esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Swab and Surge Pressures: A Survey
## 4 Proposal for New Research on Surge and Swab Pressure Modeling
### The Role of Sensors and Data Acquisition (Logging Data)  
This section presents an overview of different sensors used to understand a well's physical properties, from the surface to the final depth.  
The sensors and the sensor recording systems are classified into different categories based on the physical attributes to be measured:  
* Depth tracking sensor;
* Flow in and out tracking sensors;
* Measurement while drilling (MWD) and Logging-While-Drilling (LWD) tool;
* Electromagnetic-Wave Resistivity (EWR) tool;
* Electronic Drilling Recorder (EDR) system.  
The depth tracking sensor provides instantaneous or an average rate of penetration (ROP) based on the amount of work done by draw works or change in hydrostatic pressure in a column of water.  
The flow in and out tracking sensors are used to monitor the fluid-flow rate being applied downhole and the flow rate coming out of the annulus; these sensors provide an early warning of either a kick condition or a loss of circulation. The surface revolutions-per-minute (RPM), rotary torque, and hook load are obtained using Drill-monitor sensors, used for efficient drilling and minimizing downhole failures like stick-slip or stuck-pipe. Several other sensors are used to measure the drilling mud level, surface pressure, and gases present in the formation, which helps to understand downhole conditions better.  
Measurement while drilling (MWD) is a type of Logging-While-Drilling (LWD), in which tools are encompassed in a single module in the steering tool. This tool is with the drill string at the end of the drilling apparatus, providing wellbore position, drill bit information, and directional data, as well as real-time drilling information.  
In the rig site software system, MWD and LWD are the most comprehensive data-acquisition systems present at the rig site. Real-time data-acquisition systems typically connected to a surface and downhole sensor enable live monitoring of the rig-equipment operation and the well-construction process. The obtained surface measurements are stored in an electronic drilling recorder (EDR) system in the form of electronic tour-sheet applications.
## 3 Overview of Charging Stations
### Electric Vehicle Charging Infrastructure in India  
Currently, in India, the electric vehicle charging infrastructure is mainly divided into Battery Swapping and Charging the Battery which is further divided into AC (slow) Charging and DC (fast) Charging. In the case of alternating current charging, alternating current is provided to the electric car's onboard charger, which converts AC to DC and is then used to charge the vehicle battery. DC charging, on the other hand, converts AC to DC at the charge point and feeds DC straight to the car battery. Based on the  
1. Home Charging: Home chargers commonly use a 230 V/15 A single-phase socket with a maximum output capacity of 2.5 kW. Home charging is clearly an AC (Alternating Current) charging method. The amount of electricity used is factored into the home-mereing system. The time it takes to charge an electric vehicle is determined by the charging rate and the quantity of charge necessary (which is determined by the battery's usable capacity). Electric scooters can be charged in 2-3 h and electric cars in 6-7 h using home charging.
2. Public Charging: Public charging means charging the electric vehicle outside the home. For example charging the vehicle either in the supermarket, cinema hall, retail parks, etc.
3. Battery Swapping: The electric vehicles swap their drained batteries with fully recharged batteries. The Battery Swapping concept decouples battery charging from vehicle use, which benefits both the swapping station operator and the power system. No rapid charging is necessary, and electrical grid management is straightforward [11].
## 3 Results and Discussion  
The 24 male patients admitted to the ICU with COVID-19 were divided into three Groups (A, B and C) with 8 patients each, and the following comparisons were conducted:  
* Group A vs. B. This comparison reflects patients' severity based on the need for IMV, highlighting the progression in severity from Group A (milder condition, without IMV) to Group B (patients requiring IMV).
* Group B vs. C. This comparison focuses on mortality in the ICU among patients requiring IMV. It distinguishes patients who survived (Group B) from those who died in the ICU in an average of 7 days after the current analysis (Group C), thereby isolating factors associated to mortality in the ICU.  
* Group A + B vs. C. This analysis is similar to the previous one, except it combined Groups A and B (including both non-IMV and IMV patients), and compared them to Group C, providing a broader control group that included a higher diversity of patients.  
When performing the aforementioned comparisons (Table 1), aside from the target variables, i.e., IMV and mortality, there were no statistically significant differences in the demographic and clinical data between patient groups. However, there were exceptions for patients from Group A that spent significantly less time in the ICU (\(p\) = 0.002) and required more HFO (\(p\) = 0.007) instead of IMV.
