esa-sceva/satcom-chunk-collection huggingface dataset:
# Compositional Modeling of Dimethyl Ether-CO2 Mixed Solvent for Enhanced Oil Recovery
## 2 Materials and Methods
### Reservoir Modeling  
The GEM compositional simulator developed by CMG was used for numerical simulation. In this paper, the 2D homogenous cross-sectional reservoir model was designed to analyze the sweep efficiency change according to the DME mole fraction. The properties of the reservoir were obtained from the literature (Table 4) [31]. In oil-wet system, water flooding is not effective for enhancing oil recovery [35]. Therefore, the oil-wet system was chosen to show the improved performance of DME-CO\({}_{2}\) mixed solvent. The end point values and crossover saturation of relative permeability are measures of wettability [36]. The relative permeability obtained from the literature is shown in Figure 1[37]. The relative permeability of water is quite high at residual oil saturation and crossover saturation is less than 0.5, which are good qualitative indications of oil wet conditions.  
The size of the reservoir was designed as 52 \(\times\) 1 \(\times\) 20 grids and each grid block was 3 m \(\times\) 3 m \(\times\) 1.5 m. In this paper, WAG was applied to enhance the oil recovery. The total injection process was conducted for 12 years: 3 years for the secondary water flooding, 6 years for WAG (1:1.5 WAG ratio), and 3 years for the water flooding to produce the residual DME (Figure 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Injection Gas Contents** \\ \hline
1 & 100\% CO\({}_{2}\) + 0\% DME \\
2 & 90\% CO\({}_{2}\) + 10\% DME \\
3 & 85\% CO\({}_{2}\) + 15\% DME \\
4 & 75\% CO\({}_{2}\) + 25\% DME \\ \hline \hline \end{tabular}
\end{table}
Table 4: Classification of cases according to DME content.  
Figure 1: Relative permeability curve for the (**a**) water–oil and (**b**) liquid–gas systems.  
Figure 2: Schematic diagram of the water alternating gas (WAG) injection design.  
Four WAG cases depending on the mole fraction of DME (0% to 25%) were analyzed (Table 4).  
Other properties, including production pressure, were maintained to examine the effect of the injection fluid. Reservoir properties and the operation conditions are shown in Table 5.
## 3 Results and Discussion
### Assessment of Pearson Correlation  
The Pearson correlation matrix (Table 8) is used to assess whether trace elements may originate from common pollution sources. However, this correlation simply indicates simultaneous contamination, which could possibly result from a common source [64]. The matrix shows an average correlation between Fe and several elements Ni (0.74), Cu (0.79), Ga (0.66), Pb (0.55), and Ti (0.67) and has a strong correlation with V (0.85). The positive average correlation between these elements means that they originate from the same anthropogenic activities, such as agriculture, and are probably governed by the same physicochemical processes [65; 66]. Similarly, Ba is moderately correlated with the elements Fe (0.77), Ni (0.58), Cu (0.64), Zn (0.72), and Pb (0.53), and has a negative moderate correlation with Ti (\(-\)0.74).
## 2 Materials and Methods
### Kinetic Studies  
In this study, adsorption kinetic experiments were conducted under different time intervals ranging from 10 to 80 min. The pH was maintained at 8, while the amount of NGC used was 0.006 g, and the temperature remained constant throughout the experiments. The main objective of this investigation was to gain insights into the adsorption kinetics of MB on NGC by exploring four fundamental kinetic mechanisms. These mechanisms included the pseudo-first-order Equation (4), the pseudo-second-order Equation (5), the Elovich Equation (6), and the intraparticle diffusion Equation (7). By examining these mechanisms, a comprehensive understanding of the adsorption kinetics of MB on NGC could be achieved [34; 35; 36].  
