esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Limitations and Future Works  
This paper proposes a calculation method for ultimate vertical bearing capacity of SDRN pile considering two failure modes. The calculation result is compared with the field test result and numerical simulation result by PLAXIS. Some valuable conclusions concerning the pile responses are derived. However, there are also some limitations in this paper:  
* This paper derives the calculation method from that of similar common pile foundation mainly considering the construction technology. However, the shear strength on the interface between cemented soil and soil is also of vital importance for the bearing capacity, which is related to the mechanical properties of the cemented soil. The coefficient could also take this into consideration.
* One or two more field tests could be implemented to support this calculation method more forcefully by changing the thickness of cemented soil around the pile.  
Therefore, several further investigations on the pile responses could be conducted. Some laboratory experiments could be designed for the shear strength parameters of cemented soil. Field tests concerning the collaborative work between cemented soil and soil both along the pile shaft and in the expended cemented soil area at the pile tip also need to be carried out. Finally, this paper is focus on the vertical bearing capacity of SDRN pile, the horizontal bearing characteristic is also of vital study value.
## 3 Dynamic Routing Algorithm for Inter-Satellite Network Based on Q-Learning
### Q-Learning Routing System Model
#### 3.2.2 Business Characteristics  
The business \(S^{k}\) mainly includes the following characteristics, the source node \(s^{k}\) (the node entering the network), the destination node \(t^{k}\) (the node leaving the network), the  
Figure 5: SDN routing framework based on Q-learning.  
bandwidth requirement is set to \(b^{k}\), other QoS requirements include transmission delay and packet loss rate, which are represented by \(D^{k}\) and \(L^{k}\), respectively, referring to the highest acceptable delay and packet loss thresholds for this service.  
Let \(x_{ij}^{k}\) denote whether the SDN controller assigns the link to transmit the service flow, if the service flow passes through the link, then set \(x_{ij}^{k}\) to 1, otherwise it is 0.  
The optimization goal is to balance the link load in the network and reduce the transmission delay as much as possible on the basis of ensuring the QoS requirements, that is, to minimize the Formula (2a).  
\[\lambda\underset{(i,j)\in E}{max}\text{{load}}_{ij}+(1-\lambda)\sum\
olimits _{k\in K}\sum\
olimits_{(i,j)\in E}x_{ij}^{k}\cdot d_{ij}\] (2a) where \[\text{{load}}_{ij}\] is the bandwidth utilization of the link \[(i,j)\], \[\text{{load}}_{ij}=\frac{\sum\
olimits_{k\in K}x_{ij}^{k}\cdot b^{k}}{b_{ij}} \times 100\%\], \[\sum\
olimits_{(i,j)\in E}x_{ij}^{k}\cdot d_{ij}\] denotes the cumulative delay of the selected path, parameter \[\lambda\in[0,1]\] used to adjust the proportion of link load and delay.  
The main constraints under this model are as follows:  
\[\sum\limits_{(i,j)\in E}x_{ij}^{k}-\sum\limits_{(j,i)\in E}x_{ij}^{k}=\left\{ \begin{array}{ll}1,&i=s^{k}\\ 0,&i\
eq s^{k},t^{k}\\ -1,&i=t^{k}\end{array}\right. \tag{2b}\]  
\[x_{ij}^{k}\cdot b^{k}\leq\min(b_{ij}-\sum\
olimits_{k_{0}\in K_{k_{0}}\
eq x_ {ij}^{k_{0}}\cdot b^{k_{0}}}),\forall k\in K \tag{2c}\]  
\[\sum\
olimits_{(i,j)\in E}\sum\
olimits_{k\in K}x_{ij}^{k}\cdot b^{k}\leq b_{ij} \tag{2d}\]  
\[\sum\
olimits_{(i,j)\in E}x_{ij}^{k}\cdot d_{ij}\leq D^{k},\forall k\in K \tag{2e}\]  
Constraint (2b) represents the flow conservation in the traffic network link, Formula (2c) means that the current available bandwidth of the link meets the transmission rate requirements of its business flow, and constraint Formula (2d) means that the traffic carried by the link does not exceed its link bandwidth capacity. The following constraint expression shows that in addition to meeting the transmission rate requirements, other QoS requirements of the business must also be met. Based on the routing framework of SDN, the Q-learning algorithm is further introduced into the routing problem, the reward function is designed, and a dynamic routing algorithm based on Q-learning is proposed.