# Brief Review of Recent Developments in Fiber Lasers
## 3 Rare Earth-Doped Fiber Lasers
### Rare Earth Ions for Laser Applications
\[\lambda_{F}=\frac{2\pi c}{\omega_{F}}=\frac{\int\lambda I_{F}(\lambda)d\lambda} {\int I_{F}(\lambda)d\lambda} \tag{15}\]  
where \(c\) is the speed of light in the vacuum, and \(I_{F}(\lambda)\) is the fluorescence intensity at the wavelength \(\lambda\). If the upper level is more populated than the lower level, a population inversion takes place in the system. Population inversion is a necessary condition for amplification or lasing. It is impossible to attain population inversion in a two-level system [26]. The system must have at least three levels in order to be suitable for laser amplification. A commonly used four-level laser scheme is presented in Figure 2a for Yb\({}^{3+}\) ions. In this scheme, two levels of the ground manifold and two levels of the excited manifold are involved in a laser cycle. As mentioned above, the quantum defect, \(\eta_{q}\), is an unavoidable source of heat generation in lasers. The heat generation process is responsible for transverse-mode instability, thermal lensing, and catastrophic fiber damage. In RE-doped fiber lasers, heating can also cause a reduction in both the emission and absorption cross-sections of the RE ions, affecting the gain of the active fiber. Once the concentration of the RE ions exceeds a certain limit, cooperative effects such as an energy transfer process, a cross-relaxation process, nonradiative energy transfer, cooperative emission, and excitation become essential. Some of these processes can cause heat generation [27]. Cooperative effects limit the maximum  
Figure 2: Energy diagrams of a traditional four-level laser (**a**) and a radiation-balanced laser (**b**). \(\lambda_{P}\) and \(\lambda_{L}\) are the pump and laser frequencies, respectively. \(\lambda_{F}\) is the mean fluorescence frequency. Solid and dashed arrows illustrate photon and phonon transitions, respectively. ion concentration that can be doped into the fiber, resulting in the use of longer fibers. On the other hand, a longer fiber can serve as a source of nonlinear effects, for example, Raman and Brillouin scattering.
## 3 Joint Simulation Process and Modelling of Vibration and Circuit  
### Simulation Process  
This research used the general-purpose finite element software ABAQUS (version 6.14-5, 3 DEXPERIENCE Company, Waltham, MA, USA), the circuit design and simulation software Tina-TI (version 9.3.30.248, Texas Instruments, Dallas, TX, USA) and the programming and numeric computing platform MATLAB (version 2020b, MathWorks. Inc., Natick, MA, USA) for the numerical modeling and simulation. The co-simulation of the structural vibration simulation and electromagnetic energy harvesting process was realized through user-defined programs. The simulation process is shown in Figure 4. Firstly, the vibration signal of the bridge under a moving load was obtained by using the finite element model of the ABAQUS software. Then, the program for calculating the electromechanical coupling coefficient of the magnetic induction coil was compiled on the MATLAB software platform and the electromotive force of the magnetic induction coil was obtained by inputting the vibration data extracted from ABAQUS. Then, the calculated electromotive force data was imported into the Tina-TI software for the circuit simulation to obtain the current signal in the coil. Finally, according to the current signal to calculate the electromagnetic damping force, the calculated magnetic damping as a load was inputted into the ABAQUS finite element model and then simulated to obtain the vibration signal and then extracted, repeating the above steps until convergence.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 3 System Architecture and Key Technologies
### Energy Beaming Payload: Fiber Lasers  
One of the most important steps towards making these systems more sustainable and attract attention to them is to advance the TRL of the energy-beaming technology (i.e., moving from TRL 4 to 8/9), as explored in depth in [20]. In solid-state lasers, scaling to higher pump powers can lead to a decrease in beam quality and reduced gain due to thermally induced optical aberrations and non-linearities in the gain element, as explored in depth in [21]. Similarly, a larger modal volume can lead to heat removal difficulties in the laser crystal and multi-spatial mode operation. While attempts to optimize the radiance of single gain elements are continuing, many laser systems appear to be reaching their physical limits. Introducing high-brightness laser diodes and double-cladded fibers has made a revolutionary step in fiber laser technology, which has resulted in unprecedented output powers in the CW regime. The proposed architecture as mentioned above makes use of an ytterbium-doped fiber laser, in which the gain medium is an active optical fiber doped with rare elements, as explained in [22]. Fiber laser technology [Figure 4] is rapidly advancing thanks to their advantages over solid-state lasers, and they present characteristics which are critical for WPT. These lasers have been defined the \"perfect building blocks\" for directed energy applications thanks to their properties of being electrically powered, efficient, modular and reliable with their civilian technology backbone. With the development of new rare-earth-doped fibers such as those with ytterbium and erbium, fiber lasers have the ability to scale to very high powers. The advantages of using a fiber laser include higher beam quality, higher efficiency and greater stability and reliability [23; 24; 25; 26; 27; 28]. The pursuit of high-power outputs in industrial lasers and in directed energy weapons has led to an interest in scalable system geometries in which an arbitrary number of identical laser beams can be added together to achieve final output power levels in the order of tens of kilowatts. The use of optical fibers to deliver the output beam allows for greater flexibility in power scaling [29; 30]. Although the very narrow output beam has a divergence angle in the order of micro-radians, diffraction over very long distances will cause the beam to spread out as it travels through the vacuum of space. Without proper collimation, the beam would have a final diameter on the Moon's surface of several hundreds of meters, and thus would not be useful for the purpose of power harvesting. In order to reach the surface with a beam of reasonable size, the beam must be expanded at the source. For this purpose, metallic mirrors can be used to create an off-axis telescope, with optical coatings for the VIS and NIR that exhibit extremely low scattered light and absorption losses, both in the order of \(10^{-5}\)-\(10^{-4}\)% [31; 32; 33].  
