esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Wear Behaviors  
In order to evaluate the wear behaviors of conical PDC cutters, a lengthy, 20 h experiment was conducted in both dry conditions and drilling fluid media. The results are presented in Figure 10, with cutting tracks on the limestone shown in Figure 10,b. It is clear that the cutting track formed in the dry condition is narrower than that obtained in the drilling fluid, thus indicating that more rock is cut and removed with the existence of drilling fluid, when all other parameters are the same. Profiles of the cutter tip were characterized using the profilometer and shown in Figure 10c-f. Remarkable differences were observed between the two-dimensional (2D) profiles of the two cutters. A layer of tribofilm was found on the cutter in the dry conditions, whereas scratches formed on the cutter in drilling fluid. The formation of the tribofilm layer is demonstrated by the bump shown in the three-dimensional (3D) image in Figure 10e. During the cutting process, hard PDC easily breaks the rock surface producing small chips of rock, some of which remain in the cutting area and are ground during the cutter-rock interaction until tiny rock particles are produced. In addition, a massive amount of heat is generated during the cutting in dry conditions. Localized temperatures on the cutter tip become notably high and the rock particles are thus sintered together to form a new phase. Similar tribofilm formation has been reported in other brittle materials, such as alumina and shale rock [24; 25; 26]. The newly formed tribofilm attaches to the cutter and forms the structure observed in Figure 10c,e. The hard PDC is covered by the new phase, which forms a structure similar to balling, thus leading to reduced cutting efficiency, as seen in Figure 10a. In real air drilling, however, the aggregation and grinding of rock chip can be avoided because of air circulation.  
Figure 9: Impact of polypropylene glycol (PPG) additive on cutting force at 1 mm cutting depth and 65\({}^{\circ}\) back rake angle. Dashed line is the cutting force without additive.  
This scenario is distinguished for the cutter in drilling fluid media, in which the rock chips removed from the rock flow away with the fluid, so no smaller particles are produced. In addition, the heat generated at the cutter tip dissipates quickly in water, so the temperature remains low. As a result, PDC can continually cut and remove rock during the cutting process, leading to fewer scratches and higher cutting efficiency.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 12 Future Works  
The development of research usually involves various choices in order to define the scope of the paper. As a consequence, some unexplored possibilities and possible improvements are listed below as an encouragement to future endeavors.  
* Use other metrics to define the QL states and actions, such as the BandwidthDistribution. As presented in Section 10.3, these metrics can also be the combination of different variables. As a result, the algorithm is expected to improve in flexibility and efficiency when using two parameters, adapting to a broader set of scenarios. For example, the throughput could be included as part of the reward;
* Expand proposed scenarios: vary number of users, add mobility, varying number and location of hotspots (which could also appear in random locations), and test different values for BandwidthDistribution;
* Evaluate the system using different metrics, such as the relation between convergence speed, the amount of state/action pairs, or the Packet Loss Ratio (PLR);
* Apply the solution on a system without isotropic antennas, such that transmission is made in sectors within a cell;
* Apply the presented solution for 5G New Radio (NR) or the LTE UL, given that the interference in the UL has different characteristics, when compared to DL;
* Provide a similar solution, replacing the RL algorithm for, e.g., multi-armed bandit, providing a simpler solution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Best SINR Gain** & **Worst SINR Gain** & **Best Tput Gain** & **Worst Tput Gain** \\ \hline
**Scenario 1** & & & & \\ \hline Hotspot users & 94\% & 35.3\% & 59.8\% & 5.4\% \\ \hline Outside hotspots & 99.1\% & 11.1\% & 6.5\% & 0.2\% \\ \hline All Users & 96.7\% & 12.8\% & 10.3\% & 0.2\% \\ \hline
**Scenario 2** & & & & \\ \hline Hotspot users & 180.2\% & 25.7\% & 12.5\% & 1.7\% \\ \hline Outside hotspots & 140.9\% & 8.6\% & 1.9\% & \(-\)3.8\% \\ \hline All Users & 167.1\% & 10.3\% & 6.7\% & \(-\)0.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 12: Best and worst results for SINR and throughput of the Q-Learning solution, for each group of users.  
**Author Contributions:** Conceptualization, I.D.d.R., V.A.d.S.J.; data curation, I.D.d.R.; formal analysis, I.D.d.R.; supervision, V.A.d.S.J.; visualization, I.D.d.R.; writing--original draft, I.D.d.R.; writing--reviewing and editing, V.A.d.S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brasil (CAPES)--Finance Code 001.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The proof of concept simulations provided by this paper was supported by the High Performance Computing Center at UFRN (NPAD/UFRN).  
