esa-sceva/satcom-chunk-collection huggingface dataset:
# Magnetic and Hydrophobic Composite Polyurethane Sponge for Oil-Water Separation
## 2 Experimental
### Method for the Oil Adsorption Tests  
All composite sponge samples were immersed in peanut oil at room temperature. The composite sponge samples were weighed firstly (M1), and then immersed into peanut oil until the samples adsorbed peanut oil to saturation. When the sponge was completely immersed in peanut oil and sunk to the bottom of the beaker, it was saturated. They were removed from the peanut oil and weighed (M2). The adsorption capacity Q is calculated according to the following equation:  
\[Q=\frac{M_{2}-M_{1}}{M_{1}} \tag{1}\]  
where M1 is the weight of initial sponge and M2 is the total weight of wet sponge with peanut oil. The adsorption capacities with adsorption cycles were also tested. The pre-weighed sponge samples were then immersed in the peanut oil and allowed for saturated adsorption. Once they were removed from the peanut oil, the saturated sponge samples masses were obtained. The sponge samples were squeezed thoroughly to remove most of the adsorbed peanut oil, and the absorbing-squeezing process was repeated for six cycles successively. In the beaker, the sponge was simply squeezed with a heavy object to perform a desorption test, and the peanut oil obtained was weighed for the adsorption capacity calculation. The squeezing was performed every time in the same way and squeezing tests were repeated on five sponges of the same kind. In our experimental process, to mimic the practical industrial application, the sponge samples were tested for the reusable adsorption capacity of the composite sponge within six adsorbing-squeezing cycles under pressure, successively. All the above processes were also carried out in lubricating oil and silicone oil.
# Research on the Efficient Space Debris Observation Method Based on Optical Satellite Constellations
## 2 Constellation Design
### Design Results  
We established the above constellation design model and optimized it using the genetic algorithms [24; 25]. Then the optimal orbit parameters such as orbit altitude and inclination were obtained. According to these orbit parameters, the different constellation configurations were designed, and the spatial and time domain coverage performances of the constellations were analyzed according to the established model to obtain high-quality constellation configurations using screening.  
The optimization process was carried out using the genetic algorithm, and the optimization process is shown in Figure 5. It can be seen from the figure that when the population evolved to 55 generations, the optimal solution can be obtained. The optimal orbital altitude was 609.782 km and the inclination angle was 96.142\({}^{\circ}\).  
The following situations were mainly considered, namely the number of orbital planes (P = 3, 4, 5, 6) And the number of constellation satellites (T = 12, 15, 16, 18, 20, 21, 24). Corresponding to the following 13 constellation configurations (12_3P, 12_4P, 12_6P, 15_3P, 15_5P, 16_4P, 18_3P, 18_6P, 20_5P, 21_3P, 24_3P, 24_4P, 24_6P), the specific meaning was that, taking 18_3P as an example, the constellation was composed of 18 satellites that were evenly distributed on three orbital planes, with six satellites on each orbital plane. The right ascension of each orbital plane was evenly distributed and the angle between the adjacent orbital planes was 120\({}^{\circ}\).  
According to the optimal deployment orbit altitude and the inclination of the satellite, and then according to the constellation spatial coverage model established above, the spatial coverage performance of each constellation can be calculated to judge the quality of the constellation's monitoring performance.  
The calculated airspace coverage performance of each constellation is shown in Figure 6. The airspace coverage performance represents the airspace performance indicators that can be covered by all the different constellations in a constellation cycle, which can be used to compare the performance of each constellation. It can be seen from the figure that with the increase in the number of satellites in the constellation, the airspace coverage performance of the constellation showed an upward trend as a whole. However, the constellation with the same number of satellites had a very different performance due to the different constellation configurations. Therefore, the reasonable design of the constellation configuration plays a very important role for improving the observation efficiency of the constellation.  
Figure 5: The optimization process of the orbital parameter design using the genetic algorithm.
