esa-sceva/satcom-chunk-collection huggingface dataset:
Modelling Flexibility and Qualification Ability to Assess Electric Propulsion Architectures for Satellite Megaconstellations  
[PERSON]  
1 Department of Industrial and Materials Science, Chalmers University of Technology, Chalmersplatsen 4, 412 96 Gothenburg, Sweden; [EMAIL_ADDRESS] (O.B.); [EMAIL_ADDRESS] (O.I.)1  
[PERSON]  
1 Department of Industrial and Materials Science, Chalmers University of Technology, Chalmersplatsen 4, 412 96 Gothenburg, Sweden; [EMAIL_ADDRESS] (O.B.); [EMAIL_ADDRESS] (O.I.)1  
[PERSON]  
1 Department of Industrial and Materials Science, Chalmers University of Technology, Chalmersplatsen 4, 412 96 Gothenburg, Sweden; [EMAIL_ADDRESS] (O.B.); [EMAIL_ADDRESS] (O.I.)1  
Footnote 1: email: {marc@sic.sic.  
Nevertheless, such cost rationalization approaches impose new challenges for manufacturers of electric propulsion systems. In relative comparison, constraints related to the manufacturing, testing and operation of the product (e.g., manufacturability, flexibility, qualification ability) play a higher role in these new applications compared to traditional businesses based on low production volumes ([8], point C in Figure 1). These constraints are often labelled as _non-functional requirements_. While _functional requirements_ specify \"what the product must do\" [9] (e.g., thrust), _non-functional requirements_ constrain \"how\" the system must accomplish the \"what\" [10]. They are often referred to as \"-ilities\" (e.g., manufacturability and flexibility) or \"-ities\" (e.g., integrity and security) [11]. Due to their tacit and articulated nature [10; 12; 13], non-functional requirements represent hidden sources of cost and increases in lead time. As a consequence, the difference between the target cost and the real cost of these products once produced in large volumes can be large (point B and C in Figure 1).  
These considerations drive the need to analyse and mitigate non-functional requirements already from the preliminary design stages. Emerging from a study in a real space development project, this paper demonstrates how two key non-functional requirements--flexibility and qualification ability--can be modelled and traded against functional requirements. A methodology for assessing flexible propulsion architectures has been proposed--which puts strong emphasis on including variations in market behavior and qualification activities during architecture trade studies. The use of the methodology is illustrated through an industrial case related to the study of xenon vs. krypton architectures for megaconstellation businesses.  
The following sections review more in detail the current research related to the modelling of non-functional requirements, emphasizing specifically research on flexibility and qualification ability. The need for a deeper focus on these two attributes of a design has been stressed by the industrial partners involved in the empirical study (and described in chapter 2).
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 5 QAOA Mappings: Orderings and Schedules
### Example: Single Machine Scheduling (SMS), Minimizing Total Squared Tardiness  
**Problem**. \((1|d_{j}|\sum w_{j}T_{j}^{2})\). Given a set of \(n\) jobs with processing times \(\mathbf{p}\), deadlines \(\mathbf{d}\), and weights \(\mathbf{w}\), find a schedule minimizing the total weighted squared tardiness \(\sum_{j=1}^{n}w_{j}T_{j}^{2}\). The tardiness of job \(j\) with completion time \(C_{j}\) is defined as \(T_{j}=\max\{0,C_{j}-d_{j}\}\). Here, we took all quantities to be integers.  
The configuration space and domain are the set of all orderings of the jobs. Given an ordering \(\iota\) of the jobs in which job \(i\) is the \(\sigma_{i}\)-th job to start, the corresponding schedule \(\mathbf{s}(\iota)\) is that in which each job starts as soon as the earlier jobs finish: \(s_{j}(\iota)=\sum_{i=1}^{\sigma_{j}-1}p_{i_{i}}\).  
For a job \(i\) starting at time \(s_{i}\), consider the expression:  
\[\min_{y_{i}\in[0,d_{i}-p_{i}]}\left(s_{i}+p_{i}-d_{i}+y_{i}\right)^{2}=\begin{cases} (s_{i}+p_{i}-d_{i})^{2},&s_{i}+p_{i}>d_{i},\\ 0,&\text{otherwise}.\end{cases} \tag{59}\]  
When the \"slack\" variable \(y_{i}\in[0,d_{i}-p_{i}]\) is minimized, this expression is equal to the square of the tardiness of job \(i\). Therefore, we recast SMS as the minimization of:  
\[f(\iota,\mathbf{y})=\sum_{i=1}^{n}w_{i}(s_{i}(\iota)+p_{i}-d_{i}+y_{i})^{2} \tag{60}\]  
over the configuration space of orderings \(\iota\) and slack variables \(\mathbf{y}\).  