**Conflicts of Interest:** The authors declare that they have no competing interests.
## 4 Results
### Bidirectional Paths  
Figure 4 provides graphs for comparing the results of the two approaches depending on the number of vehicles used. In the case of the system shown in Figure 3, the way in which process reservations are carried out is irrelevant. This is due to the transport tasks implemented in this system, which use only dedicated stations belonging to the PU and DE sets. This means that regardless of whether we carry out process reservations in a staged manner or execute them once--we will obtain the same result of system operation. Moreover, dedicating these stations exclusively to specific tasks also means that the deadlock-free zones implemented in the system will have a positive impact on the system's performance. They divide the entire bidirectional guide path of the vehicle into subsequent segments, which can be considered separately according to the SOCP method. Simulations for this example were carried out in the Delmia QUEST. Their results were compared with the results of simulations published in [2] concerning the application of the original version of the SOCP method for a system with bidirectional paths. The tests were carried out for the number of AGVs varying from 1 to 10. The results of the simulations are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of** & **End Time [s]** & **End Time [s]** \\
**AGVs** & **for SOCP-Standard** & **for SOCP-Three Stages** \\ \hline
1 & 20,454 & 20,454 \\
2 & 11,449 & 11,018 \\
3 & 9410 & 8024 \\
4 & 7964 & 6347 \\
5 & 7311 & 5474 \\
6 & 7425 & 5200 \\
7 & 7392 & 4868 \\
8 & 7831 & 4886 \\
9 & 7889 & 4664 \\
10 & 7458 & 4468 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the simulation (bidirectional paths).  
The empty circles present results for the original SOCP method while the red circles show the results of this method using the deadlock-free zones shown schematically in Figure 1b. The use of the new type of zone allowed for a significant reduction in transport task completion times of up to 40%, which is due to the reduction in the restrictiveness of the method by segmenting the guided paths.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 2 Method  
This study applied a comprehensive approach to evaluate the feasibility of converting flare gas to hydrogen production from both operational and economic perspectives. Data were collected from the Nigerian National Petroleum Company's Annual Statistical Bulletin. Data Envelopment Analysis (DEA) and inverse DEA were applied to conduct an efficiency assessment of oil companies operating in Nigeria and to estimate the maximum potential reduction in flare gas. After this, a process simulation was performed using Aspen HYSYS to model the conversion of flare gas into hydrogen. Finally, an economic analysis was performed. Figure 4 shows a simplified process flow diagram of the method.
###### Abstract  
Few studies have reviewed the reduction of doses in Computed tomography (CT), while various diagnostic procedures use ionizing radiation to explore the optimal dose estimate using multiple exposure quantities, including milliampere-seconds, kilovoltage peak, and pitch factors while controlling the CT dose index volume (CTDvol) and dose length product (DLP). Therefore, we considered optimizing CT protocols to reduce radiation and organ doses during head, chest, abdominal, and pelvic CT examinations. For establishing institutional diagnostic reference levels as a benchmark to correlate with national diagnostic reference levels (NDRLs) in KSA conforming to international guidelines for radiation exposure, 3000 adult-patients underwent imaging of organs. Dose parameters were obtained using Monte Carlo software and adjusted using the Siemens TeamplayTM software. CTDivol, DLP, and effective dose were 40.67 \(\pm\) 3.8, 757 \(\pm\) 63.2, and 1.74 \(\pm\) 0.19, for head; 14.9 \(\pm\) 1.38, 547 \(\pm\) 42.9, and 7.27 \(\pm\) 0.95 for chest; and 16.84 \(\pm\) 1.45, 658 \(\pm\) 53.4, and 10.2 \(\pm\) 0.66 for abdomen/pelvis, respectively. The NDRL post-optimization comparison showed adequate CT exposure. Head CT parameters required additional optimization to match the NDRL. Therefore, calculations were repeated to assess radiation doses. In conclusion, doses could be substantially minimized by selecting parameters per clinical indication of the study, patient size, and examined body region. Additional dose reduction to superficial organs requires a shielding material.  
Keywords:ionizing radiation; organ dose; exposure; computed tomography; achievable dose
## 2 Materials and Methods
### Artifact and Noise Management  
In order to ensure the quality of the data, we applied noise reduction and quality control implementation [32; 33] for a proper model validation.  
