esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Thrust Nonlinear Orbit Control for Very Low Lunar Orbits
###### Abstract  
In the next decades, both space agencies and private competitors are targeting the lunar environment as a scientific and technological resource for future space missions. In particular, the confirmed existence of water-ice deposits in the vicinity of the poles (predominantly the south pole) makes polar or near-polar low lunar orbits attractive for the purpose of designing space missions that could search for suitable Lunar base sites. However, traveling very-low-altitude orbits is very challenging, as they are strongly perturbed by the Moon's gravity field as well as third- and fourth-body effects due to the Earth and the Sun. Several studies demonstrate that these orbits are expected to impact the lunar surface in a few months. Therefore, the definition and implementation of an effective station-keeping strategy represents a crucial issue in order to extend satellites' lifetime. In this paper, a feedback nonlinear control law is employed in order to perform corrective maneuvers aimed at keeping the state of the satellite within acceptable margins. The satellite is assumed to be equipped with a steerable and throttleable low-thrust propulsion system. The control law is based on the Lyapunov stability theory and does not require any reference path to track, with a considerable decrease in the computational cost. The proposed real-time control law includes control saturation, related to the maximum available thrust magnitude, and is developed employing modified equinoctial elements, in order to avoid singularities and extend its range of application. Finally, the strategy at hand is tested in the presence of all the relevant perturbations (i.e., harmonics of the selenopotential, third- and fourth-body effects) in order to show its effectiveness and efficiency.  
low lunar orbits; low-thrust propulsion; real-time guidance; feedback control; Lyapunov theory
## 3 Data Pre-Processing
### Method for Accumulating Measurement Error Data
According to \(\left\{\left(x_{1,v},y_{1,v}\right),\cdots,\left(x_{1,k},y_{1,k}\right)\right\}\) and \(\left\{\left(\tilde{x}_{f,1},\tilde{y}_{f,1}\right),\cdots,\left(\tilde{x}_{ f,k},\tilde{y}_{f,k}\right)\right\}\)and Equation (3), the true measurements at \(1:k\)th time, that is, \(\left\{\tilde{z}_{x,1},\cdots,\tilde{z}_{x,k},\tilde{z}_{y,1},\cdots,\tilde{z} _{y,k}\right\}\) are calculated. 7. The measurement errors at time \(1:k\)th are the difference between \(\left\{\tilde{z}_{x,1},\cdots,\tilde{z}_{x,k},\tilde{z}_{y,1},\cdots,\tilde{z} _{y,k}\right\}\) and \(\left\{z_{x,1},\cdots,z_{x,k},z_{y,1},\cdots,z_{y,k}\right\}\). 8. The distances between AUV and M-DS at \(1:k\)th time, that is, \(\left\{dis_{1},\cdots,dis_{k}\right\}\) are calculated. To verify the effectiveness of method 1 and method 2, we use IMU to sense the positions of AUV, F-DS and M-DS, namely \(\left\{\left(\tilde{x}_{v,1},\tilde{y}_{v,1}\right),\cdots,\left(\tilde{x}_{ v,k},\tilde{y}_{v,k}\right)\right\}\), \(\left\{\left(\tilde{x}_{f,1},\tilde{y}_{f,1}\right),\cdots,\left(\tilde{x}_{ f,k},\tilde{y}_{f,k}\right)\right\}\). According to Equation (3), the measurements at \(1:k\)th time, that is, \(\left\{\left(z_{x,1},z_{y,1}\right),\cdots,\left(\tilde{z}_{x,k},\tilde{z}_{y,k }\right)\right\}\) are calculated. The measurement errors at \(1:k\)th time are the difference between \(\left\{\left(\tilde{z}_{x,1},\tilde{z}_{y,1}\right),\cdots,\left(\tilde{z}_{x,k },\tilde{z}_{y,k}\right)\right\}\) and \(\left\{z_{x,1},\cdots,z_{x,k},z_{y,1},\cdots,z_{y,k}\right\}\). We utilize three sets of experimental data of our recovery system. Figures 5 and 6 show the true and estimated measurement error data and their corresponding operating distance in AUV homing to F-DS, respectively. Figure 7 is the comparison between them. The Figures 8 and 9 are the true and estimated measurement error data and their corresponding operating distance in AUV homing to M-DS, respectively. Figure 10 is the comparison between them. The comparison results show that the system error of estimated measurement error is close to that of true measurement error. Although there are some deviations between them, the estimated measurement error can reflect the statistical characteristics of measurement error when only using acoustic sensor. The methods mentioned above are not suitable for the long-distance situation, because the mileage error of AUV for long-distance is large, which results in a large gap between the estimated measurement error and the true measurement error. Figure 5: The true measurement error data between inertial measurement unit (IMU) and acoustic sensor. Figure 6: The estimated measurement error data according to method 1. Figure 7: The comparison of true and estimated measurement error data.
