esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Validation and Discussion of Land Subsidence Results
### Typical Land Subsidence Areas Discussion  
The time series deformation from 2016 to 2022 shows that the land subsidence in Shanghai is unevenly distributed. The subsidence mainly occurs in some coastal areas and the JS District, and most of the subsidence areas have not shifted. The following section will analyze the settlement results from typical settlement areas, the subway, and other line projects and study the correlation between settlement and rainfall based on the SSA method and the correlation between settlements and human activities.
## 5 Conclusions  
In general, it seems that car and navigation companies have likely not carried out the necessary research to make built-in navigation devices really useful to a broad user base. This research can serve as a starting point. The specific findings are as follows:  
1. Generally speaking, the built-in GPSs currently on the market are overloaded with features. On the one hand, users themselves (regardless of country) seem to require less functionality in the interface, and on the other hand, the existing functions are not fully utilized either.
2. From a cartographic point of view, users desire the display of fewer and fewer objects and need them represented in the simplest possible form. Further simplification may be a necessary as a car navigation system is primarily intended to facilitate driving.
3. The main downside is the price of these built-in devices. Many people also do not pay to update the data. Thus, these built-in devices will become less useful over time.
4. Only a few users take advantage of the more sophisticated features in these devices. Those who do so do it confidently and regularly.
5. Due to the different economic backgrounds in the countries surveyed, there are differences in the desired interface. However, when looking at usage habits, these differences become much more negligible.
6. Navigation software companies should notice that a lot of people use the GPS while driving, especially to enter a new address. It may be worth re-thinking this feature to minimize the potential for accidents.  
An area of future research associated with navigation devices of all types would be whether map-reading skills are declining as users become more dependent on their devices. It may also be the case that the quality or functionality of mental maps is declining. While navigation devices help us move through the spatial environment, they may not help the user conceive of the space they are traversing. If that is the case, humans will become increasingly dependent on them.  
Conceptualization, F.V., G.G., M.P.P. and B.K.; methodology, F.V.; software, F.V.; validation, B.K., G.G. and M.P.P.; investigation, F.V., G.G., M.P.P. and B.K.; resources, G.G. and M.P.P.; data curation, F.V.; writing--original draft preparation, F.V.; writing--review and editing, G.G. and M.P.P.; visualization, F.V.; funding acquisition, F.V. and B.K. All authors have read and agreed to the published version of the manuscript.  
F.V. is supported by the UNKP-21-3 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation fund. B.K. is supported by the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
## 2 Related Work
### Classification by Power Consumption Rate
In the field of academic, commercial, and industrial trends, devices with ARM CPUs, such as the Cortex M based on single-core and dual-core architectures, dominate the spectrum of prototypes used in Space Aerospatial applications, life support medical equipment, army, and industrial robotics, among others [67; 68]. In [69], a set of established promising practices is proposed to reduce power consumption in hardware-constrained embedded systems designed for implementation in ARM Cortex M microcontrollers with single-core and dual-core architecture. Specifically, they propose an implementation in two single-core systems subjected to executing the same set of tasks simultaneously and the same computationally difficult executed tasks. On their scenery, one core operates under optimal stable frequency and voltage conditions, while the two single-core systems have a core subjected to executing the same algorithm under unstable operating frequency and voltage conditions. The results show that, although there is an imminent imbalance in the operating conditions, the ARM Cortex M architectures are robust and manage to compensate for anomalies and achieve similar energy consumption in both cores. Arduino Portenta H7 has a Cortex M type CPU, specifically the 32-bit dual core STM32H747 XI (M7 + M4), which makes it a candidate for the implementation of methodologies such as those proposed in [69]. Works such as [70; 71] explore the efficient use of SRAM and flash memory resources within the new RISC-V architectures to optimize power consumption in systems designed under these architectures. The results reveal that the correct use of memory resources does reduce power consumption. It should be noted that the strategy of improving power consumption by optimizing memory use is much easier in RISC-V architectures than in ARM Cortex M. ESP32-S3 has a RISC-V architecture, which gives it the possibility of using the techniques described in the state-of-the-art for optimization of power consumption.