Using the direct one-hot encoding defined in Section 5.1.2, in which \(x_{j,\alpha}\) indicates that job \(j\) is the \(\alpha\)-th to start, this is equivalent to :  
\[f(\mathbf{x},\mathbf{y})=\sum_{i=1}^{n}w_{i}(s_{i}(\mathbf{x})+p_{i}-d_{i}+y_{ i})^{2}, \tag{61}\]  
where:  
\[s_{i}(\mathbf{x})=\sum_{\alpha=2}^{n}x_{i,\alpha}\sum_{j\
eq i}p_{j}\sum_{ \beta=1}^{\alpha-1}x_{i,\beta}. \tag{62}\]  
Note Equation (61) may seem to be quartic; however, the encoding constraints \(\sum_{i}x_{i,\alpha}=\sum_{\alpha}x_{i,\alpha}=1\) that come with the direct one-hot encoding imply that the quartic terms disappear in the full expansion. The objective function is thus a cubic pseudo-Boolean function, which corresponds to a 3-local diagonal Hamiltonian for the phase separator.  
Mixer and initial state. We used the same initial state preparation, and the same mixer as in TSP for mixing the ordering, in addition to any of the single-qudit mixers from Section 4.1.1 for each of the slack variables. Because the ordering and slack mixers act on separate sets of qubits (\(\mathbf{x}\) and \(\mathbf{y}\)), they can be implemented in parallel. Note that the only requirement for the upper bound of the range of the slack variable \(y_{i}\) is that it be _at least_\(d_{i}-p_{i}+1\). In particular, it could be \(2^{\lceil\log_{2}(d_{i}-p_{i}+1)\rceil}\), allowing us to use the binary encoding without modification.
# RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation
## 3 Methods
### RSVQ-Diffusion Model
#### 3.1.1 VQ-Diffusion Model Network Architecture
Additionally, remote sensing images typically exhibit more complex and variable backgrounds, significantly influenced by climatic, geographic, and temporal factors. Moreover, the types and sources of noise and interference (e.g., atmospheric effects and sensor errors) in remote sensing images differ greatly from those in general images. Therefore, the generation and processing of remote sensing images demand higher technical requirements and more complex model designs. This paper primarily improves the VQ-Diffusion model by focusing on the spatial characteristics and local details of remote sensing images. The model is then trained on a remote sensing image dataset to enhance its capability in generating high-quality remote sensing images.
# Design of a System for Verification of Automatic Guided Vehicle Routes Using Computer Emulation
## 2 Materials and Methods
### Designing a System for Verification of AGV Routes Using Computer Emulation
#### 2.1.1 Structure of the Module Representing Real Logistical Elements
Communication between the host computer and the AGV control module is ensured in this instance (Block 1.5). The host computer is connected. The status in the internal database is checked to see if it contains a logistical task request (Block 1.2). Where such a requirement exists, it shall be included in the list of transport tasks to be carried out (Block 1.7). This list collects information about inventory manufacturing and tracking requirements at individual logistics hubs. The information sent to the to-do list is personalised for a specific logistics node or logistics system, depending on the difficulty of the logistics system's tasks. Based on this information, the process of prioritising individual tasks becomes necessary (Block 1.8). The prioritisation system is set up individually according to user-defined rules. The rules thus defined may consider the interconnections of individual logistics hubs for the maximum utilisation of logistical elements and the supply of bottlenecks and critical points to ensure continuous manufacturing. After creating a task execution plan, communication is established between the host computer and the already-selected logistics equipment that will perform the task (Block 1.9). Suppose the device is not available, either due to a malfunction or to performing another task (Block 1.10). In that case, the control system returns to the task prioritisation step, where a new logistical task is selected for another available device (Blok 1.8). After ensuring the availability of the logistics equipment, the process of performing the logistical task is initiated (Block 1.11). If the logistical task has not been fulfilled (Block 1.12), the location of the logistics element is necessary (Block 1.13). This localisation is possible through software solutions that collect movement information based on the RFID tag and real-time location system (RTLS). After (Block 1.13), we check the state of the device that was used in (Block 1.13.1-1.13.5). If the logistics task was performed, the data collection process is started (Block 1.14), which is also personalised according to the client's requirements. This might contain information such as the state of the logistical means, statistics on the length of the logistical task, and inventory monitoring in particular logistics nodes. The information gathered in this manner is subsequently translated into a user-friendly format (Block 1.15) and transferred to an internal database for analysis (Block 1.16).