## 4 Results
### Time Performance of \(k\)-Anonymity Set
#### 4.2.2 Impact of Spatial Partitioning  
In this subsection, we experiment on the impact of the embedded spatial partitioning methods on the computation time of \(k\)-anonymity set, despite the fact that in GeoSpark the spatial partitioning method is usually used to optimize spatial join.  
Initially, in Figure 8, the total number of records in PointRDDs for four partitioning methods in a specific timestamp is demonstrated. Obviously, the partition size of \(KBD\)-Tree partitioning is more balanced than that of Quad-Tree. Also, we observe that regarding \(R\)-Tree partitioning, an overflow in data partition (e.g., partition 2) occurs, which is much larger than other partitions because \(R\)-Tree does not consider the whole space [33].  
Note that, in the third dataset, with size \(N=2000\), the data distribution varies from one timestamp to another and thus, it is not recorded in Table 10.  
In Table 10, we focus only on the impact of \(R\)-Tree and \(KDB\)-Tree spatial partitioning on \(k\)-anonymity set time computation without PointRDD indexing, as GeoSpark does not support building local indexing on spatially partitioned data for spatial \(k\)-NN queries.  
We choose to evaluate, for a specific \(k\) with value equal to 16, the impact of spatial data partitioning on the time needed to formulate the \(k\)-anonymity set for 10 consecutive timestamps, as aforementioned. As the results show, the time cost of the \(k\)-anonymity method did not benefit from data partitioning.  
Figure 8: Spatial PointRDD Data Distribution for 4 Spatial Partition Techniques for 2000 Mobile Objects.  
Figure 7: Time Cost for Mobile Objects \(N=\{500,2000,8000,32.000\}\) without Indexing for \(k=8\).  
In addition, the time for the \(k\)-anonymity set computation is a little bit lower when using \(R\)-Tree for datasets having smaller size. However, in case of a larger dataset, the difference between the two types of partitioning is normalized. This means that most of the processing time, in the same machine, is focused on \(k\)-NN query computation.
## 1 Introduction
Advanced MEMS technology has been leveraged to create a portable TP analysis system, eliminating the constraints of time and location associated with traditional autoclave equipment. By employing a parallel arrangement of multiple types of LOC equipment, it is possible to achieve large-capacity detection and meet the total phosphorus pretreatment requirements across various scale levels. The most important aspect is that, owing to the integration and miniaturization technologies employed, it has significant advantages over traditional autoclave equipment in terms of both cost and size. This enables the use of more cost-effective and environmentally friendly testing procedures.
# Life Cycle Assessment of Medical Waste Management: Case Study for Istanbul
## 2 Materials and Methods
### Life Cycle Assessment
#### 2.2.1 Goal and Scope Definition  
The objectives of this study are to evaluate the environmental impacts of the current medical waste management system in Istanbul through an LCA and to investigate the environmental improvement potential of alternative waste management strategies by creating scenarios with varying proportions of disposal methods such as landfill, incineration, and sterilization. Therefore, this study aims to identify the medical waste management system with the least environmental impact among the baseline and alternative scenarios.  
In this context, each scenario was assessed from environmental and human health perspectives; however, no economic analysis was conducted.  
The functional unit was chosen to be 1 ton of medical waste generated in Istanbul to compare the baseline and alternative scenarios.  
