esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Characterization of Diatomic Powders, Diatomite/Sodium Alginate Dispersions and Resultant Granules
The results indicate that the heat-treated granules prepared with the D04-D16 dispersions (3.85 wt.% to 13.79 wt.%), which displayed viscosities from 273 mPa\(\cdot\)s to 429 mPa\(\cdot\)s, showed sphericity values between approximately 0.4 and 0.6, exhibiting a tear-like shape (D04) or an ellipsoid shape with a small protrusion (D08-D16). As both the diatomic content (16.67 wt.% to 28.57 wt.%) and the viscosity of the dispersion increased (D20: 468 mPa\(\cdot\)s to D40: 1326 mPa\(\cdot\)s), the granules presented sphericity values of around 0.7-0.8. The D43 dispersion exhibited the highest sphericity of around 0.9, with a diatomic solid content and viscosity of 30.07 wt.% and 1560 mPa\(\cdot\)s, respectively. However,  
Figure 5: Porous diatomic granules obtained from different dispersion compositions after a heat treatment at 1200 \({}^{\circ}\)C for 1 h in air. a slight increase (1.63%) in the solid content (30.56 wt.%) resulted in a significant increase in the viscosity of the D44 dispersion (3160 mPa\(\cdot\)s), leading to pear-shaped heat-treated granules with decreased sphericity of approximately 0.5. This suggests that under the fixed processing conditions, there is a diatomic solid content and a viscosity threshold of approximately 30.07 wt.% and 1560 mPa\(\cdot\)s, respectively, which can be used to achieve porous diatomic granules with sphericity of around 0.90. Similar trends were observed in CSZ microspheres [22; 68] and \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) granules [6] under pre-established granulation conditions [6]. The shape of these specimens was improved to achieve a sphericity value close to 1 (sphericity factor close to 0) by increasing the solid matter (raw materials or precursors content) [6; 22] or the viscosity of the starting granulation sols [68], until optimized parameters were reached. Beyond this condition, the desired sphericity value could no longer be maintained. Thus, the resultant spherical granule shape is influenced by the optimization of the solid content and the viscosity of the dispersions to stabilize the solid network within the granules and minimize deformations during granulation [40]. Based on the above analysis and considering the tested granulation conditions, the selected diatomic/sodium alginate dispersion composition to ensure millimeter-sized granules (2.60-3.10 mm) with a sphericity of approximately 0.90 is 43 g of diatomic/100 mL H\({}_{2}\)O and 1.50 wt.% of sodium alginate. An investigation of alternative granulation parameters, gelling bath conditions, and alginate compositions or additives to optimize the sphericity of the granules processed with higher contents of diatomic (D44: 30.56 wt.%) is beyond the scope of this work. Therefore, the porous diatomic granules for the subsequent experiments were prepared using the fixed D43 dispersion composition and granulation conditions.
