esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Discussions  
The time exponential rate P&O technology is proposed in this work to extract the maximum electrical energy from TEG. By using the time exponential rate P&O, the proposed MPPT circuit observes the power change by comparing PMOS on-time and perturbs the power by adjusting NMOS on-time exponentially, which shows an enhanced tracking efficiency for a wide range of TEG. The MPPT circuit was implemented in a 110 nm CMOS process and its tracking efficiencies varied from 98.9% to 99.5% with TEG open-circuit voltage from 50 mV to 400 mV and the TEG internal resistance from 1 \(\Omega\) to 12 \(\Omega\).  
This time exponential rate P&O technology is expected to contribute to the versatility of the MPPT circuit for various TEGs, which are made of varied semiconductor materials and have different internal resistances. However, there are also limitations of the proposed MPPT circuit. Nowadays, a multiple-input multiple-output (MIMO) converter is a trend for energy harvesters. The MIMO converter can store partial electricity when the ambient energy is redundant, and this electricity can be used to power the load when the ambient energy is insufficient. However, the proposed MPPT circuit is designed for a single-input single-output (SISO) system. The time exponential rate P&O technology should be reconsidered for the MIMO system, especially when the voltage of the electricity storage capacitor (V\({}_{\text{ST}}\)) is low, and a high conversion rate (V\({}_{\text{ST}}\gg\) V\({}_{\text{TEG}}\)) is impossible. These challenges are an important future research direction for the MPPT circuit.  
Conceptualization, H.C. and J.M.; methodology, J.M. and H.C.; investigation, J.M.; data curation, J.M. and Y.L. ([PERSON]); project administration, W.L.; funding acquisition, Z.S. and Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2017 YFB0701703, 2017 YFA0206104, 2017 YFB0405601, 2018 YFB0407500), National Natural Science Foundation of China (61874178, 61874129, 91964204, 61904186, 61904189), Strategic Priority Research Program of the Chinese Academy of Sciences (XDB44010200), Science and Technology Council of Shanghai (17 DZ2291300, 19 JC1416801), Shanghai Sailing Program (19 YF1456100).  
Not applicable.  
The authors would thank the Semiconductor Manufacturing International Corporation (SMIC) for support.  
The authors declare no conflict of interest.
## 3 Results  
In this section we present the results obtained for the analysis performed. For a clearer understanding of the analysis presented in the article, we start from a classic case based on a ground--GEO satellite link. This classic analysis provides the basis for extending the study to multi-orbital systems that will be detailed later. In this section, the parameter values for the ground station, satellite, and link properties are chosen to reflect the real conditions encountered in modern satellite communication systems. For example, the use of the Ku-band is justified by the fact that it represents an optimal compromise between capacity and susceptibility to atmospheric attenuation, being widely used in commercial networks, including multi-orbital systems. The choice of this band is also supported by its use in current satellite constellations, such as Starlink or OneWeb. In addition, other link parameters, such as antenna gain, losses, and G/T ratio, have been selected to correspond to real values encountered in satellite communication systems, ensuring a realistic analysis of the performance of different modulation schemes and error correction codes.  
To obtain and analyze the results presented in this study, we used MATLAB R2023b, a well-known software for simulating and modeling communication systems. MATLAB was used to calculate the link budget, evaluate the attenuation caused by different factors, and analyze the performance of different modulation schemes and error correction codes.
## 4 Experimental Results
### Firewall Performance under Real Operative Conditions
#### 4.2.1 Download/Upload of a File  
During this first test, the Arduino IDE (file size 197 MB) was downloaded from the official website using the Linux command 'wget'. The file was downloaded four times in sequence by setting different limits for the download bandwidth (2.5 MB/s, 5 MB/s, 10 MB/s, 25 MB/s). The results are reported in Figure 10.  
As can be seen, the data throughput from Port A to Port B (i.e., from the PC to the website hosting the file) has values of 1.77 kB/s, 2.23 kB/s, 13.07 kB/s and 58.54 kB/s during the four download operations with a bandwidth limit of 2.5 MB/s, 5 MB/s, 10 MB/s and 25 MB/s, respectively. This can be explained by the fact that, during a download operation, data transferred from the PC to the website hosting the file represent  
Figure 10: Data throughput measured by the proposed firewall during a file download with the Linux command ‘wget’ and four different limits for the download bandwidth.  
Figure 9: Experimental setup for the measurement of the firewall performance under operative conditions of real network traffic.  
only control packets that are sent to the website to negotiate the file download. The download data are then sent from the website to the PC (from Port B to Port A). In this case, the measured data throughput is 2.76 MB/s, 5.57 MB/s, 10.91 MB/s and 27.9 MB/s during the four download operations with a bandwidth limit of 2.5 MB/s, 5 MB/s, 10 MB/s and 25 MB/s, respectively. The total downloaded data in each of the four download operations are equal to the file size (197 MB).  