## 5 DSS Techniques and Generalized Finite State Machine
### Interference Detection and Avoidance Procedure in DSS Techniques  
The procedure of interference detection and initiation of an interference avoidance procedure is presented in this section. For interference detection in both DSS techniques,we used PHY metrics like SINR, and MAC metrics like packet success rate (PSR), falling below a predefined threshold as an interference event trigger.  
Let us analyze an example where a satellite-GS link or satellite-ES link is already established and starts to experience interference. At the time an interference event is triggered, the nodes participating in the link must take some actions to correct the link quality. Since it is the receiver that will identify the interference event, it is up to the receiver to consult its synthetic interference map (in DSS1) or local energy map (in DSS2) and pick new free and non-interfered slots to replace the ones that are getting interfered. The receiver will then alert the transmitter node to alter the schedule of the transmission by removing the interfered slots and enabling the new MF-TDMA slots. In case the GS is responsible for deciding on spectrum allocation, the satellite will just inform the GS about the interference event detection, and then the GS will decide to move the interfered slots to other time-frequency slots. Next, the GS will inform the satellite about the newly chosen slots. In both cases, the link will recover by abandoning the interfered slots and replacing them with new interference-free slots.  
Let us also note that in case there is no MF-TDMA in place, but just an FDMA approach utilized on the link, the same link setup and interference avoidance procedures can be applied for channel selection instead of slot selection.
## 4 Parameter Sensitivity Analysis
### The Impact of Different Pipeline Radii on Surface Frost Heave Displacement  
Simulations of one year of freeze-thaw cycles were conducted for three pipeline radius conditions (0.4 m, 0.5 m, and 0.6 m) while maintaining a constant pipeline burial depth of 1.5 m, as shown in Figure 16. The results indicate that as the pipeline radius increases, the surface frost heave displacement gradually decreases. This trend suggests that an increase in the pipeline's volume ratio leads to a rise in local heat capacity. A larger heat capacity acts as a thermal buffer during the winter, reducing the temperature gradient in the soil surrounding the pipeline and consequently slowing down the rate of moisture migration towards the cold end and the freezing process. This slower freezing process reduces the frost heave effect in the soil. On the other hand, the increased pipeline radius also increases the contact area between the pipeline and the surrounding soil, which helps maintain a relatively stable temperature in the soil, thus reducing the extent of deformation during the freeze-thaw cycles.  
Figure 15: Third principal stress contour map for different initial saturation levels.
## 4 Basic Principle of Thermal Parameter Inversion
### The Objective Function  
With the aim of reducing the gap between the calculated temperature and the measured temperature of the simulated concrete to the minimum, by using the ordinary least squares, the mathematical expression model of the objective function is formulated as follows:  
\[Q(x)=\sum_{j=1}^{p}\sum_{i=1}^{q}\left(\frac{T_{ij}{}^{\prime}-T_{ij}}{T_{ij}} \right)^{2} \tag{17}\]  
\[x=(x_{1},x_{2},x_{3},\cdots x_{D})\in[L_{d},U_{d}] \tag{18}\]  
In the formula, \(Q(x)\) is the objective function of the optimization problem; \(x\) is the feasible solution of the optimization problem. Each solution is a \(D\)-dimensional vector; \(D\) is the number of parameters of the intelligent optimization problem; \(L_{d}\) and \(U_{d}\) are the upper and lower bounds of the solution space; \(T_{ij}{}^{\prime}\) and \(T_{ij}\) are the calculated value of finite element temperature and the measured value of field temperature at the \(j\) position of concrete at time \(i\); \(p\) is the entirety number of measuring points; and \(q\) is the total duration.  
In MATLAB R2010a, the parameter inversion is converted into an optimization problem through the application of the objective function. The thermal parameters are iteratively optimized by the WaOA algorithm, and then introduced into ANSYS 12.1 for temperature field simulation. When the fitness values of the objective function fulfil the convergence condition, the optimal thermal parameter values are outputted. Figure 4 shows the flowchart of MATLAB calling ANSYS for computation.  
Figure 4: Flowchart of MATLAB calling ANSYS for computation.