# Comparative Analysis of Viscous Damping Model and Hysteretic Damping Model
## 3 Comparative Analysis of Steady Responses Based on Different Damping Models
### The Steady Response of Viscous Damped System  
Due to the harmonic wave, the time-domain equation for an SDOF system based on viscous damping model is expressed as  
\[m\ddot{x}(t)+\frac{2\xi k}{\omega}\dot{x}(t)+kx(t)=-mp\sin\theta t \tag{20}\]  
where \(\theta\) is vibration frequency of harmonic wave; \(p\) is the vibration amplitude of the harmonic wave.  
Due to the harmonic wave, the steady response of an SDOF system is assumed as  
\[x(t)=C\sin\theta t+D\cos\theta t \tag{21}\]  
where \(C\) and \(D\) are undetermined coefficients.  
The change cure between damping force and displacement is shown in Figure 3. The area of the ellipse is the dissipated energy. Based on the viscous damping model, the dissipated energy in each cycle is  
\[W_{\rm v}=\int_{0}^{\frac{2\pi}{\theta}}\frac{25k}{\omega}\dot{x}(t)\mathrm{d }x(t)=\frac{2\pi\xi k\theta}{\omega}(A^{2}+B^{2}) \tag{22}\]  
Figure 2: The change curve of damped natural frequency ratio.  
According to Equation (22), the dissipated energy e in each period is related to the excitation frequency based on the viscous damping model.  
Substituting Equation (21) into Equation (20), the undetermined coefficients are expressed as  
\[\left\{\begin{array}{l}\mathcal{C}=\frac{-(\omega^{2}-\theta^{2})^{2}P}{( \omega^{2}-\theta^{2})^{2}+4\xi^{2}\omega^{2}\theta^{2}}\\ D=\frac{2\zeta_{\omega}\omega\bar{P}}{(\omega^{2}-\theta^{2})^{2}+4\xi^{2} \omega^{2}\theta^{2}}\end{array}\right. \tag{23}\]  
The vibration amplitude can be expressed as  
\[Q_{\mathrm{v}}=\sqrt{C^{2}+D^{2}}=\sqrt{\frac{P}{\left(\omega^{2}-\theta^{2} \right)^{2}+4\xi^{2}\omega^{2}\theta^{2}}} \tag{24}\]
## 3 Results and Discussion
### Microbial Quality
coli_, so it can be said that the halophytes analysed fulfilled the microbiological criteria of food hygiene according to the European Commission [38; 39]. Results of the present study indicate that soilless cultures using low salinities produce plants adequate for consumption, considering their microbial quality. Antioxidative defence systems, especially non-enzymatic antioxidants (like flavonoids, ascorbate, glutathione, and tocopherols) but also enzymatic antioxidants (like superoxide dismutase, catalase, and glutathione reductase), are part of the mechanisms that enable halophytes to thrive in high salt conditions [3]. They may also be implicated in antimicrobial defence systems in some halophyte species/strains such as _M. nodiforum_ and _S. maritima_. On the other hand, different species of halophytic plants certainly develop distinct adaptive strategies depending on their gene pool and the environment in which they grow. In the case of _D. crassifolium_, hydroxybenzoic acids, which are antimicrobial and antifungal agents, were not detected, thus justifying the increase in the number of filamentous fungi, with increasing salinity in the plants studied (Table 1). In addition, this species has been described as having an efficient osmolyte accumulation system [40], and we hypothesise that these osmolytes constitute food reserves for some microbial groups (filamentous fungi). The present study's results indicate that soilless cultures using low salinities produce plants adequate for consumption, considering their microbial quality. However, the growth of the microbial populations during storage was not evaluated, and thus, the shelf-life of plants at different temperatures, including refrigeration, was not evaluated.