A second test was carried out using FileZilla, an open-source multi-platform software that can be used to transfer files using the FTP protocol [48]. In this case, the same file of 197 MB in size was first uploaded to a FileZilla server and then downloaded to the PC. The measured data throughput at the two ports of the firewall are plotted vs. time and presented in Figure 11. During the upload phase, lasting 36 s, the average data throughput from the PC to the FileZilla server (i.e., from Port A to Port B) is 5.34 MB/s while the average data throughput from the FileZilla server to the PC (i.e., from Port B to Port A) is 80.88 kB/s. During the download phase, lasting 166 s, the average data throughput from the PC to the Filezilla server (i.e., from Port A to Port B) is 13.75 kB/s while the average data throughput from the FileZilla server to the PC (i.e., from Port B to Port A) is 1.19 MB/s.
## 2 State-of-Art Review
### The Effect of Varying RFA Incorporation Ratio on Concrete Performance
#### 2.2.6 Chloride Penetration Resistance  
The chloride ion penetration resistance of concrete is highly affected by the pore content in concrete, similarly to carbonation resistance. Generally, it can be said that as the incorporation of RFA increases the chloride penetration resistance decreases because RFA increases concrete's porosity. The source of the deleterious action of chloride ions is generally classified into two types: internal agents, such as the mixing water, aggregate or admixtures, e.g., in very low temperature calcium chloride is used as an accelerator, and agents that may come from external sources, e.g., marine environment, pools and de-icing salts [90].  
Figure 11: Carbonation depth over time of concrete mixes versus fine NA with RFA incorporation ratio.  
Figure 10: Relative carbonation of RA concrete vs. **(a)** RFA incorporation level and concrete relative compressive strength **(b)** relative concrete compressive strength.  
In his study, [PERSON] [79] listed several factors that affect the chloride ion penetration of RAC, such as the RA replacement level, quality of the RA, exposure to different curing conditions, mixing procedure, admixture use, and use of mineral additions. The author reported that RA leads to higher chloride ion penetration depths than NA. The aggregates subjected to primary plus secondary crushing procedure are better than those from primary only crushing procedure because aggregates subjected to secondary cursing procedure have lower adhered mortar content, which leads to lower aggregate porosity. Thus, the RA allow lower permeability and chloride ion penetration. The resistance of RAC to chloride ion penetration approaches the resistance of NAC with the passage of time. The addition of materials and steam curing have a positive effect on RAC performance in terms of chloride ion penetration. The following studies [86; 89] concluded that incorporating 10% of RFA may have a positive effect on the resistance to chloride ion penetration. For higher values, similarly to carbonation, the chloride ion penetration resistance decreases as the incorporation of RFA increases (Figure 12). The regression model displayed in the graph shows that given a conventional concrete mix exhibiting a certain chloride penetration potential \(P_{\text{CI1}}\), the predicted value is relative to that with the addition of a % X of RFA \(P_{\text{CI2}}\) (Equation (5)).  
\[Y_{\text{cl}}\left(P_{\text{CI2}}P_{\text{CI1}}\right)=1+0.0033\text{X} \tag{5}\]
# Hierarchical Model Updating Method for Vector Electric-Propulsion Satellites
## 5 Conclusions  
To reflect the coupling effect between the subsystems and improve the prediction accuracy of the vector electric-propulsion satellite model, the mathematical model is established according to the whole satellite, subsystems, and devices. The state variables and telemetry variables that have a major influence are selected, grouped, and sorted by the hierarchical rules proposed. Each group is then updated by leveraging the least-squares method in sequence. The iteration loop is executed until the telemetry errors converge to the expected threshold.  
The simulation results show that the residual errors of telemetry variables become smaller, and the estimation values of state variables become closer to the true value by hierarchical model updating with the close-loop iterations, which verifies the effectiveness of the hierarchical rule in solving the model updating problem of the vector electric-propulsion satellite.  
The research in this paper provides a method to decompose a complex system into simple parts and solve them separately, which can not only be used for model updating of vector electric-propulsion satellites but also can be extended to other complex satellite digital twin models.  
Conceptualization, Y.D. and X.W.; methodology, X.W.; software, X.W.; validation, X.W.; formal analysis, X.W.; investigation, X.W.; resources, X.W.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W.; visualization, X.W.; supervision, Y.D.; project administration, X.W.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was partially supported by the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education.  