* **Noise Reduction Techniques** Right before the image processing, the code inspects the resolution of each image. If any image is below the defined resolution (640 \(\times\) 480 pixels), then this image is skipped. This prevents the negative impact of low-quality or overly compressed images on landmark detection. Also, this ensures that the model will be provided with enough detailed images for accurate landmark detection. This Noise reduction technique aims to improve the clarity of the images given as the input right before the landmark extraction. We also used OpenCV's fast non-local means denoising algorithm, to enhance the quality of the images for a more accurate landmark extraction from MediaPipe.
* **Quality Control** The application of fast Non-Local Means Denoising using OpenCV's cv2.fastNLMeansDenoisingColored() method in the code reduces noise in images while at the same time preserving important features like edges. The denoising parameters chosen were (10, 10, 7, 21), with the first two being responsible for controlling the noise filter strength for luminance and color channels. The other two parameters were responsible for fine-tuning the window size of the filter and its strength. This crucial step ensures that compression and noise artifacts in images will not negatively affect the process of the landmark detection, and therefore, the model can better capture the image features.
# Glueless Multiple Input Multiple Output Dielectric Resonator Antenna with Improved Isolation
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \begin{tabular}{c} **IBW** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Gain** \\ **(dB)** \\ \end{tabular} & \multirow{2}{*}{**Excitation**} & \
\begin{tabular}{c} **Isolation** \\ **between Ports** \\ \end{tabular} &
\begin{tabular}{c} **No. of** \\ **Ports** \\ \end{tabular} & \multirow{2}{*}{**Glueless**} \\ \hline
[23] & 3.95–4.1 & 5.5 & \
\begin{tabular}{c} microstrip \\ line \\ \end{tabular} & not applicable & 1 &
\begin{tabular}{c} yes, groove \\ and side pin \\ \end{tabular} \\ \hline
[25] & 3.95–4.1 & 6 & Probe & not applicable & 1 &
\begin{tabular}{c} yes, groove in \\ ground plane \\ \end{tabular} \\ \hline
[30] & 3.78–4.07 & 5.1 &
\begin{tabular}{c} Multiple \\ feed line \\ \end{tabular} & \(-16\) & 2 & No \\ \hline
[31] & 2.4–2.51 & 2.49 &
\begin{tabular}{c} Cross \\ aperture \\ coupled \\ \end{tabular} & Not Given & 3 & No \\ \hline
[32] & 3.1–3.7 & 5.1 &
\begin{tabular}{c} Aperture \\ coupled \\ \end{tabular} & \(-25\) & 2 & No \\ \hline
[33] & 5.18–5.83 & 5.94 &
\begin{tabular}{c} Microstrip \\ feed \\ \end{tabular} & \(-30\) & 2 & No \\ \hline
[34] & 3.7–4.9 & 6 & \
\begin{tabular}{c} Aperture \\ coupled \\ \end{tabular} & not applicable & 1 &
\begin{tabular}{c} yes, \\ compound \\ ground plane \\ \end{tabular} \\ \hline
[35] & 3.35–3.65 & 5 & \
\begin{tabular}{c} Aperture \\ coupled \\ \end{tabular} & not applicable & 1 &
\begin{tabular}{c} yes, side metal \\ clamps \\ \end{tabular} \\ \hline
[36] & \begin{tabular}{c} 1.41–1.49 \\ and 2.2–2.85 \\ \end{tabular} & \begin{tabular}{c} 4.83 and \\ 4.92 \\ \end{tabular} & Probe & \(-6\) & 2 & No \\ \hline This work & 8.2–9.02 & 6.2 & \
\begin{tabular}{c} Aperture \\ coupled \\ \end{tabular} & \(-14\) & 2 &
\begin{tabular}{c} yes, thin \\ copper rods \\ used \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison Table. Figure 9: Surface current distribution at 8.8 GHz.
## 2 Internal Leakage Minimization Based on Sealing Technology
### Elastic Ring Sealing  
The traditional mechanical elastic ring sealing is composed of a seal ring and a support ring [13]. This has the function of a good sealing effect and less leakage and has also the advantages of low cost and convenient replacement. Therefore, it is the most widely used in hydraulic cylinders. We call it the first generation hydraulic cylinder [14], and the cross-section of the elastic sealing ring is shown in Figure 3.  