\[log(q_{e}-q_{t})=logq_{e}-\left(\frac{k_{1}}{2.303}\right)t \tag{4}\]  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}\ q_{e}^{2}}+\frac{1}{q_{e}}(t) \tag{5}\]  
\[q_{t}=\frac{1}{\beta}ln\ (\alpha\beta)+\frac{1}{\beta}ln\ (t) \tag{6}\]\[q_{t}=K_{dif}t^{\frac{1}{2}}+B_{L} \tag{7}\]  
In the time frame denoted as \(t\), the rate constants for the pseudo-first-order and pseudo-second-order models are denoted as \(k_{1}\) (min\({}^{-1}\)) and \(k_{2}\) (g mg\({}^{-1}\) min\({}^{-1}\)), respectively. Furthermore, \(\alpha\) represents the initial adsorption rate (mg g\({}^{-1}\)), while \(\beta\) represents the desorption constant (g mg\({}^{-1}\)). Additionally, \(K_{dif}\) signifies the intraparticle diffusion rate constant (mg g\({}^{-1}\) min\({}^{-1/2}\)), and \(B_{L}\) refers to the boundary layer thickness.
# An Investigation on the Synthesis of Alkali Activated Materials from Thermally Modified Clays
## 3 Results and Discussion
### Precursor Characterization
#### 3.2.3 Influence of NaOH-to-Na\({}_{2}\)SiO\({}_{3}\) Ratio  
Figure 5 shows the AD and CS of the binders produced with various NaOH-to-Na\({}_{2}\)SiO\({}_{3}\) ratios by mass (SH/SS ratio), as per Table 1. The results showed that CS increased in all formulations with curing. AD showed the opposite trend but no noticeable differences were observed after 28 days of curing (\(\approx\)1.32 g/cm\({}^{3}\)). Differences in WA and OP were also found to be residual, particularly between HS1.00 and HS1.25 binders (Table 5). However, the SH/SS ratio showed a considerable influence on strength development. After 7 days of curing, HS1.00 and HS1.25 recorded similar CS (\(\approx\)10 MPa), whereas HS1.50 showed significantly lower values (7.1 \(\pm\) 1.2 MPa). At later ages, CS was maximized at the SH/SS ratio of 1.00 (14.3 \(\pm\) 1.6 MPa), with HS1.25 showing a CS similar to those of HS1.50 of 12.5 \(\pm\) 2.0 MPa and 12.5 \(\pm\) 1.0 MPa, respectively. These findings are in agreement with previous results reported by, among others, [PERSON] et al. [60], [PERSON] et al. [61], and [PERSON] et al. [62], who found increases in CS when reducing the SH/SS ratio. The authors also suggested the existence of the optimum SH/SS ratio below which further reductions will not deliver increases in strength. However, the existence of such a threshold limit cannot be inferred from the results discussed here. Increasing Na\({}_{2}\)SiO\({}_{3}\) promoted the formation of more Si-rich structures, which contributed to the development of the highest CS in HS1.0. In addition, reducing the SH/SS ratio also decreased the activating solution pH, leading to more progressive dissolution and polycondensation, but ultimately allowing for higher reaction degrees [20]. It is interesting to point out the differences in the effects of introducing soluble silicates in the activating solution rather than as secondary powdered precursors (Section 3.2.2). In previous works [17], the authors demonstrated that using soluble silicates (K\({}_{2}\)SiO\({}_{3}\)) resulted in more densified structures and enhanced the mechanical performances. Although these effects can be largely determined by the precursor's composition and granulometric distribution, and by the concentration and nature of the silicate solutions (Na\({}^{+}\) vs. K\({}^{+}\)), the results in Figure 5 recommend an SH/SS ratio of 1.00 to achieve the maximum CS values when synthesizing AAMs from CRC.  
It should be mentioned that silicate solutions entail significant production costs and environmental burdens [20; 21; 63] and restrictions in the Na\({}_{2}\)SiO\({}_{3}\) dosage should be considered in the following optimization studies, if technologically possible.
## 6 Conclusions  
In this paper, an IRS-enabled wireless system is designed for a 6G wireless communication system which exploits the passive reflective properties on IRS element to maximise the achievable rate. A highly non-convex rate maximisation problem under QoS guarantee and total power constraints is considered. The solution equivalent between the rate maximisation and corresponding power minimisation is demonstrated by solving the power minimisation problem optimally. A thorough analytical solution is described before the effectiveness of the proposed method is demonstrated via rigorous simulation work. Through detailed analysis of the results, it has been demonstrated that that the proposed method outperforms not only the benchmark methods such as random phase shift but also other well-known techniques such as SDR and MRT. The user movement is analysed at various AP-IRS distances, and through this, the optimal positioning of the IRS is also explored for most effective performance.  
