectively. Considering the distance error \(d\) and the positioning error of coordinate \((x,y)\), the weighting factor of each positioning result is \(w_{i}\).  
\[W_{i}=\frac{1}{\sqrt{\Delta x_{i}^{2}+\Delta y_{i}^{2}+d_{i}^{2}}}\ i=1,\ 2, \ldots,n \tag{18}\]  
where \(i\) indicates the positioning coordinate serial number; \(\Delta x_{i}\) indicates the \(X\)-axis error of the \(i\)th coordinate, unit: m; \(\Delta y_{i}\) indicates the \(Y\)-axis error of the \(i\)th coordinate, unit: m; and \(d_{i}\) indicates the \(i\)th coordinate distance error, unit: m.
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability
### Peptide Microtubes  
In the last decade, there has been an increased focus on biomaterial fabrication, of which peptide micro and nanotubes provide a good example. These ordered structures origin from a dipeptide, usually L-diphenylalanine (FF), by self-assembling. Six FF units form cyclic hexamers, which can further be stacked to produce narrow channels with a diameter of approximately 10 A. Various peptides can be used as building blocks, thereby these vehicles can be tailored according to the desired application [82]. In a study by [PERSON] et al., EA was encapsulated by the self-assembling of the synthetic precursor bolaamphiphile bis (N-amido threonine)-1, 5-pentane dicarboxylate, with a pentyl chain between the head groups of the two aminoacids, according to the following procedure [83]. First, the bolaamphiile solution was allowed to assemble for 2-3 weeks with a higher yield at pH 6, then EA was added at a concentration of 0.06 mM, which maximizes EE, as at elevated concentrations EA forms discotic liquid crystal assemblies upon aggregation, while lower EA amounts efficiently insert into the nanotubes by capillarity. The self-assembled EA-loaded nanostructure had an average diameter ranging from 0.5 to 1 \(\upmu\)m with an EE of about 80%. EA release was affected by pH, as expected, with about 50% of loaded acid released within 2 h at pH 6 and 7. EA antibacterial property was assessed in vitro against Gram-positive S. aureus and Gram-negative E. coli, and in both cases EA-loaded microtubes were significantly more efficacious than free EA. That was an unexpected result, since EAalone did not exert any action against S. aureus, but probably the explanation relies on the antibacterial activity of threonine itself.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Composite Structures  
Research on carbon fibre composites by [PERSON] et al. [96] examined the effect of material properties (ply number, fibre orientation, material thickness) on the propagation of the acoustic waves. These variations were characterised as changes in amplitude and waveform through AE descriptors, waveform and wavelet analysis. [PERSON] et al. [97] tested a carbon fibre panel with an artificial crack to fatigue loading and processed the AE signals using neural network technique, thus allowing a good understanding of the structural mechanism. [PERSON] et al. [98] examined the effect of the acoustic signal by either embedding sensors on fibre composite structures or mounting them on the surface. It was demonstrated that embedded sensors have good potential for SHM of composite structures.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
###### Abstract  
When extending the life of Wireless Reschargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes' power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values.  
charging algorithm; no knowledge charging; wireless recharging sensor network; wireless power transfer; unmanned aerial vehicle +
Footnote †: journal: Sensors
## 1 Introduction
The agencies and official organizations in Europe that are involved in pesticide control, such as ECHA, European Food Safety Authority (EFSA), Joint Research Centre (JCR), European Centre for Ecotoxicology and Toxicology of Chemicals (ECTOC), Organisation for Economic Co-operation and Development (OECD) have promoted QSAR as an important tool in the pesticide risk assessment process. These organizations are responsible for the systematization and harmonization of computation tools, as well as the standardization of QSAR model validation [54]. The OECD announced guidelines that a valid QSAR model should have: a defined endpoint; an unambiguous algorithm; a defined domain of applicability; appropriate measures of goodness of fit, robustness, and predictivity; and a mechanistic interpretation [55]. CAMD has been generally accepted and extensively applied in ecotoxicological modeling and design of agrochemicals. This is due to its high efficiency in the design of novel compounds, saving both time and economic costs in large-scale experimental synthesis and biological tests. According to the REACH (Registration, Evaluation, and Authorization of Chemicals) guidelines of the European Chemicals Agency (ECHA) from 2011, animal tests can be avoided if the hazardous properties of a substance can be predicted using computer models of the QSAR approach [56]. Molecular docking is a valuable tool for drug discovery, but it has also been successfully used in the discovery of novel plant protection agents. The molecular docking technique reveals the mechanism of action of a potential drug or pesticide at the atomic level. This method allows insight into the interactions between a small molecule (ligand) and the binding site of target proteins (receptors) related to biological activity. Molecular docking involves the prediction of the ligand orientation within the binding site of the protein, as well as the evaluation of binding affinity between the receptor and the ligand by the scoring function (binding energy) [57; 58]. The molecular dynamic simulation study provides more insights into ligand-receptor complex dynamics and structural properties. It evaluates the docked complex's thermal and structural stability [59]. This paper reviews a recent advance in research into the biological activities of coumarins related to plant protection. It also discusses the use of CAMD techniques for the development of new pesticides.