According to the structure and shape of the elastic parts, they can be divided into O-ring seals [15], V-ring seals [16], and Y-ring seals [17]. Many scholars continue to improve the performance and life of seals through structural optimization and numerical simulation [18; 19]. Some also use more than two materials or two different ring structures for combined sealing [20]. Taking the O-ring seal, for example, the friction between the hydraulic cylinder and the sealing ring can be calculated with the microelement method proposed by [PERSON]'s group [21]. The axisymmetric model of an O-ring and C-type combined  
Figure 3: Schematic diagram of elastic sealing ring: (**a**) O-ring seal; (**b**) V-ring seal; (**c**) Y-ring seal.  
sealing ring was established in their study, and the friction resistance of the sealing ring under different pressures was calculated.  
\[f=\pi d\int\limits_{0}^{B}p(b)\mu db \tag{1}\]  
where the friction force is defined as \(f\) between the hydraulic cylinder and the sealing ring. The friction coefficient is defined as \(\mu\), and \(p\) (\(b\)) is the contact stress; its value will be changed by \(b\) (the term \(b\) is the width of the sealing groove, and its value ranges from 0 to \(B\)), and the diameter of the piston is \(d\), which is shown in Figure 4 and Equation (1).  
However, due to the direct contact between the cylinder barrel and the sealing ring, the friction resistance is very large during the reciprocating motion of the piston, which seriously affects the frequency response. Therefore, the response speed of the piston cannot be more than 0.5 m/s [22].
## 2 Formal Analysis
### Analytical Structure of the Cross-Section  
In the following, we assume that the optical dressing pulse has a rectangular shape:  
\[\varepsilon_{d}(t)=A_{d}\Big{(}\theta(t-t_{1})-\theta(t-t_{2})\Big{)}\sin( \omega_{d}t), \tag{19}\]  
as shown in Figure 1.  
In Equation (19), \(\omega_{d}\) is the photon energy of the dressing field and \(A_{d}\) is the field amplitude. The dressing pulse is assumed to start at \(t_{1}=0\). Thus, the time \(\tau\) associated with the attosecond XUV pulse is the delay of the attosecond XUV pulse relative to the beginning of the optical dressing pulse. For the pulse in Equation (19), analytical expressions can be obtained for the functions \(f_{fxy0}^{mn}(t)\) appearing in Equation (18). These analytical expressions may be found in the appendix, where we assume that initial-state dressing is negligible, i.e., in Equation (18) terms involving indices \(a\) and \(b\) are set to zero. From Equation (12) we see that the XUV one-photon cross-section can be written as:  
\[\sigma^{(1)}(\omega,\tau)=\frac{2\pi\omega}{c}\sum_{f}\Big{(}\bar{F}_{0f}( \omega,\tau)F_{f0}(\tau)e^{-i\omega\tau}-\bar{F}_{0f}(-\omega,\tau)F_{f0}( \tau)e^{i\omega\tau}+c.c.\Big{)}, \tag{20}\]  
where  
Figure 1: Rectangular-shaped dressing pulse that starts at time \(t_{1}=0\) and ends at \(t_{2}\), which is an integer multiple of the period \(2\pi/\omega_{d}\). \[\tilde{F}_{0f}(\omega,\tau)=\int\limits_{-\infty}^{\infty}dt\;F_{0f}(t)\theta(t- \tau)e^{i\omega t} \tag{21}\]  
is the Fourier transform of the function \(F_{0f}(t)\theta(t-\tau)\). In view of Equation (20), the analytical structure of \(\tilde{F}_{0f}(\omega,\tau)\) determines the resonance peaks in the XUV one-photon cross-section as a function of \(\omega\). As shown in the appendix, \(\tilde{F}_{0f}(\omega,\tau)\) has the general form:  
\[\tilde{F}_{0f}(\omega,\tau)=\sum_{k}C_{k}\;\frac{e^{-i(\Delta_{k}-i\frac{ \Gamma_{f}}{L}-\omega)\tau}}{i(\Delta_{k}-i\frac{\Gamma_{f}}{L}-\omega)}, \tag{22}\]  
where each \(\Delta_{k}\) is one of the quantities presented in Equations (A1)-(A3) in the appendix. Thus, the \(\Delta_{k}\) define the poles of \(\tilde{F}_{0f}(\omega,\tau)\) in the complex \(\omega\) plane. The peaks of \(\sigma^{(1)}(\omega,\tau)\) at the \(\Delta_{k}\) correspond to XUV transitions accompanied by the absorption or emission of zero [Equation (A1)], one [Equation (A2)], or two [Equation (A3)] dressing-laser photons, or one absorption and one emission [Equation (A1)]. The \(C_{k}\) in Equation (22) depend on whether the attosecond pulse precedes the dressing pulse (\(-\infty<\tau<t_{1}\)), whether the attosecond pulse overlaps with the dressing pulse (\(t_{1}<\tau<t_{2}\)), or whether the attosecond pulse arrives after the dressing pulse is over (\(t_{2}<\tau<\infty\)). The coherent superposition of the electronically excited states associated with the resonance peaks at the energies \(\Delta_{k}\) gives rise to oscillatory changes in the XUV one-photon cross-section as a function of \(\tau\).