The work can be further extended by incorporating challenging scenarios where extracting actual channel state information which is very difficult to obtain in real time. For example, optimal design for the vehicular network scenarios with fast-moving vehicles equipped with IRS can be considered. Also, the theoretical performance bound for the achievable rate in the considered scenario is still unknown. Analysing the theoretical bounds could provide useful insights for optimal design.  
Conceptualisation, A.J.E. and M.R.A.K.; methodology, R.A.; problem formulation: M.R.A.K. and A.J.E. simulation and validation: R.T.K., M.S. and F.T. results analysis: A.A.M., F.T. and A.J.E.,writing: all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.  
Figure 5: Transmit power at AP vs. AP-IRS distance (\(d_{0}\)) when user distance (\(d=30\)).
# Formation Kinetics and Morphology Characteristics of Natural Gas Hydrates in Sandstone Fractures
## 2 Methodology  
### Experimental Apparatus  
Figure 2 illustrates a self-assembled high-pressure visualization reaction system, encompassing the in situ hydrate formation system, the visualization monitoring system, and the data acquisition system.  
The in situ hydrate formation system comprises a visualization reaction vessel, a CH\({}_{4}\) gas cylinder, an SPVC plunger pump, a magnetic stirrer, a water bath temperature control system, sensors, and piping. The reaction vessel is custom-manufactured from 316 stainless steel and has a maximum pressure tolerance of 20 MPa. The SPVC plunger pump is connected to the CH\({}_{4}\) gas cylinder, precisely controlling the gas volume and pressure. Additionally, a pressure sensor is installed to continuously monitor the pressure within the vessel and its fluctuations. A back-pressure valve at the outlet facilitates the control of internal pressure through the venting of gas. A custom magnetic stirrer is  
Figure 2: A schematic diagram of the high-pressure visualization system.  
installed at the bottom of the kettle to create an agitated environment, with the stirring speed adjustable between 5 rpm and 450 rpm. The vessel is enclosed by a circulation jacket, with a DC-1006 water bath (using ethylene glycol as the coolant) to maintain precise temperature control within \(\pm\)0.05 \({}^{\circ}\)C, meeting the temperature requirements for CH\({}_{4}\) hydrate formation. A temperature sensor inside the kettle monitors real-time variations in internal temperature.  
The visualization monitoring system consists of sapphire windows, a high-definition camera, an HR lens, and a dehumidifier. The side wall of the reaction vessel is fitted with two 25 mm-diameter sapphire windows. Images are captured by an HR lens (Moritex Co., Ltd., Tokyo, Japan) with a focal length of 25 mm and a magnification range of 0.1-0.4\(\times\), which are then recorded by a high-definition camera (acA4024-8 gc GigE, Basler Co., Ltd., Ahrensburg, Germany). The camera is equipped with a 12.2-megapixel resolution sensor and operates at a frame rate of 8.7 frames per second, enabling precise automatic photography at the second level. To prevent condensation around the sapphire windows caused by the cooling liquid, which could result in blurred images, a dehumidifier is employed during the experiment to maintain the dryness of the windows.  
All experimental data, including pressure, temperature, and images, are transmitted in real time to a computer via a data acquisition system, facilitating subsequent data processing and analysis.
## 5 Results and Discussion
### End of Discharge (EOD) Prediction
#### 5.2.1 EOD Prediction (First Test)  
This test was carried out for a cell cycled at 24 \({}^{\circ}\)C using a 2 A discharge current until the voltage reached the EOD voltage of 2.7 V, corresponding to cell B0005 of the dataset. The EOD prediction was tested for two cycles of the dataset, one of the earliest (discharge cycle 5) and one of the latest (discharge cycle 160). The main reason for doing this was to show that the algorithms perform well when the EOD time is reduced due to the capacity fade with cycling. Figure 13 shows the results for the discharge cycle 5, following the same logic as for EOD: the measured voltage is the blue line, the filtered observations from the particle filter are the thick red line, and the EOD threshold is denoted by the green line. The real EOD time is evaluated when the measured voltage goes below the EOD voltage threshold. Once again, three predictions were done at three times while discharging. The state of the particles was propagated for each of them, obtaining the scaled probability density functions of the possible EOD times. The earliest prediction is denoted by the lightest blue PDF and the darker one depicts the most recent one. The prediction becomes more accurate as the EOD time approaches. The results are depicted in Figure 13.