## 3 Results and Discussion
### Polarization Characteristics of Particle Composition  
Our second set of experimental samples is three kinds of water-soluble salts: Na2 SO4, NaCl, and NaNO3. The scatter plot of polarization data has been shown in Figure 7, and the corresponding mean values, PFR and Wilk's Lambda of the experimental polarization indices are shown in Figure 8. The water-soluble salt samples are generated in the form of spray, which have similar particle size ranges less than 4 um and a nearly spherical shape. Thus, Figure 8 can be used to evaluate the connection between polarization parameters with the refractive index of aerosols.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Index** & **Number** & **Index** & **Number** & **Index** \\ \hline
1 & H-Hdop (30\({}^{\circ}\)) & 9 & P-Hdop (30\({}^{\circ}\)) & 17 & R-Hdop (30\({}^{\circ}\)) \\
2 & H-Pdop (30\({}^{\circ}\)) & 10 & P-Pdop (30\({}^{\circ}\)) & 18 & R-Pdop (30\({}^{\circ}\)) \\
3 & [PERSON] (60\({}^{\circ}\)) & 11 & [PERSON] (60\({}^{\circ}\)) & 19 & [PERSON] (60\({}^{\circ}\)) \\
4 & [PERSON] (60\({}^{\circ}\)) & 12 & [PERSON] (60\({}^{\circ}\)) & 20 & [PERSON] (60\({}^{\circ}\)) \\
5 & [PERSON] (85\({}^{\circ}\)) & 13 & [PERSON] (85\({}^{\circ}\)) & 21 & [PERSON] (85\({}^{\circ}\)) \\
6 & [PERSON] (85\({}^{\circ}\)) & 14 & [PERSON] (85\({}^{\circ}\)) & 22 & [PERSON] (85\({}^{\circ}\)) \\
7 & H-Hdop (115\({}^{\circ}\)) & 15 & P-Hdop (115\({}^{\circ}\)) & 23 & R-Hdop (115\({}^{\circ}\)) \\
8 & H-Pdop (115\({}^{\circ}\)) & 16 & P-Pdop (115\({}^{\circ}\)) & 24 & R-Pdop (115\({}^{\circ}\)) \\ \hline \end{tabular}
\end{table}
Table 2: The numbering order of polarization index in the figures.  
Figure 7: 24-dimensional polarization indices distribution of aerosol samples with different refractive indices.  
From Figure 8a, H-Pdop (30\({}^{\circ}\)), H-Pdop (60\({}^{\circ}\)), and H-Pdop (85\({}^{\circ}\)) can separate three types of samples better than other polarization indices. Then, according to Figure 8b,c, H-Pdop (30\({}^{\circ}\)) can show relatively optimal resolution and discrimination due to smaller PFR and Wilk's Lambda (marked by the red inverted triangle). On the other hand, if we want to find out which polarization parameter is insensitive to the composition of water-soluble aerosols, we can select P-Pdop (30\({}^{\circ}\)), P-Hdop (60\({}^{\circ}\)), P-Pdop (60\({}^{\circ}\)), and R-Pdop (85\({}^{\circ}\)). Besides, the P-Pdop (60\({}^{\circ}\)) seems a better stable indicator unaffected by the refractive index difference between the three kinds of water-soluble salts.  
The main difference between these water-soluble aerosols is the refractive index due to different compositions. In this case, if we consider various incident polarization states and scattering angles, the incident light with an H (horizontal) linear polarized state and the 30\({}^{\circ}\) detection angle seem to be the proper choice.
## 4 Conclusions  
The work assessed the feasibility of utilizing clayish reservoir sediments (RS) as an SCM (supplementary cementitious material) in CSA (calcium-sulfoaluminate) cements. The RS were heated (TT) to 830 \({}^{\circ}\)C to obtain the amorphous constituents that were due to the dehydroxylation of the silico-aluminate crystalline components. TTRS were added at dosages of 15, 25, and 35% by weight, and the related binders were aged up to 56 days.  
The key findings derived from the investigations are reported below:  
1. when compared to plain CSA cement pastes, those based on blended CSA binders showed increased pore size distributions (as evidenced by the mercury intrusion investigations);
2. the mechanical strength decreased with increases in the TTRS replacement levels (the dilution effect), although the gaps for the plain CSA-based mortars were reduced as the curing time increased;
3. the X-ray diffraction and differential thermal-thermogravimetric results showed that the presence of thermally treated clayish reservoir sediments affected neither the creation of CSA hydration products nor the dimensional stability of cement pastes (when cured in both water and air).  
The utilization of TTRS as SCM can allow both a reduction in the use of natural materials and the prevent the waste disposal in a landfill; in addition, it can permit the dilution of the CSA cement, which has implications for both the reduction of CO\({}_{2}\) emissions and evident energy cuts per unit mass of binder.  
Future research will deal with the study of the blended CSA cements cured for periods longer than 56 days; moreover, further studies will involve the utilization of TTRS in high-belite CSA and belite cements.  