# Energy-Optimized 3D Path Planning for Unmanned Aerial Vehicles
## 1 Introduction
### Brief Description of the Aims  
As mentioned above, limited energy availability is one of the weak points of drone operation. This notion led to the development of the proposed trajectory planning method, which uses energy as effectively as possible, thus optimizing consumption during the flight. It requires the creation of a drone \"energy map\", where the components consuming most energy are determined, so that the trajectory planning mechanism can be adjusted accordingly. Weather reports, especially wind reports of the terrain, must be available during the drone's path planning.  
The energy map creation is followed by 3D environment configuration, since this is how the drone interprets its environment. With the partitioning of the \"work space\" (WS), the following areas are created: \"free spaces\", \"occupied areas\", and \"semi -occupied/-free spaces\". The occupied areas contain obstacles, while the semi-occupied ones partly contain them. Depending on the partitioning method used and based on specific resolution of partitioning, the semi-occupied areas can be further partitioned. Once the environment has been partitioned, the modelling can begin. Through the modelling, the drone represented as a point can move in the configured WS. Obstacles, the dimensions of which are increased by the radius (\(R\)) of the sphere drawn around the drone, are called configured obstacles (see Figure 1). In this way, the physical dimensions of the drone are considered in the WS model. Naturally, extra \"safety zones\" can be added based on user requirements.
# Analysis of Mobile Edge Computing for Vehicular Networks +
## 1 Introduction
Through rigorous simulation, the authors demonstrate the benefits of FC in vehicular networks and its ability to support a large number of vehicles in a dense network. An additional concept that has gained traction in vehicular networking is the use of named data networking (NDN). NDN is an extension of content-centric networking (CCN) and is based on the premise that the primary function of a network is data dissemination and retrieval. In [17] the proposed Navigo scheme separates content based on geographic location and is simulated with a music streaming application. With regard to task delegation, the geographic classification of data used by the authors could be applied to resource manager in an edge computing network. Given that the biggest factor in performance is often delay, the default resource for a region could be designated as the geographically closest resource. In this paper, we extend our work originally presented in [18] and provide a more in-depth analysis of the proposed architecture and its application to real-world test cases. Additional experiments are conducted with LTE wireless communications and a mobile node to better replicate the environment of vehicular networks. In our architecture, we assume that computational tasks can be delegated to any available resource throughout the network. This would include remote cloud servers, edge nodes, and the mobile devices themselves, where edge nodes are defined as computational resources distributed throughout the network. These could be in the form of computationally-enabled base stations, computationally-enabled RSUs, or small cloud servers. In this aspect, our technique is not unlike traditional edge computing in that we seek to utilize computational resources at the network edge and alleviate the load on remote resources. However, our framework differs from others in the way we separate requests based on their context and maintain a default resource for time-critical tasks. Traditional edge computing seeks to minimize delay and delegate tasks to the nearest resource that can accommodate the job. However, the priority of request can vary and depends solely on the application for which the request was made. For example, a task to process data collected from on-board sensors about overall long-term vehicle performance, would be classified differently than a task to process data collected on real-time traffic status. Creating a distinction between task types ensures availability of sufficient resources for time-critical jobs as they arise and allows delay-tolerant jobs to be completed as resources become available.