Figure 4: High-power fiber laser scheme.
## 4 Genetic Regulatory Network-Based Rescheduling Method  
Based on the event-driven mechanism, a rescheduling problem is to be addressed if the delivery time deviation of jobs is larger than the deviation tolerance. A GRN-based method is proposed to solve this problem:  
1. Genes are generated to represent the decision variables. In terms of decision variables \(y_{mn}\) and \(x_{rii}\) in Table 1, two kinds of genes (i.e., \(\{\boldsymbol{\pi}_{rm}|r=1,2,\cdots,R;\,m=1,2,\cdots,M\}\) and \(\{\boldsymbol{\pi}_{rtki}|r=1,2,\cdots,R;\,\,k=1,2,\cdots,K_{i};\,i=1,2, \cdots,I\}\)) are generated. The gene \(\boldsymbol{\pi}_{rm}\) denotes that the \(r\)th job entering the HFS belongs to job type \(m\), whereas the gene \(\boldsymbol{\pi}_{rtki}\) denotes that the \(r\)th job entering the HFS is processed on the \(k\)th machine at the \(i\)th production stage.
2. Regulation equations are developed to describe the constraints and objectives: \[\boldsymbol{\sigma}_{z}=f_{z}(\boldsymbol{\mu}_{1}(n),\boldsymbol{\mu}_{2}(n),\cdots,\boldsymbol{\mu}_{Z}(n),\theta_{1},\theta_{2},\cdots,\theta_{E}),\] (3) where \(Z\) represents the number of genes in a GRN, \(\boldsymbol{\mu}_{z}(n)\) is a binary variable that is equal to 1 if gene \(\boldsymbol{\pi}_{z}\) is in the expressed state at the \(n\)th iteration, otherwise, \(\boldsymbol{\mu}_{z}(n)\) is equal to 0, \(\boldsymbol{\sigma}_{z}\) is the inhibition coefficient that describes the inhibitory effects on gene \(\boldsymbol{\pi}_{z}\) quantitatively; \(\theta_{1},\theta_{2},\cdots,\theta_{E}\) are regulatory parameters; and \(f_{z}:\mathbf{R}^{(Z+E)}\rightarrow\mathbf{R}\) is a nonlinear function related to workshop conditions.
3. Gene expression procedures are designed to determine solutions. At the beginning of such a procedure, the set of related genes is first confirmed based on operations within the rolling window. If a reactive scheduling is necessary, all these genes are initialized to the unexpressed state. At each iteration (i.e., \(n\,=\,1,\,2,\,3,\,\cdots,\,N\)), some of these genes are converted to the expressed state based on the regulation equations. When \(n>N\), genes in the expressed state are confirmed, and their corresponding decision variable values are equal to 1 in the rescheduling solution.
4. Regulatory parameters are optimized to minimize the makespan. A near-optimal solution is obtained by gene states \(\{y_{rm}|r=1,2,\cdots,R;\,m=1,2,\cdots,M\}\) and \(\{x_{rtki}|r=1,\,2,\,\cdots,\,R;\,k=1,\,2,\,\cdots,\,K_{i};\,i=1,\,2,\,\cdots, \,I\}\) that are decided by the optimized regulation equations.  