Figure 11: Compressive strength related to bound water, as determined by the TG weight loss at 500 \({}^{\circ}\)C (normalised to CSA anhydrous cement) (**left**). Compressive strength compared to evaluated total porosity (**right**).  
**Author Contributions:** A.T.: conceptualization, methodology, formal analysis, investigation, data curation, writing--original draft preparation, writing--review and editing; M.M.: conceptualization, methodology, formal analysis, investigation, data curation, writing--original draft preparation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Simulation Model Development
### Spiral Bevel Gear  
Spiral bevel gears are a kind of bevel gear which has helical teeth. The spiral bevel gear used in the differential gear system of a tractor transmission receives the power of the driving shaft of the transmission from the drive pinion gear and transmits power to the left and right wheels in a right angle direction. The spiral bevel gear consists of a pinion gear and a ring gear, in a structure that intersects at right angles. Table 2 shows the main specifications of the spiral bevel gear used in this study. The torque and rotation speed of the spiral bevel gear were calculated using the gear ratio and efficiency, based on the torque and rotation speed data of the wheel measured in the field, as shown in Equations (1) and (2). The efficiencies of the spiral bevel gear and the final reduction gear are 0.98 and 0.94, respectively, and the gear ratios of the spiral bevel gear and the final reduction gear are 3.82 and 6.00, respectively.  
\[T_{s}=\frac{1}{\eta_{s}\eta_{f}}\frac{1}{G_{s}G_{f}}(T_{rl}+T_{rr}) \tag{1}\]  
where \(T_{s}\) is the spiral bevel gear input torque (Nm), \(\eta_{s}\) is the efficiency of the spiral bevel gear, \(\eta_{f}\) is the efficiency of the final reduction gear, \(G_{s}\) is the gear ratio of the spiral bevel gear, \(G_{f}\) is the gear ratio of the final reduction gear, \(T_{rl}\) is the torque of the rear left wheel (Nm), and \(T_{rr}\) is the torque of the rear right wheel (Nm).  
\[N_{s}=G_{s}G_{f}\Big{(}\frac{N_{rl}+N_{rr}}{2}\Big{)} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pinion Gear** & **Ring Gear** \\ \hline Tooth profile & & Gleason \\ Module (mm) & & 6.5 \\ Pressure angle (\({}^{\circ}\)) & & 20 \\ Helix angle (\({}^{\circ}\)) & & 35 \\ Center distance (mm) & & 89 \\ Material & & SCM822 \\ Number of teeth & 11 & 42 \\ Face width (mm) & 42.5 & 42.5 \\ Pitch circle diameter (mm) & 71.5 & 273 \\ Cone distance (mm) & 141.104 & 141.104 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of spiral bevel gear used in this study.  
Figure 2: Power transmission system of wheeled tractor.  
where \(N_{s}\) is the rotational speed of the spiral bevel gear (rpm), \(N_{rl}\) is the rotational speed of the rear left wheel (rpm), and \(N_{rr}\) is the rotational speed of the rear right wheel (rpm).
# Research on Safe Following Distance on an Expressway Based on Braking Process Analysis
## Modeling
### Analysis and Assessment of Braking Distance
#### 3.1.3 Problem Simplification  
In order to obtain the critical safe distance between two cars and carry out theoretical analysis, it is necessary to perform a specific analysis of driving on an expressway and make some simplifications [22].  
1. The car movement in a short time on the expressway is simplified to a uniform movement. Assume that at a certain moment on the expressway, the front car (A) is moving at the speed of \(V_{A}\) and the following car (B) is moving at the speed of \(V_{B}\). In this case, the distance between car A and car B is \"\(D\)\". This is shown in Figure 1.