###### Abstract
By adjusting the accelerator to sulfur (A/S) ratio, the level of mono, di, and the polysulfide crosslinks in the cured network can be manipulated. The conventional accelerated-sulfur vulcanization (CV) system generally produces a cured network with 95% poly and di sulfidic crosslinks and 5% monosulfide. Therefore, for a CV system, the accelerator to sulfur ratio should maintain in the range 0.1-0.6. This means in the CV system, the sulfur dose is around 2-3.5 phr and the accelerator dose is around 0.4-1.2 phr. [12; 13; 14]. Unlike NR, both CR and BIIR are generally cured with ZnO. However, some amount of magnesium oxide (MgO) is also used along with ZnO to cure CR. The cured network of CR or BIIR mainly consists of C-C crosslinks [15; 16; 17]. It is very important to note that being a polar elastomer, CR has many unique properties such as oil, ozone and weather resistance. Similarly, the bromobutyl rubber also possesses resistance to ageing and weathering from atmospheric exposure due to its predominantly saturated polyisobutylene backbone of the butyl rubber. Moreover, bromobutyl rubber possesses low gas and moisture permeability. Therefore, blending of NR with CR or BIIR can exploit certain unique properties of these individual elastomers. However, because of the microstructural differences and the cure rate incompatibility, the blending and curing of NR with either CR or BIIR is very difficult. Therefore, a chemical that can act as a reactive compatibilizing or co-curing agent between NR and CR (or BIIR) is essential for enhancing the compatibility between NR and CR or BIIR. In our previous article, the co-curing effect of a combination of conventional accelerated sulfur (CV) and a bismaleimid on a 50/50 blend of NR/CR was reported based on a moving die rheometer (MDR-3000, Mon Tech, Buchen, Germany). Mainly based on this rheometer cure data, a mechanism responsible for the co-curing effect of the CV/bismalimide on the NR/CR blend has been proposed [18]. In the present work, the curing behavior of a 50/50 blend of NR with another grade of CR and a 50/50 blend of NR with another halogenated rubber was investigated; BIIR was again investigated in the presence of a combination of CV/bismaleimide using the same MDR. For a comparative analysis, the curing behavior of neat NR with CV system and the curing behaviors of neat CR and BIIR with metal oxides were also reported. To check the validity of the curing behavior and the mechanism proposed in reference [18], a specially designed testing protocol was employed in this study with the help of a rubber process analyzer (RPA).
## 2 Materials and Methods
### Data Collection  
The OD values were monitored at 680 nm by UV-VIS spectrophotometry (Optizen 2120 UV, Meacsys Co., Daejeon, Korea). A pH meter (Thermo Fisher Scientific Korea Ltd., Seoul, Korea) was used to measure pH. For cell counting of the cultures, _Microcyts_ sp. KW was stabilized with Lugol's solution and counted using the Hausser Scientific hemocytometer counting chamber [32]. Meanwhile, counting of _Anabaena_ filaments was performed with the Sedgwick-Rafter counting chamber, which allows for accurate quantification of fibrous cell density without cell/fiber layering. Sonication was employed to separate the _Anabaena_ and _Microcyts_ cells preparatory to counting. The measurement of this round and fibrous cell size and biovolume was performed according to [PERSON] et al. (1999). All of the test samples are itemized to at least 200 cells. Cell imaging was conducted by bright optical microscopy (Leica Microsystems, Heerbrugg, Switzerland). Reactive oxygen species (ROS) assays were performed following the method prescribed in the literature [33]. The zeta potentials were measured by a Zetasizer Nano-ZS particle analyzer (Malvern Panalytical, Malvern, UK). The structural confirmation of the ACs also followed the literature [21].
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
## 2 Modeling
### Looseness Fault Model  
If there is a manufacturing or assembly error in the bearing pedestal, the preload of the bolt may be be lost after harsh working loads, resulting in a gap between the bolt and its attached interface, which is called a looseness fault, as depicted in Figure 8. When a bolt becomes loose, the pedestal may shift within the gap caused by the loosening. Consequently, the relative position between the bearing housing and the elastic support will also be altered.  
Figure 8: Schematic of pedestal looseness.  