# Prediction of the Absorption Characteristics of Non-Uniform Acoustic Absorbers with Grazing Flow
## 3 Impedance Boundary Navier-Stokes Equations (IBNSE) Method
### Comparisons of Different Impedance Models  
The impedance characteristics of the perforated plate were calculated using the above four semi-empirical models. The real part \(\mathrm{Re}(z_{M})\) and imaginary part \(\mathrm{Im}(z_{M})\) of the relative specific surface impedance of perforated plate \(z_{M}\) (unit: 1) were compared as a function of hole diameter, perforation rate, Mach number and frequency. The relative surface impedance \(z_{M}\) is expressed as  
\[z_{M}=\frac{S_{0}}{\rho_{0}c_{0}}Z_{M} \tag{23}\]  
where \(S_{0}\) is the area of the MPP and \(Z_{M}\) is the impedance of the perforated plate.  
The basic parameters used for the comparison are \(d=0.8\) mm, \(\sigma=8.04\%\), \(M=0.1\) and \(f=3000\) Hz. The acoustic pressure level was set as 120 dB. According to the report from the MPP manufacturers [44], the hole diameter and the panel thickness are usually equal due to manufacturing limitations. Therefore, in this study, the hole diameter \(d\) and the thickness of the MPP \(t\) were set to \(t=d\). In each round of simulation, only one of the above four parameters was allowed to change, and the other parameters remain unchanged. The comparison results are shown in Figure 2.  
Figure 2: _Cont._  
It can be seen from Figure 2 that the impedance characteristics predicted by the four semi-empirical models were very different, especially the real part of the impedance. In the influence of frequency on impedance, the four models gave very similar results for the imaginary part of impedance. The sound absorption performance of the absorber depends on the sum of the MPP impedance and the cavity impedance. When the real part of the sum of impedances is 1 and the imaginary part is 0, the theoretical sound absorption coefficient reaches 1 and, in this case, the perfect sound absorption occurs. Since there is no exact theoretical formula for the impedance of complex cavities in grazing flow, the sound absorption characteristics of the whole structure can be calculated by combining simulation of cavities and a semi-empirical theoretical model of MPP. The next section will show that the proper selection of the semi-empirical impedance models in the IBNSE method can produce an accurate prediction of the transmission loss characteristics of the acoustically treated duct system under grazing flow.
## 3 Results and Discussion
### Bioactivity of the Microalgae Extracts
#### 3.4.2 Antimicrobial Activity of Microalgae Extracts Against Bacteria
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Gram\(-\)**} & \multicolumn{3}{c}{**Gram\(+\)**} \\ \cline{2-7}
**Microalgae** & & & & _Salmonella_ & & \\
**Species** & _Escherichia coli_ & _Yersinia_ & _Terseinia_ & _Terseinia_ & _Staphylococcus aureus_ & _Bacillus_ & _Listeria monocytogenes_ \\  & & & & & & \\  & & & & & & \\ \hline _Limospira_ sp. & 2.5 & 1.25 & 1.25 & 2.5 & 2.5 & 1.25 \\ _Dunaliella_ sp. & \(>\)5 & \(>\)5 & \(>\)5 & 2.5 & \(>\)5 & \(>\)5 \\ _Lobsphaera_ sp. & 2.5 & 2.5 & 1.25 & 5 & 5 & 1.25 \\ _Nannochloropsis_ sp. & 1.25 & 0.63 & 1.25 & 1.25 & \(>\)5 & 1.25 \\ _Odontella_ sp. & 2.5 & 2.5 & 2.5 & 2.5 & 1.25 & 2.5 \\ _Porphyridium_ sp. & \(>\)5 & \(>\)5 & \(>\)5 & \(>\)5 & \(>\)5 & \(>\)5 \\ _Tetraselmis_ sp. & 5 & 2.5 & 2.5 & 2.5 & \(>\)5 & 2.5 \\ \hline \hline \end{tabular} Three replicates.  
\end{table} 
Table 6: Minimum inhibitory concentration (MIC, mg/100 uL) of the microalgae extracts against selected Gram\(-\) and Gram+ bacteria.