2. With the passage of time, the distance between the front car (A) and the following car (B) has two possible changing trends. One is that the distance between the two cars is getting larger and larger, which obviously eliminates the problem of safe following distance between the two cars, so this situation is not the process to be discussed. We are concerned about the other situation, where the distance between the two cars gets smaller and smaller, and if no action is taken, there will be a rear-end collision. Among the possible situations, the most dangerous is that either the front car brakes and the following car accelerates, or the front car brakes and the following car still moves at its original speed. However, those two scenarios are not what the article is talking about here, as, in them, the following car is likely trying to overtake the front car, the two cars are not in the same lane, or the crash is intentional. Obviously, the situation in question is that on the expressway, the front car and the following car are moving in the same direction in the same lane, and the following car does not intend to pass the car in front, but just follows the front car. At a certain moment, car A suddenly brakes. The driver of car B will discover that the distance between the two cars is decreasing, so the following car immediately brakes to avoid a collision between the two cars (assuming that there is no possibility or possible behavior of  
Figure 2: Change curves of brake-pedal force and braking deceleration with time in the process of automobile braking.  
steering to avoid the crash). In this case, when the following car adopts braking, the front car and the following car will not collide. We define the minimum distance that the two cars must keep at the moment before the front car brakes as the critical safe distance.
3. We assume that the driving conditions of the two cars are the same and the drivers are in the same condition. That is, it is assumed that the braking parameters of the two cars are the same and the drivers' abilities to respond are the same.
## 3 Results and Discussion
### Zeta Potential Measurements  
The increased stability of the PHB-stabilized ZVI particles was additionally characterized by studying the changes in surface potential. In practice, the electric potential near the surface of the molecule (i.e., \(\zeta\)-potential) is oftentimes assessed and applied as an indicator of the surface potential. The \(\zeta\)-potential values for ZVI and ZVI-PHB particles at pH 7 were found to be \(-\)0.03 \(\pm\) 0.1 mV and 18.73 \(\pm\) 0.5 mV, respectively. The magnitude of the surface potential generally regulates the degree of the electrostatic repulsion between molecules. The ZVI particles were slightly negative at pH 7. After PHB addition, the \(\zeta\)-potential increased to 18.73 mV, which could significantly increase the repulsion between the particles. Increasing the \(\zeta\)-potential can enhance the electrostatic repulsion among particles, thereby stabilizing the suspension.
## 3 Results
### Experiments with Dimensionality and Sample Size
#### 3.3.2 Conditional GAN Performance  
CGAN presented limited success when augmenting datasets, with only Wasserstein Gradient Penalty loss succeeding in overcoming mode collapse. Nevertheless, CGAN was still able to generate data with insignificant differences (Table 8), successfully augmenting the targeted datasets (Table 9 and Figure 9). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Original Data**} & \multicolumn{2}{c}{**Bootstrap**} & \multicolumn{2}{c}{**GAN**} \\ \cline{2-7}  & **PC1** & **PC2** & **PC1** & **PC2** & **PC1** & **PC2** \\ \hline Shapiro \(w\) & 0.99 & 0.98 & 0.98 & 0.98 & 0.99 & 0.98 \\ Shapiro \(p\) & 0.75 & 0.30 & 0.25 & 0.18 & 0.92 & 0.10 \\ Central Tendency \({}^{\ddagger}\) & \(-\)0.10 & \(-\)0.16 & \(-\)0.14 & \(-\)0.21 & \(-\)0.06 & \(-\)0.04 \\ Deviation \({}^{\lx@sectionsign}\) & 0.41 & 0.43 & 0.40 & 0.43 & 0.41 & 0.49 \\ Minimum Value & \(-\)1.00 & \(-\)1.00 & \(-\)1.00 & \(-\)1.00 & \(-\)1.00 & \(-\)1.00 \\ IR 0.05 Limit & \(-\)0.81 & \(-\)0.98 & \(-\)0.81 & \(-\)0.99 & \(-\)0.78 & \(-\)0.85 \\ IR 0.95 Limit & 0.47 & 0.61 & 0.47 & 0.52 & 0.55 & 0.84 \\ Maximum Value & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 \\ Kurtosis & \(-\)0.33 & 0.19 & \(-\)0.43 & \(-\)0.24 & \(-\)0.49 & \(-\)0.65 \\ Skewness & \(-\)0.02 & 0.35 & \(-\)0.01 & 0.21 & 0.05 & 0.18 \\ \hline \multicolumn{7}{l}{\({}^{\ddagger}\) Metric used = mean; \({}^{\lx@sectionsign}\) Metric used = standard deviation.} \\ \end{tabular}