When the looseness fault occurs, the relative motion between the pedestal and the elastic support is shown in Figure 9, and the motion can be divided into three stages:  
* \(W_{b}<\alpha\): the pedestal and the elastic support are close together.
* \(0\leq W_{b}\leq\alpha\): the pedestal moves within the loosening gap.
* \(W_{b}>\alpha\): the pedestal moves away from the elastic support and is beyond the loosening gap.  
According to some scholars' studies on the looseness faults of rotor systems [22; 29; 30], the effects of the above looseness faults on the dynamic performance of rotor systems can be described by the piecewise linear stiffness and damping:  
\[k_{\mathrm{support}}=\begin{cases}k_{s}&W_{b}<0\\ k_{l}&0<W_{b}<\alpha\\ k_{s}&W_{b}>\alpha\end{cases} \tag{17}\]  
\[c_{\mathrm{support}}=\begin{cases}c_{s}&W_{b}<0\\ c_{l}&0<W_{b}<\alpha\\ c_{s}&W_{b}>\alpha\end{cases} \tag{18}\]  
where \(k_{s},c_{s}\) are the stiffness and damping provided by the elastic support when no looseness failure occurs, \(k_{l},c_{l}\) are the stiffness and damping provided by the connection structure when looseness failure occurs; \(W_{b}\) is the longitudinal displacement of the pedestal, and \(\alpha\) is the length of the loosening gap.
# Integration of LTE 230 and LTE 1800 in Power Wireless Private Networks
## 4 Integration of the Access Network
### Synchronization Signal of LTE 230  
In the LTE 230 system, \(T_{s}{=1/(2000\times 64)}\)s is defined as the basic unit of time. One radio frame occupies \(T_{f}=3200\cdot T_{s}=25\)ms in the time domain and consists of five subframes of length 5 ms. The frame structure is shown in Figure 7.  
Subframe 0 is the downlink subframe, while Subframes 2, 3, and 4 are uplink subframes. Subframe 1 is a special subframe with the three fields DwPTS, GP, and UpPTS, whose functionalities are the same as in LTE 1800. The supporting uplink-downlink configurations are listed in Table 3.  
A physical channel of length 25 kHz in frequency is defined as a subband, which can be classified as a synchronized subband, broadcast subband, or service subband.  
In comparison with LTE 1800, there is no difference in the generation of the original 62-point sequence in the frequency domain. The difference is in the way of mapping the sequence to resource elements given LTE 230's small bandwidth.  
The PSS and SSS use the same method to solve this problem. The 62-point sequence is divided into seven parts with lengths \(\{4,11,11,11,11,11,3\}\). Each part is mapped to an OFDM symbol separately. For the PSS, the sequence \(v_{u}(n)\) is mapped to the resource elements according to  
\[\begin{split} a_{kJ}&=d(k),k=0,\ldots,3,l=7\\ a_{kJ}&=d(4+11(l-8)+k),k=0,\ldots,10,l=8,\ldots,12\\ a_{kJ}&=d(4+11(l-8)+k),k=0,\ldots,2,l=13.\end{split} \tag{12}\]  
For the SSS, the sequence \(d(n)\) is mapped to resource elements according to  
\[\begin{split} a_{kJ}&=d(k),k=0,\ldots,3,l=0\\ a_{kJ}&=d(4+11(l-8)+k),k=0,\ldots,10,l=1,\ldots,5\\ a_{kJ}&=d(4+11(l-8)+k),k=0,\ldots,2,l=6.\end{split} \tag{13}\]  
Then, 64-point IFFT is operated in each OFDM symbol to transform frequency domain data into time domain data. The time domain signal is finally generated by connecting the seven parts in order, with a length of 448 points.  
Figure 8 illustrates the distribution of the PSS and SSS in an LTE 230 subframe.  
Figure 7: Frame structure of LTE 230.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Uplink–Downlink** & \multicolumn{3}{c}{**Downlink-to-Uplink**} & \multicolumn{3}{c}{**Subframe Number**} \\ \cline{2-6}
**Configuration** & **Switch-Point Periodicity** & **0** & **1** & **2** & **3** & **4** \\ \hline
0 & 25 ms & D & S & U & U \\ \hline \hline \end{tabular}
\end{table}
Table 3: Uplink–downlink configurations of LTE 230.