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 4 Results and Discussion
### Economic Analysis and Discussion of Road Classification  
The primary objective of road classification is to enhance the economic efficiency of truck transportation by reducing fuel consumption. To analyze the economic benefits of the proposed fuel-efficient road classification method, two additional haul roads in the Haerwusu open-pit coal mine were selected (Figure 9). Two roads with relatively smooth southern benches were chosen for the case study. The testing involved measuring the IRI of these two roads in segments at 150 m intervals, and the relevant road selections and statistical data are presented in Figure 14. For both loaded and unloaded trucks, the selected roads were evenly distributed between \"medium\" and \"poor\" quality roads, with only a limited number of \"good\" quality roads. The fuel price was calculated as CNY/L 7.4. Fuel costs were calculated for every 150 m road interval. The IRI values calculated every 150m for the two road sections, the unit fuel consumption computed according to Equations (3) and (4), and the total fuel costs of the roads are shown in Table 7. The findings show that loaded trucks incur a cost of CNY 420.05, while unloaded trucks cost CNY 311.26. \begin{table}
\begin{tabular}{c c c} \hline \hline  & Good & Moderate & Poor \\ \hline IRI & 0–15 & 15–21 & 21 and above \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fuel-efficient road classification for unloaded trucks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Clusters** & **Cluster Center** & **Distance Between Classes** & **Intra-Class Distance** \\ \hline \multirow{3}{*}{3} & 1403 & - & 43 \\  & 1436 & 33 & 46 \\  & 1498 & 62 & 56 \\ \hline \multirow{3}{*}{4} & 1399 & - & 33 \\  & 1420 & 21 & 23 \\  & 1450 & 30 & 30 \\  & 1498 & 48 & 56 \\ \hline \multirow{3}{*}{5} & 1397 & - & 29 \\  & 1415 & 18 & 19 \\ \cline{1-1}  & 1434 & 19 & 25 \\ \cline{1-1}  & 1473 & 39 & 38 \\ \cline{1-1}  & 1515 & 42 & 31 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Clustering results for unloaded trucks. \begin{table}
\begin{tabular}{c c c c} \hline \hline Range (L/100 km) & 0–1420 & 1421–1470 & Greater than 1470 \\ \hline Classification & fuel-efficient & moderate & fuel-intensive \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fuel consumption classification for unloaded trucks. For comparison, if the road quality of the two sections is upgraded from poor to medium, the fuel costs for trucks under loaded and unloaded conditions would decrease to 407.57 CNY and 305.44 CNY, respectively. For loaded trucks, the medium-quality upgraded road adopts an intermediate IRI value of 16, while for unloaded trucks, the IRI is set at 18. The computed fuel costs following road quality upgraded are presented in Table 8. The highlighted sections represent the road segments where the quality was improved from poor to medium. The results demonstrate that the fuel costs for loaded and unloaded conditions decreased to CNY 407.57 and CNY 305.44, respectively, representing cost savings of 3% and 2%.
## 3 Results and Discussion
### Technical and Economic Evaluation by Process Stage
#### 3.1.2 Technical and Economic Evaluation for the Primary and Secondary Harvest Stage
At this stage, the removal efficiency of 90% was set for all technologies, for this reason, we observed the same biomass production for all cases, 69.957 tons/year (Table 4). However, centrifugation could have a biomass recovery greater than 90%, which would contribute to higher biomass production for the case that considers this technology. Furthermore, the speed of harvest and the efficiency of this technique makes it one of the ideal units for collection [31,41,50]. According to the results, centrifugation technologies (case D1) and vacuum filters (case D2) are the technologies with the highest energy consumption for secondary harvest, 16,773 kWh/year and 9027 kWh/year, respectively. Centrifugation requires a high amount of energy for its operation and a high maintenance cost [11,58]. In the application of filtration, it is necessary to maintain a pressure drop through the system to force the flow through a membrane or filter, and this could cause high energy consumption (depending on the type of filtration technology) [53], such as is the case of vacuum filtration. While the belt filter press technologies (case D3) and pressure filters (case D4) are the ones with the lowest energy consumption, 941 kWh/year and 1399 kWh/year, respectively. The operating costs are estimated at US $76,000/year for centrifugation in case D1, US $15,000/year for vacuum filter in case D2, US $71,000/year for the belt filter press in case D3, and US $15,000/year for the pressure filter in case D4. Operating costs consider the costs associated with the demand for raw materials, labor, services and energy, maintenance of equipment and other expenses that may occur in the process; this evaluation does not consider costs for taxes, since in most cases, these will depend on the place where the process is physically installed. The belt filter press is a low \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Case C1** & **Case C2** & **Case C3** \\ \hline Biomass Production (tons/year) & 77.73 & 78.91 & 78.91 \\ Annual Flocculant Consumption & 2.59 & 0.63 & 0.754 \\ (tons/year) & & & \\ Material Cost & & & \\ (US \$/year) & 1191 & 544 & 1579 \\ Energy Consumption (kWh/year) & 5720 & 5720 & 5721 \\ Operating Cost (US \$/year) & 7000 & 6000 & 7000 \\ Unit Production Cost (US \$/kg) & 0.09 & 0.07 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Results for Primary Harvest (Flocculation). energy consumption technology, but its operating cost is high due to the high cost of this equipment. This implies an increase in the costs associated with maintenance and a longer operating time required to operate the case D3. The selection of technologies to consider in the biomass harvest will depend to a large extent on the species of microalgae that is used, since the separation efficiency can differ considerably from one species to another [58]. Therefore, it is of interest to investigate, develop and evaluate technologies for the collection of the biomass.