System boundary: In this study, each scenario includes waste collection from healthcare facilities, transportation to disposal sites, the use of different disposal technologies such as incineration, sterilization, and landfilling, as well as energy recovery (electricity generation) from these methods. The system excludes the disposal of hazardous wastessuch as ash-slag, filter cake, etc., during the incineration process, the consumption of fuel during transportation, sterilization, and incineration, the treatment of leachate in the landfill, and the distribution of the electricity produced to the customer. The system is presented in Figure 1.  
Impact Categories: global warming potential, acidification potential, eutrophication potential, ozone layer depletion potential, freshwater aquatic ecotoxicity potential, and human toxicity potential are among the categories found through the CML 2001 impact analysis method selected for LCA application in this study.
## 4 Experimental Verification
### Force-Thermal Follow-Up Loading Experiment  
#### 4.2.1 Deflection Deformation Experiment  
After completing the loading system capability tests, a force-thermal follow-up loading experiment was conducted on the multi-stage telescopic wing. The applicability of the  
Figure 34: Constant force load regulation experiment.  
Figure 35: Time-varying force load control experiment.  
loading method was analyzed based on the deflection deformation trend of the wingtip during the telescoping process.  
The experimental conditions were set to be the same as the simulation analysis conditions described earlier. The evaluation conditions were set as follows: (1) only force load acting, (2) only thermal load acting, and (3) combined force and thermal loads acting. During the experiment, the force load was applied based on the time-varying distributed force load calculated using the dynamic multi-point equivalent method proposed in this study, and the thermal load was applied through conductive heating using the thermal loading unit. The wingtip deformation during the movement of the multi-stage telescopic wing was recorded under different loading conditions and compared with the simulation results. The comparison results of the experiment and simulation are shown in Figures 36 and 37. The deformation trends of the wingtip under all loading conditions were similar to the simulation results, but the deformation trends in the experiment were somewhat larger than those in the simulation. The reason for this difference is that, during the actual installation of the experimental setup, there were certain installation gaps between components. This resulted in the loading time gaps, causing some motion in the wingtip, which led to a certain deformation error and, thus, a reduction in the stiffness in the experimental tests.  
The equivalent loads obtained using the dynamic multi-point distribution method can, to some extent, fit the wingtip deflection deformation behavior.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 3 Methods
### Wavelength Scanning  
When using a fixed diffraction grating for broadband spectral detection, diffraction grating spectrometers exhibit response roll-off and diffraction order overlap issues in spectral edge regions [10; 39; 40]. Through wavelength scanning, the mount produces spectra over a wide wavelength range while maintaining uniform wavelength resolution. The mirror not only reflects light but also selects the wavelength. It reflects the diffracted light of different wavelengths at various reflection angles; however, only rays perpendicular to the mirror plane return along the same path. When the grating rotates about its central groove, the wavelength perpendicular to the mirror varies with the grating angle. Let the initial working wavelength be denoted as \(\lambda_{0}\), with the corresponding incidence angle \(\alpha_{0}\) and diffraction angle \(\beta_{0}\). After rotating the grating by an angle \(\delta\), the new working wavelength \(\lambda\) and its associated angles are shown in Figure 5a. The diffraction equations before and after grating rotation are given by:  
\[\begin{array}{l}d(\sin\alpha_{0}+\sin\beta_{0})=m\lambda_{0}\\ d[\sin(\alpha_{0}-\delta)+\sin(\beta_{0}-\delta)]=m\lambda\end{array} \tag{13}\]  
Figure 4: Schematic of MSDM mount: (**a**) Top view of approach. FM, first monochromator; SM, second monochromator; F, fiber; S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), slits; L\({}_{1}\), L\({}_{2}\), lenses; M, M\({}_{1}\), M\({}_{2}\), M\({}_{3}\), mirrors; G, grating; D, detector. (**b**) Rays of FM on grating. (**c**) Rays of SM on grating.  
The wavelength selection formula for the double monochromator is derived by solving these equations simultaneously:  
\[\lambda=\lambda_{0}+d[\sin(\alpha_{0}-\delta)-\sin\alpha_{0}]+d[\sin(\beta_{0}- \delta)-\sin\beta_{0}] \tag{14}\]  
Thus, the mirror could select light with a wavelength moving in the expected direction. The grating rotation process completes the wavelength scanning of the monochromator.  