## 2 Challenging Open Issues
### Other Open Issues
#### 2.2.3 Energy Consumption  
WBSNs designed for human use must be noninvasive and ambulatory. Thus, it must have fewer and smaller nodes, which also implies a smaller energy capacity. Because of this, one has to consider the trade off between the energy capacity and energy consumed by the processing and communication operations in order to use energy efficiently. The routing protocols in [12; 13; 14; 15] try to mitigate this issue.
## 1 Introduction  
Current protocols for calibrations of external beam radiotherapy are based on absorbed standards. These include the AAPM TG51 protocol implemented in North America, the DIN 6800-2 protocol in German-speaking countries and the Institute of Physics Code of Practice, the IPEM 1996 protocol in the UK, and NCS Report No. 18 in the Netherlands. Conversely, IAEA TRS-398, the international atomic energy dosimetry protocol, is used internationally [1, 2, 3, 4, 5].  
The benefits of having a protocol based on absorbed standards, which include reduced uncertainty, establish a harmonized system of primary standards, coupled with the use of a simple, straightforward formalism [2, 3]. Achieving accuracy in radiotherapy necessitates dose standardization, which reduces measurement discrepancies and ensures traceability. For these reasons, comparing dosimetry protocols increases traction in research, which hasbeen discussed extensively in the literature [6; 7; 8; 9]. There is always a need to report progress in dosimetry practices and compare uncertainties in absorbed dose measurements using commonly used protocols and codes of practices.  
Herein, we compared absorbed dose to water measured using the IAEA 398, DIN 6800-2, and TG-51 protocols in a concert of a plane-parallel and two cylindrical chambers to determine dose conversion coefficients between the three most common dosimetry protocols. Chamber-to-chamber variations and their influence in absorbed dose measurements were also investigated. The results were anticipated to facilitate the intercomparison of measurements performed using the three-dosimetry protocols.  
The three protocols' common feature is that they are all based on absorbed dose standards where the calibrated ionization chambers have absorbed dose to water calibration factors. However, the three protocols differ in defining the beam quality index used to determine the beam quality conversion coefficients required to convert the ionization chamber reading into absorbed dose to water. Also, there are some differences in the methods used to determine correction factors for influence quantities that affect ionization chamber readings. Our results reflected the influence of these differences on absorbed dose measurements.  
Herein, we aimed to compare the absorbed dose measurements made using three comprehensive dosimetry protocols and their associated uncertainties. The results may help in understanding discrepancies in measurement results made using the three protocols.