## 4 Discussion and Conclusions  
For the calculation of lightning electromagnetic fields over a perfectly conducting ground, the common method is to evaluate the integral by means of numerical integration. In the proposed method, the formula for calculating the lightning electromagnetic field is divided into two parts: One that can be solved analytically, and the other that can be solved numerically only by integral operations.  
For the conventional method, the results are sensitive to \(dz^{\prime}\) at a close distance (tens of meters), which must be small enough to get an accurate numerical solution. However, when \(dz^{\prime}\) is too small, the amount of data required in the program is huge and the calculation steps are tedious, resulting in a lengthy time and slow calculation speed. As for the proposed method, \(dz^{\prime}\) can be adopted as a relatively large value, so the computational efficiency can be improved inevitably. Two calculation methods were simulated in MATLAB, of which the simulation results showed that under the same accuracy, the calculation time of the proposed method is about 1/40 of that of the original method. As for large lightning striking distances (hundreds of meters), the conventional method's results are not sensitive to \(dz^{\prime}\) anymore, and relative larger \(dz^{\prime}\) can be adopted to get an accurate numerical solution. In other words, for the conventional method, small \(dz^{\prime}\) should be adopted for the observation point near the lighting channel while large \(dz^{\prime}\) can be used for the observation point far away. However, the proposed method has better generality because it can calculate the horizontal electric fields at different lightning striking distances with a uniform \(dz^{\prime}\), which makes its implementation more convenient and easily programmed. Additionally, as can be seen, the only item required to be calculated numerically is very simple and other items can be implemented in an analytical way, so the proposed method is very easy to be programmed, compared with the original method.  
Conceptualization, X.L.; methodology, X.L. and T.G.; software, X.L. and T.G.; validation, X.L. and T.G.; formal analysis, T.G. and X.L.; writing--original draft preparation, T.G.; writing--review and editing, X.L.; visualization, T.G.; supervision, X.L.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. The authors declare no conflict of interest.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 3 Integrated Simulation Model  
Considering the models introduced in the previous section, the proposed integrated model can be formulated as presented in Figure 8 in a flowchart. The problem is divided into the following main parts: orbit, attitude, irradiance, thermal, electrical, battery, and control model. To solve the satellite's transient temperature field, the energy equation was implemented in the satellite's main parts, here designed as the photovoltaic panels and battery, resulting in a total of seven equations. The irradiance in the boundary condition is transient to simulate different scenarios of spin and orbit.  
The iterative process is performed until reaching the maximum desired simulation time. Overall, the simulation model consists of the following steps, contemplated in Figure 8.  
* Given an initial SoC value, battery voltage, and temperature values, the algorithm calculates the CubeSat parameters for each iteration _it_;
* Given a time \(t\), an iterative process takes place to calculate the new battery voltage;
* The heater power is calculated considering a possible controller;
* The load current is calculated based on the battery voltage and the maximum power drained (through tasks and/or payloads);
* The maximum current supplied by the solar panels is calculated based on the voltage of the battery, its temperatures and the irradiance affecting them;
* The battery parameters (SoC and current) are calculated based on the charging currents, heater and solar panels;
* The power balance is calculated based on the difference between the current supplied by the panels and that drained by the CubeSat (optional to be used in the control procedure, not necessary for the algorithm convergence);
* The battery voltage calculation is performed until convergence;
* A control procedure defined by the operator is performed, aiming to control the heater power and the allocated tasks;
* The calculation process is repeated for \(t+\Delta t\).  
Figure 7: Battery cell equivalent circuit.