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
## 2 Integrated Error Model of 3-Axis MTs  
### Geometric Error Sources of 3-Axis MTs  
The geometric errors of MTs consist of position-dependent geometric errors (PDGEs) and position-independent geometric errors (PIGEs) [18]. For a 3-axis MT, there are three linear guideways. PDGEs are six-dimensional position errors caused by the motion axis, including 3 translation errors and 3 angle errors [19]. Taking the motion of the X-axis as an example, the 6 geometric errors are expressed as \(\delta_{x}(x)\), \(\delta_{y}(x)\), \(\delta_{z}(x)\), \(\varepsilon_{x}(x)\), \(\varepsilon_{y}(x)\), and \(\varepsilon_{z}(x)\)[20]: where \(\delta_{x}(x)\) is the positioning error; \(\delta_{y}(x)\) and \(\delta_{z}(x)\) are the straightness errors; \(\varepsilon_{x}(x)\), \(\varepsilon_{y}(x)\) and \(\varepsilon_{z}(x)\) are the rotations about the \(x\), \(y\) and \(z\) axes under \(x\) motion, which are also called roll, pitch and yaw errors of the X-axis, respectively.  
The physical meaning is shown in Figure 1.  
PIGEs usually appear as non-parallel, non-vertical or non-intersecting axes. For 3-axis MTs, there are three squareness errors \(\alpha_{xy}\), \(\alpha_{xz}\) and \(\alpha_{yz}\) between three axes. They do not vary with the motion of its axis. To sum up, the 21 geometric errors of the 3-axis MT are shown in Table 1.
## 4 Results and Discussion
### PD Controller
The dynamic of \(\psi\) converges in minimum time and consequently, the control signal \(u_{4}\) is kept at a small value. In Figure 13, we can see that the trajectory tracking in \(X\)-\(Y\)-\(Z\) space, evaluating the optimized parameters, is successfully developed. With the obtained parameters, we consider a different circuit. The desired trajectory is sinusoidal for translational dynamics (x and y), and altitude (z) is a step. We found that the QUAV can follow it, as shown in Figure 14. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Statistics** & **PSO** & **GWO** & **HGS** & **ISHADE** & **LSPACMA** & **MPA** & **SMA** & **WOA** \\ \hline Best & 0.037396168112890 & 0.0373620595865019 & **0.037263538506379** & 0.0389976567657 & 0.039741105315137 & 0.0372842298991813 & 0.037835061121072 & 0.0373709474738 \\ Mean & 0.05437635728900 & 0.04954322277539 & 0.045739759656500 & 0.0528989549809 & 0.050922002950839 & **0.038978646154916** & 0.051466828827586 & 0.069892624666992 \\ SD & 0.0181932440893048 & 0.018828735403726 & 0.010205236903067 & 0.01403828646638 & 0.008402296173043 & **0.0018858061376063** & 0.01703656970125065 \\ Worst & 0.047492336225780 & 0.140069842213860 & 0.07452707102758 & 0.00123523418241 & 0.079482210630274 & **0.041018557338656** & 0.107283492612729 & 0.6151708183965657 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental result of RMSE with 20 individuals, 20 evolutions and 30 repetitions. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline
**Statistics** & **PSO** & **GWO** & **HGS** & **ISHADE** & **LSPACMA** & **MPA** & **SMA** & **WOA** \\ \hline Best & 0.037298689969719 & 0.0378366141082 & **0.03720593739661** & 0.0370382913285 & 0.039391  
Figure 5: Convergence curves of RMSE and boxplot distribution of the different algorithms, iterations and individuals. **(a)** 20 individuals, 20 iterations; **(b)** 30 individuals, 20 iterations; **(c)** 40 individuals, 20 iterations. Figure 8: Comparison of tracking performance in Z-axis of the different algorithms evaluating parameters obtained with 20 individuals. Figure 6: Comparison of tracking performance in X-axis of the different algorithms evaluating parameters obtained with 20 individuals. Figure 7: Comparison of tracking performance in Y-axis of the different algorithms evaluating parameters obtained with 20 individuals. Figure 11: Comparison of tracking performance in \(\psi\)–angle of the different algorithms evaluating the parameters obtained with 20 individuals. Figure 10: Comparison of tracking performance in \(\theta\)–angle of the different algorithms evaluating the parameters obtained with 20 individuals. Figure 9: Comparison of tracking performance in \(\phi\)–angle of the different algorithms evaluating parameters obtained with 20 individuals. Figure 12: Signals \((u_{1},u_{2},u_{3},u_{4})\), calculated by PD control optimized with 100 individuals. Figure 13: Tracking performance in three-dimensional space with optimized parameters using HGS algorithm.