By selecting 1450 nm as the initial wavelength \(\lambda_{0}\) and substituting it into Equation (14), the wavelength scanning process can be graphically represented as a \(\lambda-\delta\) plot, as shown in Figure 5b. Through this rotational scanning protocol, the spectral detection range of the double monochromator is extended to 1250-1650 nm.
## 3 Analyze Results and Discussion
### Comparison of Analytical Results with Field Observations Reported by [PERSON] et al. [3]  
[PERSON] et al. [3] explicitly reported the number of collapsed buildings in the province of Kahramanmaras based on their field observations conducted after the earthquake. According to their sources [3] and the Kahramanmaras and Hatay Earthquake Reports [31], the total number of residual buildings in the province of Maras is understood to be 243,153 [32]. The percentage of reinforced concrete buildings in Turkey is 86.7% of the total building stock according to the Kahramanmaras and Hatay Earthquake Reports, totaling of 210,814 reinforced concrete structures. Among these, buildings constructed between 1981 and 2000 constitute 26.9% of the total (amounting to 56,709 old buildings), while buildings constructed after 2001 account for 58.1% of the total (amounting to 122,483 new buildings). The present study was conducted with the aim of analytically demonstrating that these observed collapse ratios are not consistent with current code requirements, thereby providing evidence of significant code noncompliance within the existing building stock. To fulfill this purpose, the number of 48,756 collapsed or severely damaged buildings in the official data (which is not divided into old and new) was compared with the analytically calculated rates. The results obtained from this analysis indicate that 17.17% (7.57% + 9.60%) of the old buildings and 6.82% (4.86% + 1.96%) of the new buildings were heavily damaged or destroyed. When these percentages are applied to the 56,709 old buildings and 122,483 new buildings in the province of Kahramanmaras, it is found that approximately 9737 old buildings and 8353 new buildings were heavily damaged or destroyed, totaling 18,090 buildings. Unfortunately, this figure does not come close to the actual situation of 48,756 buildings being severely damaged or destroyed. Although the second earthquake also contributed to the higher number of collapsed buildings in reality, this alone does not fully explain the difference. Although the calculation itself is straightforward, this comparison was conducted to emphasize the destructive implications of the ratios presented in Section 3.1, which were calculated to be nearly twice as high.
## 2 Analytical Solution for Birdcage RF coil
### Dominant Resonance Path
As for the low pass coil, capacitors are present only in the legs, for high pass only in the end rings while for the band pass in both locations, so the relationship to determine the total capacitance in each case can be given as follows,  
\[\text{Band Pass }\ C_{T}=\frac{C_{L}\cdot C_{ER}}{NC_{L}+2C_{ER}} \tag{7}\]  
\[\text{Low Pass }\ C_{T}=\frac{C_{l}}{2} \tag{8}\]  
Figure 3:0  
The lumped capacitors required in the legs and end-rings of the low pass and high pass birdcage RF coil can easily be determined using Equations (8) and (9) respectively for the given coil dimensions and required resonance frequency. However, for the band pass birdcage coil, the end-ring capacitor value is usually needed to be assumed while the leg capacitor value is calculated using Equation (7). Moreover, there exist different combinations of leg and end-ring capacitors for the band pass birdcage coil and the one which causes more homogeneous magnetic field distribution is selected.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions
### Development of Realistic Training Environments and State Space Modeling  
A critical aspect of our methodology was creating a highly realistic RL training environment through meticulous state space identification. This ensured the accurate initial training of neural networks and subsequent fine-tuning of the actual system. Incorporating engine operating time into the RL observation space significantly improved pressure regulation time compared to fixed-parameter PID control.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 2 The Case Study
### Case Study Description and Material  
The study aims to analyze parameters in a 3D structure, so the initial design space needs reliable dimensions in the x, y, and z directions. The typical load case involves minimizing compliance with a volume fraction constraint in a cantilever beam with a vertically applied distributed force. In this study, a point force (tip load) is used to emphasize the 3D nature of the case.  