## 5 Performance Evaluation Metrics
### Coefficient of Determination (COD)  
Also known as R\({}^{2}\), COD indicates the fraction of the variance in the dependent variable that is predictable from the independent variables [51]. It is calculated by:  
\[COD=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{i=1}^{n} \left(y_{i}-\overline{y}\right)^{2}} \tag{9}\]
## 1 Introduction
The connections with leads and cables used in most current monitoring devices present obvious drawbacks in that they restrict the mobility of patients and, in addition, they may cause skin irritations or infections, and therefore contribute to deterioration of health conditions. Sensor networks thus represent a significant improvement compared to conventional sensors [6]. Despite the fact that a wireless connection is not a requirement for physiological parameter monitoring with implanted sensors, the issue mentioned above is one of the main motivations for this trend to use wireless technology in modern biomedical implanted systems [7]. Examples of physiological data collection platforms with wireless links include a wide range of biomedical applications. [PERSON] _et al_. [8], for example, have described a system for continuous wireless intracavitary pressure monitoring of the bladder, while other authors have assembled neural prosthetic devices [9, 10, 11]. [PERSON] _et al_. have described their vision [12] of a future when one device will be able to build a WSN with a large number of nodes, both inside and outside the human body that may be either predefined or random, according to the application. This vision can only be applied through the use of common communication protocols for wireless sensor networks. The standardized hardware and software architectures can support compatible devices, which are expected to significantly affect the next generation of healthcare systems. Some of these devices can then be incorporated into the wireless body area network (WBAN), providing new opportunities for technology to monitor health status [13]. This paper explores wearable and implantable body sensor network systems which provide rich contextual information and alerting mechanisms for abnormal conditions in continuous monitoring of patients at large. In addition, this paper evaluates the state of the art research activities and present issues that must be addressed to improve the quality of life through wearable and implantable body sensor networks. As well, we provide an overview of the recent trends on the future of intelligent applications for monitoring, not only from the wearable sensors perspective but also from the implantable sensor perspective. We also discuss the benefits that will be achieved and the outstanding research questions; in addition we introduce the open research problems that will be addressed with the development of sensor network based healthcare environment. The remainder of this review is organized as follows: Section 2 briefly overviews the related work on wearable and implantable body sensor networks. Section 3 surveys wireless sensor networks as one of the promising technological cornerstones of ambient intelligence. Next, Section 4 presents the extension of wireless sensor networks to medical applications to turn them into wireless body sensor networks. Section 5 addresses the challenges and open research problems for wireless body area networks and healthcare applications. Finally Section 6 draws the conclusions and future work.
# An Optical Interference Suppression Scheme for TCSPC Flash LiDAR Imagers
## 1 Introduction  
Light detection and ranging (LiDAR) is rapidly emerging as a key technology in automotive sensing, both for advanced driver assistance systems (ADAS) and as an enabler of fully autonomous driving [1]. The use of LiDAR as a complement to existing technologies, such as radar, ultrasonic range finding, thermal imaging, and image processing, is rapidly changing the landscape of advanced automotive sensor systems. The joint use of heterogeneous sensors, known as sensor fusion, promisesto facilitate the safe implementation of level 3 and level 4 autonomy [2; 3; 4], and to be an enabling factor of fully autonomous driving [5]. Even outside the automotive field, where the reliable and safe navigation of autonomous or semi-autonomous machines is needed (e.g., advanced robotics, automated guided vehicles, factory automation, simultaneous localization and mapping, and drones), LiDAR is proving itself to be a valid complement to existing navigation sensors [6; 7].  
It is expected that the use of LiDAR in navigation applications will rise steadily in the future [1]. The widespread adoption of LiDAR in uncontrolled, multi-user environments will soon bring the problem of mutual optical interference to the fore. This paper describes an interference suppression scheme specifically designed for flash LiDAR cameras operating in time-correlated single-photon counting (TCSPC) mode, but the same principles are applicable to a wide variety of pulsed LiDAR techniques. The suppression scheme, referred to as FLISS (Flash LiDAR Interference Suppression Scheme) in the remainder of this paper, allows a TCSPC flash LiDAR to operate in an environment shared with other LiDARs without suffering from optical interference. FLISS does not require any form of coordination between cameras and can in principle reject optical interference generated by any type of device.  
The paper will first present an overview of existing techniques for LiDAR interference management and relevant literature on the topic. A more formal description of interference in the context of flash LiDAR imagers will then be provided, followed by a presentation of the FLISS method. The paper will conclude with a section on experimental results and future directions.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Testing the Biochar Efficiency as Tartrazine Adsorbent from Synthetic Aqueous Solutions
#### 3.2.5 The Influence of the Contact Time on the Adsorption Process  
In order to determine the dependence of the removal degree as a function of contact time, 5 mL of tartrazine aqueous solution with initial concentration of 3 mg L\({}^{-1}\) was brought in contact with 5 mg of biochar for time periods between 3 and 30 min, at 30 \({}^{\circ}\)C. The tartrazine removal degree as a function of contact time between the synthetic solution of tartrazine and the biochar is shown in Figure 5. As one can see in the mentioned figure, the maximum removal efficiency is achieved at contact times of about 18 min, with a removal degree of 90.18%.
## 3 Network Model and Assumptions
Therefore, update frequency should be carefully chosen to maintain a proper balance between information freshness and cost.