## 4 Comparison of Classical and Computer-Aided Reconstruction Results in Case 1
### Procedure of the Comparison Using Dlib-Based Face Recognition
#### 5.1.3 Step 3: Face Feature Vector Extraction  
To facilitate the mathematical comparison of faces, a numerical representation known as face encoding is necessary. These encodings are matrix representations of faces, where similar faces yield similar matrix representations, as previously mentioned [75]. Conversely, dissimilar faces produce distinct matrix representations.  
In the Dlib-based approach, the aligned face images are converted into 128D feature vectors representing each face, using the pretrained ResNet model for face recognition (dlib_face_recognition_resnet_model_v1.dat) [84]. This model achieved a verification accuracy of 0.9938 [84] on the Labeled Faces in the Wild (LFW) dataset [86], making it a very effective and lightweight solution for practical use cases.  
In addition, two modern deep learning models were also used for feature extraction in the YOLOv8-based pipeline:  
* VGG-Face [77]: A deep convolutional neural network trained on a large-scale dataset of celebrity faces. It is known for its robust performance and has achieved a verification accuracy of 0.9895 [77] on the LFW dataset [86], classifying it as a highly accurate model for face recognition.
* GhostFaceNet [78]: A lightweight yet powerful model that incorporates ArcFace loss to generate highly discriminative facial embeddings. GhostFaceNet achieved a verification accuracy of 0.9971 [78] on the LFW dataset [86], thereby outperforming both Dlib and VGG-Face.  
The models were used via the DeepFace framework, which internally handles preprocessing and supports multiple backends [87].  
The use of multiple recognition models enables a broader evaluation and allows the comparison of classical and modern face representation techniques under the same preprocessing conditions.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 4 Numerical Simulation
### Scenario Design  
The nominal orbit elements of the four targets and observer are listed in Table 1, where \(a,e,n,i,\omega\), and \(\Omega\) label the semimajor axis, eccentricity, true anomaly, inclination, argument of periapsis, and longitude of the ascending node, respectively. The nominal orbits of the observer and four targets are illustrated in Figure 3. The orbit of the observer is black and the orbits of the four targets are red, blue, green, and yellow, respectively. The nominal orbit elements of the four targets are particularly designed to guarantee that their orbits cross with each other. The crossing orbits of the targets add more difficulties in the data association, in which the advantages of the proposed method can be better demonstrated.  
Figure 3: Illustration of the nominal orbits of the observer and targets.
## 4 Finite Element Implementations
### Consistent Tangent Modulus and Calculation of Strain Increment
#### 4.4.1 Uniaxial Tension Verification  
Based on the proposed model, the uniaxial tensile behavior of the material was calculated using the user version of ANSYS. For simplicity, only one element PLANE42 with a side length of 1 was used for the calculation, as shown in Figure 15. The x-direction displacement constraint was applied to the left side of the model, the y-direction displacement constraint was applied to the lower side, and the x-direction load was applied to the right side.  
The predictions of the uniaxial tension are plotted in Figure 16 together with those predicted by the incremental loading method of MATLAB. The comparison results show that the results of the finite element method with a single element were consistent with the experimental results and MATLAB predicted results.