The authors declare no conflict of interest.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Calibration Model  
As expressed in Equation (5), at least three, six, and 10 polynomial coefficients are required to be calculated, respectively, when a 1 st, 2 nd, and 3 rd order linear polynomial is utilized for calibration, which means that at least three, six, and 10 calibration markers are required. When the number of calibration markers needed is too large, unessential input items can be removed according to principal component analysis to reduce the number of polynomial coefficients to be solved. Generally, based on an overall consideration of the real-time quality and accuracy of a gaze tracking system, four- and five-marker calibration models are most widely employed for 1 st order calculation, while six- and nine-marker calibration models are most widely employed for 2 nd order calculation [78; 79].  
Considering that there is some motion between the wearable gaze tracking system and the user's head, error of gaze point data will occur along with a drifting motion. In this paper, position coordinates of quadrangular NIR LEDs are considered as inputs of gaze estimation model to compensate for error caused by drifting motion. As shown in Figure 7, for the purpose of comparison, four-, six-, nine-, and 16-marker calibration models are employed in the process of calculating mapping function. Gaze direction is estimated with and without error compensation. The gaze tracking accuracy of the two cases is compared.
# Idealisations of Dynamic Modelling for Railway Ballast in Flood Conditions
## 4 Discussion on Suitability of Ballast Idealisation
### Research Gap and Problems to Be Resolved  
It is found that only considering the dynamic performance in dry conditions is insufficient as an abnormal climate can occur frequently. The southern Indian state of Kerala just suffered from the worst flood in 100 years [64; 65]. [PERSON] et al. [25] has pointed out that the accompanying extreme weather events have grown and are expected to increase in the future. Climate change can cause existing structures to exhibit different performance characteristics than normal [66]. Hence, for the engineering structures, there is a compelling need to consider not only safety and applicability but also reliability, resilience and durability [67].  
Until now, most investigations scarcely involve the area of flooded ballast. [PERSON] [3] has pointed out that it is necessary to determine their railway track exposure and the modal parameters of railway turnouts (switches and crossings) under flood conditions. For a better understanding of mechanical behaviours involving the railway ballast, it requires reasonable test methods and appropriate numerical models [68; 69; 70; 71]. Therefore, the study of ballast in flood conditions by identifying the changing law and establishing an ideal dynamic model is particularly important.  
This study embarks on the full-scale experiments and formulates 6 models of ballast idealisation concepts. The modal identification results reveal that the fundamental model (Model 1: spring dashpot) prevails over other models in terms of accuracy and precision. The more complicated models (such as Model 2 to 6) are not suitable for ballast idealisation, especially in a flood condition.
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods
### Image Processing of UAV Images
#### 2.3.2 Clustering of Pixels Using k-Means Clustering Method  
We then clustered, filtered, and enhanced images using the k-means clustering algorithm. The k-means clustering method is a clustering algorithm. In image analysis, it can cluster pixels of an image in unsupervised classification. Implementation of the k-means clustering in Python is uncomplicated, and therefore widely used by novice programmers and data scientists [37]. We implement the k-means for image pixel clustering in Python using its multiprocessing feature. The multiprocessing feature enables the parallel implementation of the k-means algorithm, reducing the execution time. The principal component of k-means clustering involves two steps, expectation and maximization. We assign each pixel to its nearest centroid in expectation, while the maximization step involves computing the mean of all pixels for each cluster and sets the new centroid. The flowchart in Figure 2 depicts the steps involved in implementing the k-means clustering algorithm.
## 3 Results and Discussion
### Comparison of Different Spectral Regions
The above results show that the commonly used spectral region selection can effectively reduce the prediction error of Raman-based models, but the optimal spectral region of each component is different. For Gluc, Lac, VCD, and NH4\({}^{+}\), the optimal spectral regions were 300-1800 cm\({}^{-1}\), 100-2500 cm\({}^{-1}\), 300-1800 cm\({}^{-1}\), and 600-1800 cm\({}^{-1}\) plus 2800-3000 cm\({}^{-1}\), respectively. These spectral regions were used to build models and predict the validation set, and the results are summarized in Table 3. With the three selected spectral regions, the model performance was improved, and the R\({}_{\rm C}\) and R\({}_{\rm P}\) are both greater than 0.94. The model with spectral regions of 600-1800 cm\({}^{-1}\) plus 2800-3000 cm\({}^{-1}\) (FP5 + CHS2) was relatively better. The RMSEC and RMSEP obtained were closer, and the model performance was improved by 34-61%. Although these spectral regions can effectively improve the performance of Raman-based models, they are not very useful for simplifying the model. The number of selected spectral variables was greater than 1000.