# A Review on Ultrafast Laser Enabled Excellent Superhydrophobic Anti-Icing Performances
## 4 Advances and Challenges of ULSASs
### Passive/Active Combined Superhydrophobic Anti-Icing Surfaces  
In some severe application scenarios, the single passive anti-icing of SHSs is still limited to eliminating ice formation and accretion. Combining with other active anti-icing technologies to achieve passive/active combined anti-icing could overcome the disadvantages [162; 163; 164; 165]. Among various hybrid anti-icing methods, electrothermal/superhydrophobic and photothermal/superhydrophobic anti-icing surfaces have been developed most widely. Here we introduce the main advances in the two methods.
## 3 Results
### Satellite Signature Effect of BDS
From the above phenomena shown by the results of different stations, some SLR stations have high performance in tracking BDS satellites. CSPAD stations including Mt Stromlo, Shanghai, Graz, Wettzell, and Beijing, as well as MCP stations Yarragadee and Matera, can be considered high-performing stations suitable for BDS. These high-performing stations have a sufficient number of stable observations for each BDS satellite, and the orientation of the satellite and station, such as elevation and nadir angles, have a smaller impact on the SLR residuals. Moreover, the RMS and mean offset of the SLR residuals of these seven stations were below 60 mm and 20 mm,  
Figure 5: The variation in SLR residuals for six satellites with regard to the nadir angle observed by Shanghai. respectively. Statistical results in Table 3 show that high-performing stations can reduce the RMS of SLR residuals by about 1 to 3 mm.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 5 ETC-Based Urban Traffic Management: Applications and Practices
### Traffic Prediction and Artificial Intelligence Technology
Recent advances in graph neural networks (GNNs) have demonstrated a significant potential for enhancing ETC-based traffic analysis through sophisticated spatiotemporal modeling approaches. These developments address critical challenges in capturing complex relationships within transportation networks that are highly relevant to ETC system applications. The integration of attention mechanisms with graph structures has shown particular promise. [PERSON] et al. introduced STGAFormer, a spatial-temporal gated attention transformer that enhances long-term prediction capabilities and handles sudden traffic incidents through improved temporal feature extraction [42]. This approach is directly applicable to ETC systems, where gantry networks can benefit from attention-based mechanisms to focus on relevant spatial relationships during varying traffic conditions, particularly valuable for managing unexpected events that affect traffic flow patterns captured by ETC data. Multi-dimensional temporal pattern recognition has emerged as another crucial advancement. [PERSON] et al. proposed COOL, a conjoint spatiotemporal graph neural network that models high-order spatiotemporal relationships through heterogeneous graph construction [43]. The conjoint learning framework aligns well with ETC data characteristics, where spatial relationships between the gantries and temporal sequences of vehicle passages naturally form complex graph structures, requiring sophisticated modeling approaches. For multi-modal transportation applications, [PERSON] et al. developed the TMS-GNN, which specifically addresses traffic-aware multistep prediction while considering sensitivity to urban traffic conditions [44]. This methodology offers valuable insights for ETC-based transit management, where bus networks' coexistence with general traffic mirrors the operational environment of urban ETC systems, suggesting potential applications in integrated ETC-transit optimization strategies. These GNN advancements provide theoretical foundations and technical approaches that can enhance ETC data analysis capabilities, particularly in modeling complex network relationships, handling temporal dependencies, and integrating multi-source traffic information within urban transportation systems. Overall, although some studies do not directly utilize ETC data, their approaches and methods provide valuable references. Simultaneously, artificial intelligence technology applications in ETC data traffic prediction have evolved from simple statistical analysis to complex deep learning models, from single-modal data processing to multi-modal fusion analysis, and from black-box prediction to explainable artificial intelligence. These technological advances provide robust technical support for the future intelligent and precise applications of ETC data fusion in urban traffic management.