\end{table} 
Table 7: Comparison of descriptive statistics obtained when comparing traditional bootstrapping procedures for numeric data augmentation and the best performing GAN on DS3. Dataset was augmented to size 100. Figure 8: Histograms and scatter plots of augmented DS3 using bootstrap and GAN. New points are marked in red. When taking a closer look at the performance of CGAN, however, it is important to note that, while the magnitude of differences between synthetic and real data are insignificant, CGAN distorts the original distribution to a greater degree (Figure 9). In both samples, CGAN deviates greatly \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Sample 1**} & \multicolumn{2}{c}{**Sample 2**} \\ \cline{2-6}  & \(|d|\) & \(p\) & \(|d|\) & \(p\) \\ \hline CGAN & \(-0.100\) & \(1.3\times 10^{-8}\) & \(-0.035\) & \(2.0\times 10^{-10}\) \\ GAN & \(-0.039\) & \(2.0\times 10^{-10}\) & \(-0.074\) & \(2.9\times 10^{-10}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Best obtained absolute difference and \(p\) value calculations for robust equivalency testing when comparing targeted generation of data using CGAN and GAN on DS3. Both CGAN and GAN were trained using Wasserstein Gradient Penalty loss.
## 3 Specification Mining Based on the OPTICS Clustering Algorithm and Model Checking
### FSA Refinement Based on Model Checking  
The FSAs generated in Section 3.1 are prone to incorrect paths in the model due to erroneous merging among states. To mitigate this issue, we refine the FSAs based on model checking. Specifically, we explore the FSA and check if it satisfies the LTL formulas. When a violation is detected, we refine the automaton to eliminate the violation and improve the precision of the FSA. We repeat this process until there is no violation or the number of times for the refinement reaches a threshold value \(T\). In order to use the partial specification of the software (LTL formulas) to guide the refinement of the overall specification of the software (FSA), we input the traces generated in DSM [8; 9], property templates, and confidence thresholds into Texada [6] to generate the following three forms of LTL formulas, which were shown to specify the temporal properties of automata by [PERSON] et al. [22]. 1. \(AIF(a,b)\): an occurrence of event \(a\) must be immediately followed by event \(b\), i.e., \(G\) (\(a\to X\)\(b\));
2. \(AIP(a,b)\): an occurrence of event \(a\) must be immediately preceded by event \(b\), i.e., \(F\) (\(a\)) \(\rightarrow\) (\(-\) \(a\cup(b\)\(\wedge\)\(X\)\(a\)));
3. \(NIF(a,b)\): an occurrence of event \(a\) is never immediately followed by event \(b\), i.e., \(G\) (\(a\to X\)\((\
eg\)\(b\))). The variables used in the refinement process are defined as follows:  
1. \(D\) and \(D^{\prime}\) represent the FSAs before and after refinement, respectively;
2. \(K\) is a set of LTL formulas generated by Texada;
3. \(Trans\) is a set of tuples \(\langle SM,q_{2},m_{2},q_{3},MS\rangle\), where \(SM\) is a set of tuples \(\langle q_{1},m_{1}\rangle\), \(MS\) is a set of tuples \(\langle m_{3},q_{4}\rangle\), \(q_{1}\), \(q_{2}\), \(q_{3}\), and \(q_{4}\) are states, and \(m_{1}\), \(m_{2}\), \(m_{3}\), and \(m_{4}\) are methods;
4. \(C_{i}^{\prime}(1\leq i\leq nnum)\) represents newly added states in the FSA. Algorithm 2 and Figure 5 show the refinement process based on model checking in detail, where the initial FSA \(D\) and a set \(K\) of LTL formulas are taken as the input. In the process, \(num\) and \(times\) are initialized to \(0\) and \(D^{\prime}\) is initialized to \(D\) (Line 1). We then verify whether the number of times for refinement reaches the threshold value, and whether there exists a property violated by the FSA, i.e., whether the software behavior described by FSA does not match that described by the LTL formula (Line 2).