Considering that the element size is 1 in all directions, the initial design space consists of 80 elements in the x direction, 30 elements in the y direction, and 20 elements in the z direction. This choice helps generate fewer flat shapes in the optimization result. Figure 3 illustrates the case study with the applied load and the number of elements.  
For this study, a volume fraction of 0.2 is applied, and a material with a Poisson Ratio (v) of 0.3 is used. The value of E\({}_{\text{min}}\) in Equation (1) is set to \(10^{-9}\). It is close enough to zero to avoid affecting calculations and prevent mathematical singularities. E\({}_{0}\) is set to 1, which is a typically used value in the mentioned previous studies for researching and reducing calculation time. These properties are chosen for comparing the results to other studies [1; 26] and having a direct application in the known and accessible MatLab\({}^{\circledR}\) codes [6; 7].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline R & 1.5 & 1.8 & 2 & 2.45 & 2.5 & 2.9 & 3 & 3.4 & 3.47 & 3.5 & 3.8 & 4 \\ Reason & OS & ED & ED & ED & ED & ED & OS & ED & ED & ED & ED \\ \hline R & 4.2 & 4.4 & 4.5 & 4.7 & 5 & 5.2 & 6 & 8 & 10 & 15 & 20 & \\ Reason & ED & ED & ED & ED & ED & ED & OS & LV & LV & LV & LV & \\ \hline \end{tabular}
\end{table}
Table 1: Chosen study filter radii, the reason of the choice of these radii, and the percentage of the elements in the maximum direction that represents the radii. OS = Other Studies. ED = Euclidean Distance. LV = Large Values.  
The convergence is achieved when the maximum change in the density of any element between two consecutive iterations is less than 0.01. This is the recommended value based on the experience of previous studies [6].
## 1 Introduction  
The presence of trace metals in aquatic systems is a key factor due to the possibility of affecting or even inhibiting some organisms' biological processes when they are above their toxic levels [1; 2; 3; 4]. Nevertheless, these effects are mainly related to metal species easily assimilated by the organisms, which are commonly known as labile metal fractions. The separation and quantification of these labile species is an essential issue in order to get a better understanding of trace metals' mobility, reactivity, and toxicity in the environment. Some methods have shown their ability to perform this speciation, mainly using electrochemical approaches [5; 6]. However, these techniques focus only on a few elements, mainly Cu, Cd, Pb, and Zn. Non-electrochemical techniques, such as absorption, emission spectroscopy or inductively coupled plasma mass spectrometry, usually require a previous step to separate the species. In this context, solid or liquid extraction and diffusive gradient in thin films (DGT) are usually used [7; 8; 9; 10].  
In the last decades, and based on coupled liquid extraction/re-extraction methods, liquid membranes have appeared as a real alternative to metal separation and speciation in natural waters, since they offer selective transport of different species with an adequate selection of the extracting reagent [11; 12; 13; 14]. In addition, liquid membranes present other advantages such as high preconcentration factors, reduction of matrix effects, and low sample manipulation [15; 16].  
Any speciation study requires an operational definition of the species to be used as a reference after separation and/or determination of species [17]. Most researchers tend to compare their results with a well-established analytical method which has shown its applicability for speciation studies [13; 18; 19], an equilibrium model based on theoretical data of complexes and their stability constants [5; 20; 21; 22; 23] or both [24; 25].  
In liquid membranes-based speciation studies, the labile fraction is related to the fraction capable of crossing the membrane and then, it can be considered the bioavailable fraction [14; 26]. Depending on the experimental setup it can include the free ion or the free ion and mobile and labile metal complexes [27].  
In this work a liquid membrane system was applied to the speciation of nickel in real natural waters using an hydrazone derivative, 1,2-cyclohexanedione bis-benzoyl-hydrazone (1,2-CHBBH), dissolved in toluene (2% \(N\),\(N\)-dimethylformamid (DMF)). This reagent has previously shown its applicability to separate and quantify nickel fractions in natural water [28]. The results obtained were compared with those obtained from well-established methods as well as with the theoretical speciation data calculated with WinHumic V software.