# Graphene Oxide Adsorption Enhanced by Attapulgite to Remove Pb (II) from Aqueous Solution
## 3 Results and Discussion
### Adsorption Isotherm  
The Langmuir isotherm equation [41] and the Freundlich isotherm equation [42] were used to fit the equilibrium of the experimental data, as shown in Figure 8 and Table 1:  
\[q_{\mathrm{e}}=\frac{q_{\mathrm{max}}K_{\mathrm{L}}C_{\mathrm{e}}}{1+K_{ \mathrm{L}}C_{\mathrm{e}}} \tag{4}\]  
\[q_{\mathrm{e}}=K_{\mathrm{f}}C_{\mathrm{e}}\frac{1}{\hbar} \tag{5}\]  
where \(C_{\mathrm{e}}\) is the equilibrium concentration in mg/L, \(q_{\mathrm{e}}\) is the equilibrium adsorption capacity in mg/g, \(k_{\mathrm{L}}\) is the constant of the Langmuir model in L/mg, \(q_{\mathrm{max}}\) is the maximum theoretical adsorption capacity in mg/g, and \(K_{\mathrm{f}}\) and \(n\) are the adsorption capacity and adsorption strength of the Freundlich model, respectively.  
Figure 7: Adsorption kinetics of Pb\({}^{2+}\) on the as-prepared GO, \(C_{\mathrm{0}}\) = 150 mg/L, pH = 5.0, m/v = 0.3, and T = 298 K.  
By nonlinear fitting, the fitting parameter \(q_{\rm max}\) of the Langmuir isothermal adsorption equation is 450.9 mg/g with \(k_{\rm L}\) = 0.147 L/mg and \(R^{2}\) = 0.9925, while the \(R^{2}\) of Freundlich is 0.9127 with \(k_{\rm F}\) = 116.5 mg/g and \(n\) = 3.005. This result indicates that the adsorption process is in better agreement with the Langmuir isothermal adsorption equation than with the Freundlich equation and that the adsorption occurs through homogeneous monolayer adsorption. The maximum adsorption capacity is 450.9 mg/g.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 5 Performed Experiments
### Two-Level Discretisation  
As could be observed above in the reported research results, supervised discretisation can return such variants of the data where noticeably many input features are treated as useless in a discrete domain. Also, unsupervised algorithms can lead to more advantageous performance of classifiers working on the discretised datasets. These observations provided motivation for a more detailed study which combined both approaches. In the proposed research framework, the fundamental notion was to get more out of variables than supervised discretisation could offer. Therefore, for all features, for which through supervised processing only single intervals were found to represent their values, an additional transformation step was executed this time employing unsupervised methods. Consequently, in a discrete domain, all variables had at least two bins assigned, and all could be mined in data exploration processes. Unsupervised discretisation algorithms were used with varying the input parameter that defined the number of bins requested, and they were paired with both the Fayyad and Irani and Kononenko discretisation methods, which resulted in multiple versions of the datasets. All these variants were next mined with the group of selected classification systems, and their performance was observed. Some inducers showed similar trends, while others varied greatly. In the presented results, the bin number equal one denotes the situation where a dataset was transformed only by supervised methods; thus, some variables were assigned single intervals. When the number given is greater than one, it means additional transformations by some unsupervised algorithm, and then the number corresponds to the input parameter. The combination of the Fayyad and Irani method with equal width binning was denoted as dsF-duw and with equal frequency binning as dsF-duf. In the same convention, dsK-duw and dsK-duf stand for the combination of the Kononenko algorithm with equal width binning and equal frequency binning, respectively. BNet stood out from the other learners. Its performance was included in Table 6. For the female and male writer dataset, for all combinations of supervised and unsupervised methods, the change in performance was observed only with varying the bin number in the test sets. Changing the number of intervals in the training sets with the constant number of bins in the test sets brought the same accuracy. For each pairing, it can be noted that additional transformations by unsupervised discretisation algorithms caused increased performance. For F-writers, the trends were monotonic; for M-writers, they were close to monotonic.