## 6 Simulation Validation
### Experimental Results
#### 6.2.4 Case Study 4  
Figure 15 shows the results of case study 4, where \(\mu\) = 0.6, the semi-trailer is unloaded, and the vehicle speed is 100 km/h. In terms of path tracking, the method proposed in this paper performs remarkably. The maximum LTR value of 0.83893 is significantly lower than the 1.0 threshold observed in the comparison method, indicating a certain advantage in rollover stability. Moreover, the average steering angle, average yaw rate, and average lateral acceleration are all smaller than those of the comparison method, suggesting better driving comfort and lateral dynamic stability. Under the specific conditions of case study 4, the LTR index in Figure 15b shows sharp fluctuations between 160-190 m. From the perspective of vehicle dynamics, when the load mass of the semi-trailer is 0 t, the position of the vehicle's center of gravity changes. Under high-speed (100 km/h) and low road adhesion coefficient (\(\mu\) = 0.6) conditions, the vehicle is more sensitive to steering operations. A small path adjustment may cause a large LTR change. The abrupt change in LTR poses significant risks to semi-trailer stability:  
1. Increased lateral load transfer, leading to rollover propensity.
2. Sudden weight redistribution, compromising tire-road adhesion.
3. Dynamic instability during transient maneuvers due to coupled yaw-roll effects. The comparison of the data from case study 4 can be found in Table 8.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study 2** & **Our Method** & **Sigmoid Method** \\ \hline Average curvature of the path & 0.00175 & 0.00402 \\ Maximum of LTR & 0.74934 & 1 \\ Average steering angle & 7.70237 & 9.05435 \\ Average yaw rate & 0.62028 & 0.88300 \\ Average lateral acceleration & 0.02438 & 0.03051 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of case study 2 data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Study 3** & **Our Method** & **Sigmoid Method** \\ \hline Average curvature of the path & 0.00351 & 0.00801 \\ Maximum of LTR & 0.63285 & 0.98316 \\ Average steering angle & 10.26115 & 12.53339 \\ Average yaw rate & 0.88337 & 1.20238 \\ Average lateral acceleration & 0.02678 & 0.03348 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of case study 3 data.
# An Overview of CubeSat Missions and Applications
## 2 The Success of CubeSats and Its Enabling Factors
### A New Business Model  
With the evolution of the CubeSat ecosystem and business models, the growth of the CubeSat industry has given rise to a dedicated ecosystem encompassing manufacturing, launch services, and operational support. Companies now specialize in CubeSat components, mission integration, and satellite operations, contributing to a thriving commercial sector. One emerging trend is Satellite-as-a-Service (SaaS), where companies provide fully managed CubeSat missions, covering design, manufacturing, launch, and operation. This allows organizations without in-house space expertise to deploy CubeSat missions tailored to their needs [42].  
Since the first CubeSat was launched in 2003, their numbers have grown exponentially. By 2024, over 2500 CubeSats had been launched, with projections indicating that over 10,000 could be deployed within the next decade. CubeSats have evolved from simple educational tools into sophisticated platforms supporting diverse scientific, commercial, and exploratory missions. As their ecosystem continues to expand, CubeSats are expected to play an even more significant role in space exploration, further democratizing access to space and enabling groundbreaking applications in research, communications, and planetary exploration.