As shown in Figure 5, the regulation equation and expression procedure of genes \(\{\pi_{rm}|r=1,\,2,\cdots,R;\,m=1,\,2,\cdots,M\}\) and those of genes \(\{\boldsymbol{\pi}_{rtki}|r=1,\,2,\cdots,R;\,k=1,\,2,\cdots,K_{i};\,i=1,\,2, \cdots,I\}\) are presented in Sections 4.1 and 4.2, respectively. The regulatory parameter optimization procedure is given in Section 4.3.  
Figure 4: Operation-based rolling window.
# A Maturity Model for Trustworthy AI Software Development
## 3 A Maturity Model for Trustworthy AI Software
### Analysis of Assessment Results  
#### 4.1.1 Developers' Self-Assessments vs. External Assessors' Assessments  
We compare the self-assessment results with those made by external assessors (Table 7). We asked developers to self-assess the capability levels of AI development processes while we, as external assessors, assessed the project at the same time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Process** & **Project** & **Pr1** & **Pr2** & **Pr3** & **Pr4** & **Pr5** & **Pr6** & **Pr7** & **Pr8** & **Pr9** & **Pr10** & **Pr11** & **Pr12** & **Pr13** \\ \hline Software requirements analysis & 2 & 1 & 3 & 3 & 3 & 3 & 1 & 3 & 3 & 3 & 3 & 0 & 0 \\ \hline Software architecture design & 2 & 3 & 3 & 3 & 3 & 3 & 2 & 3 & 3 & 2 & 3 & 2 & 0 \\ \hline Data collection & 2 & 3 & 1 & 2 & 3 & 3 & 2 & 2 & 2 & 3 & 3 & 3 & 1 \\ \hline Data cleaning & 0 & 3 & 2 & 3 & 2 & 3 & 1 & 1 & 1 & 3 & 1 & 1 & 1 \\ \hline Data preprocessing & 0 & 3 & 1 & 3 & 2 & 3 & 1 & 1 & 3 & 3 & 2 & 0 & 0 \\ \hline Training process management & 2 & 3 & 1 & 2 & 3 & 3 & 3 & 3 & 2 & 3 & 3 & 2 & 2 \\ \hline Performance evaluation of AI model & 2 & 2 & 2 & 3 & 3 & 3 & 3 & 2 & 3 & 3 & 3 & 1 & 1 \\ \hline Safety evaluation of AI model & 1 & 2 & 3 & 1 & 3 & 3 & 3 & 0 & 3 & 1 & 3 & 0 & 0 \\ \hline Final AI model management & 2 & 3 & 3 & 3 & 3 & 3 & 2 & 3 & 2 & 3 & 3 & 1 & 1 \\ \hline System safety evaluation & 0 & 3 & 3 & 1 & 0 & 0 & 0 & 0 & 3 & 1 & 3 & 0 & 0 \\ \hline System safety preparedness & 0 & 3 & 3 & 0 & 0 & 0 & 0 & 3 & 2 & 0 & 2 & 2 & 0 \\ \hline AI infrastructure & 0 & 1 & 0 & 1 & 0 & 0 & 3 & 0 & 3 & 0 & 3 & 1 & 0 \\ \hline AI model operation management & 0 & 2 & 0 & 0 & 0 & 3 & 3 & 0 & 1 & 1 & 3 & 1 & 1 \\ \hline Averaged capability level of 13 processes (maturity level) & 1.4 & 2.5 & 1.9 & 2.3 & 2.8 & 3.0 & 2.2 & 2.3 & 2.4 & 2.0 & 2.7 & 1.1 & 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Assessment results (capability levels) of applying AI-MM to 13 AI projects.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Process** & **Assessment Result (Capability Level)** \\ \cline{2-3}  & **Developers** & **Assessors** \\ \hline Software requirements analysis & 3.4 & 2.3 \\ \hline Software architecture design & 3.1 & 2.4 \\ \hline Data collection & 3.2 & 2.3 \\ \hline Data cleaning & 2.6 & 1.6 \\ \hline Data preprocessing & 2.8 & 1.6 \\ \hline Training process management & 3.1 & 2.4 \\ \hline Performance evaluation of AI model & 3.2 & 2.4 \\ \hline Safety evaluation of AI model & 2.1 & 1.9 \\ \hline Final AI model management & 3.1 & 2.4 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Developers’ self-assessments vs. assessors’ assessments.