## 2 Flared SBF Antenna
### Numerical Analysis
#### 2.2.1 Effect of the Flaring Angle
This increase in gain, as discussed in previous sections, is a result of having a better impedance matching at the edge of the antenna's aperture due to reduced back reflection of waves. From the plots in Figure 2, it can be observed that increasing the flaring angle further from 20\({}^{\circ}\) to 40\({}^{\circ}\) does not improve the gain. There was a small gain drop from 17.2 dBi to 17.1 dBi when the flaring angle was increased to 25\({}^{\circ}\), then the realized gain can be seen to plummet when the flaring angle was increased to 40\({}^{\circ}\) from 17.1 dBi to 14.3 dBi. This is mostly due to an increase in the phase error due to over-flaring. The 20\({}^{\circ}\) flaring angle also produced the widest gain bandwidth of 1.1 GHz or 21% (4.7 to 5.8 GHz) using the 3 dB gain definition. From these observations, it can be deduced that the optimum flaring angle to achieve the highest gain for this design is 20\({}^{\circ}\). Figure 3c shows the worst-case cross-polarization ratio at 0 = 45\({}^{\circ}\). The results show that increasing the flaring angle from 0\({}^{\circ}\) to 40\({}^{\circ}\) generally increases the cross-polarization ratio. The minimum cross-polarization ratio for the unflared antenna is \(-\)24 dB at 5.6 GHz, while the minimum cross-polarization ratios for the flared antenna are \(-\)23.27 dB (5.8 GHz), \(-\)21.32 dB (5.6 GHz), \(-\)19.43 dB (5.4 GHz), and \(-\)14.84 dB (5.4 GHz) at 0f = 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\), respectively. Although this study shows an increase in the overall cross-polarization ratio, we found that at lower flaring angles such as 10\({}^{\circ}\) and 20\({}^{\circ}\), the increase is not as significant as those of higher flaring angles (30\({}^{\circ}\) and 40\({}^{\circ}\)). The performance of the flared antenna is summarized in Table 2. The impedance bandwidth increased as the flaring angle increased, having the highest value at 0f = 15\({}^{\circ}\) and then this value started to decrease gradually. Gain improvement was also observed with increasing flaring angle until \(\mathrm{\SIUnitSymbolFace{f}}=20^{\circ}\). The cross-polarization ratio decreased as the flare angle was increased. From the simulation results and observations, the optimum flaring angle for this design is at 0f = 20\({}^{\circ}\). Although the highest bandwidth was obtained at 0f = 15\({}^{\circ}\), the obtained gain at the same angle is \(\sim\)0.5 dB less than that of 0f = 20\({}^{\circ}\), while the bandwidth at 0f = 20\({}^{\circ}\) is lower than the highest bandwidth by just 0.6% (0.06 GHz). The difference in impedance bandwidth is very minimal compared to the gain difference, therefore we chose the flared SBF antenna with 0f = 20\({}^{\circ}\) as the optimum design.
# Air Quality Improvement in Urban Street Canyons:
## 2 Materials and Methods
### Modeling of Pollutant Concentrations in the Air  
Calculations of air pollutant concentrations were performed using the microscale dispersion OSPM model, dedicated to the assessment of air quality in street canyons. This model is characterized by high precision in predicting concentration levels, which has been widely discussed in numerous studies [36; 37; 45; 46; 68; 69; 70; 71]. The OSPM is a combination of the Gaussian plume model and the box model, which makes it possible to model direct and recirculation effects, taking into account the pollution background [35; 72]. In this model, it is assumed that air pollutants caused by vehicle traffic accumulate on the leeward side of a street canyon (recirculation vortex effect) and concentrations in the recirculation zone are calculated based on the assumption that the inflow of air pollutants is equal to its outflow. In addition, simple mechanisms of chemical transformation of NO\({}_{\rm x}\) have been implemented in the model, depending on the ozone (O\({}_{3}\)) concentration and the intensity of solar radiation [37], and hence, information about the background of O\({}_{3}\) and appropriate meteorological data are required. However, the processes of homogeneous nucleation, coagulation, condensation, evaporation or wet and dry deposition are not taken into account, which may be of some importance in modeling concentrations in the air, for example, of particulate matter [73].  
Pollutant concentrations were calculated with a 1-h time step at two extreme points of the cross section located in the center of a given street canyon at a height of 1.5 m above ground level for the BROD, SOLI and LIMA canyons, and at a height of 2.5 m above ground level for the KRAS canyon, and then they were averaged. The slightly greater height of the calculation points for the KRAS canyon resulted from the need to adjust it to the height of the air intake at the air quality monitoring station located in this canyon in order to validate the model used.  
The validation of the OSPM model was carried out by comparing the modeling results performed for the baseline variant in the KRAS street canyon to the results of air pollutant (NO\({}_{2}\), NO\({}_{\rm x}\), PM\({}_{10}\) and PM\({}_{2,5}\)) concentration measurements observed in 2017 at the traffic station located in the center of this canyon. The accuracy of the model was assessed using the following statistical parameters: mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), normalized mean bias (NMB), normalized mean error (NME), fraction of predictions within a factor of two (FAC2), Pearson's correlation coefficient (r) and index of agreement (IOA) [74; 75; 76; 77]. The uncertainty of the OSPM model was taken into account in the assessment of the significance of changes in air pollutant concentrations obtained for the considered variants of road traffic organization as a result of atmospheric dispersion modeling.