## 5 Numerical Results
### SA Solutions vs. Optimal and Best Found Solutions  
To assess the efficiency of the developed Simulated Annealing (SA) algorithm, we used the dataset introduced in [27]. In this dataset, customers and potentials sites coordinates are randomly generated between \((0,0)\) and \((10^{4},10^{4})\). The goods demands are generated from the interval \([0,500]\). The investment cost \(f_{k}\) in the interval \([0,5.10^{5}]\) and potentials sites capacities are drawn from \([0,10^{4}]\). After that, the direct cost \(c_{ij}\) is equal the Euclidean  
Figure 4: Main effects of parameters for Means.  
Figure 3: Main effects of parameters for SN ratios.  
distance between the customers \(i\) and \(j\), while the intermodal cost is computed using the following formula \(c_{ij}^{lm}=c_{ik}+\frac{1}{2}c_{km}+c_{mj}\). The objective function values of the studied model are detailed in [31]. For this, we lunched the SA method for 10 runs and we choose the best solutions. As shown in Figure 5, saving best solutions in an archive set enables to get a decreasing values of the objective function. Accordingly, the developed SA method is a descent strategy.  
Table 4 reports the gaps of the SA solutions to CPLEX solutions. The second column gives the gap of the best obtained solution from the CPLEX solution, while the third column gives the average gap of the conducted 10 runs.  
Note that we used the following formula to compute the gap:  
\[gap=\frac{\text{Simulated Annealing Best solution}-\text{Optimal solution}}{\text{Simulated Annealing Best solution}}\]  
The obtained results show that our method is efficient and may even reach the optimal solutions for the small size instances and reaches best solutions for medium instances in short times.  
To further evaluate the performance of the developed SA, we compare our results with those of [27]. Note that, for a meaningful comparison, since our problem and that studied in [27] are not the same, we made a transformation of our problem to get the same structure of [27]. In fact, since our problem is a generalization of [27], we added the two following constraints to our formulation:  
\[p=\sum_{k\in K}l_{kk}\]  
\[\sum_{kJ\in K}l_{km}=\frac{p(p-1)}{2}\]  
These two equations transform our incomplete (general case) to complete problem. The gaps of the last column in Table 5 show that our solutions by SA approach outperform best found solutions by [27] in 11 out of 15 instances.  
Figure 5: The best archived solution over the iterations for the instance 10C10L2 TL.
## 2 Experimental Section  
### Nanoparticle Synthesis and PAA Coating  
The synthesis of magnetic nanoparticles is based on the condensation of iron II and iron III salts in ammonia, yielding to the formation of magnetite nanoparticles. Magnetite is oxidized into maghemite by reacting with iron III nitrate at 80 \({}^{\circ}\)C for one hour. Typically, the maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)) nanoparticle size distribution, with size comprised between 4 and 15 nm [27; 28], is described by a lognormal distribution, with a polydispersity of 0.35. At pH 2, the maghemite nanoparticles are stabilized by electrostatic interactions arising from the native cationic charges at the surface of the particles. From this synthesis, size sorting is achieved to obtain different parts with smaller size distributions [28]. Particle sizes and size distributions were characterized by vibrating sample magnetometry, electron microscopy, and dynamic light scattering. The nanoparticles put under scrutiny presented a median  
Figure 1: Scheme of the PAA2k and PDMS-co-APMS coating on iron oxide nanoparticles.  
diameter DNP = 7.8 nm and a polydispersity = 0.19, determined by TEM. In order to improve their colloidal stability at neutral pH in water and to control their association with the PDMS-based copolymer, magnetic nanoparticles are coated with oppositely charged polyelectrolyte, namely polyacrylic acid with a molecular weight of 2,100 g-mol-1 (Sigma Aldrich).  
This synthesis followed the precipitation redispersion process described previously [22]. The precipitation of the cationic \(\gamma\)-Fe2O3 dispersion by PAA2K was performed by mixing the acidic solution of PAA2K (1 wt.%) and the acidic nanoparticle dispersion (1 wt.%) at a 2:1 weight ratio (excess of polymer). After elimination of the clear supernatant, the pH was increased by addition of ammonium hydroxide. As a consequence, the precipitate redispersed spontaneously, yielding to PAA2k-coated nanoparticles (NP PAA2K).  
Those nanoparticles were dialyzed against water in membrane of porosity 10 kD (Slyde-A-Lyzer, Thermo scientific) in order to remove the unlinked PAA2k polymer chains from the nanoparticle dispersion. This point is crucial in order to prevent reaction between the PDMS-based copolymer and the free PAA chains.