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.3 Dynamic Range Adjustment
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Times**} \\ \cline{2-3}  & **Number of Non-Dominated** & **Number of Non-Dominated** \\  & **Solutions Is Zero** & **Solutions Is Maximum** \\ \hline \multicolumn{3}{c}{The total number of iterations is 100 times.} \\ \hline \multirow{2}{*}{MOBA-CV} & the best \(\alpha\), \(\gamma\) & 0 & 12 \\ \cline{2-3}  & reference [15] & 1 & 6 \\ \hline MOBA-CV-SARSA with fixed range & 0 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Comparative analysis of 0 and MAX. Table 19 showcases that when the delay remains within an acceptable range, MOBA-CV-SARSA-DA reduces energy consumption by 102.542 W compared to MOBA-CV-SARSA-MA. This underscores the advantages of employing an automatic switching mechanism for dynamically adjusting the search scope, validating its capacity to discover more balanced solutions between the two objectives. Table 20 presents the average computational time for each method. MOBA-CV-SARSA-MA requires extended computation time based on the outcomes of MOBA-CV-SARSA with a fixed range. Conversely, MOBA-CV-SARSA-DA, integrating an automatic switching mechanism for dynamic range adjustment, saves 48.613 s compared to MOBA-CV-SARSA-MA while maintaining comparable performance. This underscores the efficacy and efficiency of incorporating an automatic switching mechanism for dynamic range adjustment in optimization problems.
# Emerging Applications of Machine Learning in 3D Printing
## 3 Results
### ML in the Design of 3D-Printed Products  
A digital twin is a dynamic, digital replica of a technical object, such as a physical system, device, machine, production process, or even a living organism. Implementing digital twin technology has become an integral part of Industry 4.0, providing companies with tangible benefits, especially in the context of sustainable production and maintenance. This approach enables the collection and transformation of data into actionable knowledge [26]. Physical models are used to simulate tasks and processes, while simulation models are used to refine and improve physical tasks and operations. Continuous monitoring of processes and parameters enables continuous improvement of intelligent eco-design. By integrating advanced algorithms and machine learning (ML), digital twins can predict system failures, optimize performance, and reduce waste [27]. This capability is essential for sustainable production, enabling efficient use of resources and reducing environmental impact. Machine learning facilitates the identification of complex patterns in process data, driving innovation in product design. Integrating ML with digital twin platforms supports adaptive manufacturing, enabling real-time adjustments based on feedback from virtual simulations [28]. Such systems are particularly valuable for designing 3D-printed products, ensuring optimal material selection and structural integrity. By simulating how a product will behave under different conditions, digital twins enable engineers to identify and address potential design flaws before physical prototyping. This not only speeds up the development cycle but also minimizes the costs associated with a trial-and-error approach [29]. In addition to enhancing product functionality, machine learning-based digital twins support intelligent decision-making, streamlining production planning and operational efficiency. As a result, companies can achieve higher levels of personalization while maintaining scalability. The synergy between digital twins, 3D printing, and machine learning is paving the way for a new era of innovative product development, where sustainability and performance are seamlessly integrated.
## 3 Results
### Cell-Free DNA (cfDNA) Quantity and Library Yields in Clinical Specimens  
Overall, 76 NSCLC patients and a total of 92 plasma samples were included in our NGS analysis. cfDNA was isolated from 1-4 mL of plasma and a range of 2.6-309 ng /mL of plasma was obtained (median 10.40 ng/mL).  
It was possible to proceed with NGS library preparation in all 92 samples and the range of cfDNA used for the NGS reaction was 6.37-59.80 ng, being aware that 23/92 (25%) samples were below the suggested 20 ng cut-off needed to reach the 0.1% LoD.  
In addition, when qualitative assessment of cfDNA was performed on a TapeStation 2200 (Agilent Technologies, Santa Clara, USA), it turned out that DNA fragments of about 150-180 bp and multiples were predominant compared to genomic DNA (high molecular weight), thus demonstrating the reliability of the magnetic extraction method in isolating the fragmented cfDNA fraction with a higher chance to be tumor-derived (data not shown).  
After preparation, libraries were quantified and showed a wide range of concentrations between 40.5-1440 pMol (median 450 pMol). As expected, it was observed that library yields increased proportionally with the amount of cfDNA ng employed per reaction ([PERSON]'s correlation coefficient (r\({}_{\text{s}}\)) = 0.67, \(p\) < 0.0001) (Figure 2), a finding in line with literature [17].  
Overall Considerations of the OncomineTM Lung Cell-Free DNA Assay (OLcfA) Sequencing Performance Applied to a Real-Life Routine Plasma Cohort