## 3 Results and Discussion
### Hydrographic Surveys of the Water Body of the Same Area and Different Depths
The survey time in very shallow waters can also be shortened by simply increasing the speed of the vessel. However, there is a risk that when using the hydrographic survey parameters above the allowable rate, the data recorded in this way will not be recognized by the client due to the exceeded total vertical and horizontal uncertainty or the failure to detect all underwater objects. Most hydrographic offices and commercial companies providing services in the field of survey works assume the maximum MBES swath angle of 120\({}^{\circ}\), and in some reservoirs, even 100\({}^{\circ}\) can be assumed [59]. Such an explicit limitation of the beamwidth, on the one hand, affects the efficiency of survey work, and on the other hand, minimizes measurement errors. Questions about the useful angular sector of multi-beam echosounder systems, the swath width, or the survey speed are significant in the case of special-purpose measurements, e.g., mapping the seabed for the purpose of safety of navigation and marine cartography.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 2 Background
### EDZL Schedulability Test
\[DBF_{EDZL}(\tau_{i},l)=\bigg{(}\bigg{\lfloor}\frac{l-p_{i}}{p_{i}}\bigg{\rfloor} +1\bigg{)}\cdot e_{i}+\max\bigg{(}0,l-\bigg{(}\bigg{\lfloor}\frac{l-p_{i}}{p_{ i}}\bigg{\rfloor}+1\bigg{)}\cdot p_{i}-(p_{i}-e_{i})\bigg{)} \tag{4}\]  
and  
\[DBF_{EDZL}^{\prime}(\tau_{i},l)=\bigg{\lfloor}\frac{1}{p_{i}}\bigg{\rfloor} \cdot e_{i}+\min(e_{i},\ l\mod p_{i}). \tag{5}\]  
**Theorem 2** (Theorem 2 in reference [20]). : _A task set \(\tau\) is schedulable under EDZL on an \(m\)-core platform if at least \(|\tau|-m\) tasks (\(\tau_{k}\in\tau\)) satisfy the following condition for all \(l_{k}\geq 0\):_  
\[\sum_{\tau_{i}\in\tau}\hat{I}_{EDZL}(\tau_{i},l_{k})+\sum_{m-1\text{ largest }\tau_{i}\in\tau}\{\hat{I}_{EDZL}^{\prime}(\tau_{i},l_{k})-\hat{I}_{EDZL}(\tau_{i },l_{k})\}<m\cdot(l_{k}+D_{k}-C_{k}) \tag{6}\]  
_where_  
\[\hat{I}_{EDZL}(\tau_{i},l)= \begin{cases}\min(DBF_{EDZL}(\tau_{i},l+p_{k}),l+p_ {k}-e_{k})&if\ \ \tau_{i}\
eq\tau_{k}\\ \min(DBF_{EDZL}(\tau_{i},l+p_{k})-e_{k},l)&if\ \ \tau_{i}=\tau_{k}\end{cases} \]  
_and_  
\[\hat{I}_{EDZL}^{\prime}(\tau_{i},l)= \begin{cases}\min(DBF_{EDZL}^{\prime}(\tau_ {i},l+p_{k}),l+p_{k}-e_{k})&if\ \ \tau_{i}\
eq\tau_{k}\\ \min(DBF_{EDZL}^{\prime}(\tau_{i},l+p_{k})-e_{k},l)&if\ \ \tau_{i}=\tau_{k}\end{cases} \]
## 3 Results
### CO\({}_{2}\) Adsorption Capacities  
The CO\({}_{2}\) adsorption of neat poly(S/DVB)HIPE and LbL amine-modified poly(S/DVB)HIPE were analyzed. Poly(S/DVB)HIPE was modified by amine solution of polyethylenimine (PEI) or tetraethylenepentamine (TEPA). CO\({}_{2}\) adsorption system was carried out using a mixture gas 15 vol% of CO\({}_{2}\) in N\({}_{2}\) with a pressure of 30 psi at room temperature. Figure 6a-c shows the CO\({}_{2}\) breakthrough curve of unmodified and amine-modified poly(S/DVB)HIPEs. Unmodified poly(S/DVB)HIPE at 100 vol% DVB has slightly higher capacity compared to (S/DVB 20:80) and (S/DVB 80:20) due to the resulting high surface area, so poly(S/DVB)HIPEs had higher physical adsorption. Compared to unmodified poly(S/DVB)HIPE, amine modified poly(S/DVB)HIPE illustrated the higher CO\({}_{2}\) adsorption rate due to the ability of chemical adsorption and physical adsorption, as both adsorptions lead to higher CO\({}_{2}\) adsorption capacity.  
Table 2 provides data on the CO\({}_{2}\) adsorption capacity of poly(S/DVB)HIPE between unmodified and amine-modified poly(S/DVB)HIPE. Significantly, CO\({}_{2}\) adsorption of S/DVB 0:100 modified by PEI had the highest adsorption capacity of 1.01 mmol/g. In addition, elemental analysis results in Table 2 illustrate the difference in the percent of amine by CHN analysis due to the effect of adhesive amine on different surface areas of polyHIPPE. After the secondary coating (consisting of a layer of polycations, which are TEPA or PEI) were applied, the element of CHN analysis were used to confirm the amount of carbon, hydrogen, and nitrogen of the modified poly(S/DVB)HIPEs. From Table 2, results indicate mass fraction percentage of amine by CHN elemental analysis with the different ratios of DVB and styrene. It is shown that moderate amounts, 0.52% and 0.59%, of nitrogen were observed in the 20:80 and 80:20 ratios of styrene and divinylbenzene, respectively. The highest mass fraction of nitrogen was observed in polyHIPPE with the 0:100 ratio, and this is because the highest abundancy of pores was coated with amine modification [27].  
Figure 5: Scanning electron micrographs of poly(S/DVB)HIPE: (**a**) unmodified S/DVB 80:20 (\(\times\)2000); (**b**) modified S/DVB 80:20 (\(\times\)2000).  
Several kinds of technologies have been employed to reduce the amount of CO2 in the atmosphere by CO2 adsorption. The CO2 adsorption performance of different materials were compared and outlined in Table 3. From the table, it was found that the modified polyHIPE with PEI by LbL technique gave a high CO2 adsorption capability at 1.01 \(\pm\) 0.27 mmol/g.  
Figure 6: Comparison of the CO2 breakthrough curve of (**a**) poly(S/DVB)HIPEs filled with different ratio of S/DVB, (**b**) modified poly(S/DVB)HIPEs with PEI and (**c**) modified poly(S/DVB) HIPEs with TEPA.