## 4 Bespoke Space System Design
### Orbit Design
A spacecraft orbit should be sufficiently high to ensure a suitable operational life for the spacecraft prior to deorbit, while also ideally complying with the UN Space Debris Mitigation Guideline to \"limit the long-term presence of spacecraft and launch vehicle orbital stages in the low-Earth orbit (LEO) region after the end of their mission\" [35]. Generally, this guideline is considered to be met if the spacecraft will naturally deorbit within 25 years of the end of the mission. Assuming a power law fit to the 1976 standard atmosphere [36], the time for a spacecraft to naturally decay to a certain altitude, \(h_{1}\), as a function of time can be defined as [37]  
\[t=\ \frac{m}{C_{D}\ A\ \Lambda\ \sqrt{\mu\ R_{\bigoplus}}}\ \frac{\left(\left(h_{0}-R_{ \bigoplus}\right)^{1+\gamma}-\left(h_{1}-R_{\bigoplus}\right)^{1+\gamma} \right)}{1+\gamma}\ 1000^{-\gamma} \tag{6}\]  
where \(h_{0}\) is the initial spacecraft altitude, \(C_{D}\) is the coefficient of drag, taken as 2.2, and \(\gamma\) and \(\Lambda\) are coefficients of the power law taken as 7.201 and \(10^{7}\), respectively. For a spacecraft with a mass, \(m\), of 100 kg and a cross-sectional area, \(A\), of 1 m\({}^{2}\) (similar to those of SkySat), and with de-orbit assumed to occur at an altitude of 100 km, the time to deorbit from a range of possible orbit altitudes are shown in Table 3. A spacecraft in an orbit of 500 km or lower will deorbit naturally in less than five years, meaning that significant on-board propulsion would be required to maintain the spacecraft altitude and meet the requirement for a minimum operational life of five years. A 600 km orbit seems suitable as it requires the minimum number of spacecraft while still meeting the 25 year natural deorbit requirement and providing \(<\)1 m GSD.
## 3 Challenges
### Manufacturing Costs  
Generally, CR-wireless sensors have been deployed in large numbers. Therefore, the cost should be significantly low. In contrast to conventional WSNs, which require less memory and computation capability, CR-WSNs require moderate memory and computational capabilities. To reduce the hardware cost, an algorithm that requires less computational power and memory should be developed. Designing such an algorithm is a challenging issue. Furthermore, CR wireless sensors should contain intelligent radio, application specific positioning systems (e.g., GPS), energy harvesting unit _etc._ which obviously increase the production cost.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### The Parameters
#### 5.1.2 Energy Consumption Model  
In order to further assess the network performance, we explore the communication cost for the tracking task. Since the computational cost in a wireless sensor network is usually neglected compared to the communication cost, we focus on the analysis of power consumption for communication. Currently, there have been a number of studies on measuring the energy dissipation per transmitted bit [5; 20]. Here, we adopt the energy model presented in [5] to describe the energy dissipation. Thus, the energy dissipation of a transmitter \(E_{Tx}\) and a receiver \(E_{Rx}\) can be modeled as follows [5]:  
\[E_{Tx} = \left\{\begin{array}{ll}tE_{elc}+t\varepsilon_{fs}d^{2},&d<d_{o }\\ tE_{elc}+t\varepsilon_{mp}d^{4},&d\geq d_{o}\end{array}\right. \tag{12}\] \[E_{Rx} = tE_{elc} \tag{13}\]  
where \(t\) is the data packet size, \(E_{elc}\) denotes the energy consumption of the electronic circuitry, \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) depend on distance \(d\) between the transmitter and the receiver for maintaining an acceptable bit-error rate and \(d_{o}\) is a transmission threshold. The values of the simulation parameters are set as [5]: \(E_{elc}=50\) nJ/bit, \(\varepsilon_{fs}=10\)pJ/bit/m\({}^{2}\), \(\varepsilon_{mp}=0.0013\)pJ/bit/m\({}^{4}\), \(d_{o}\) is the transmission range \(R\). Observe that the key to the energy consumption is the transmission distance and the data size. In our case, given a fixed transmission distance, how to reduce the data size for transmission becomes an important issue.
## 3 Results and Discussion
### Influence of the Vapor Treatment on the Structure and Transparency  
The XRD patterns of the PLA nanofibers before and after the alcoholic treatment are almost the same, indicating that the PLA structures do not change during the treatment.  
Moreover, the chemical bonds of the membrane can be analyzed by FTIR spectroscopy. Comparing the spectra of the films before and after the alcoholic treatment, it was found that the normalized spectra did not change significantly. In Figure 8, the position of the peak does not change significantly, but the intensities of the peaks change slightly. In order to investigate whether the functional groups of the membrane undergo chemical changes after alcoholic treatment with a low molecular weight, the PLA membranes were examined by ATR-FTIR, which is an approach to test the chemical bonds via molecular vibration and rotation after the infrared light interacting with the molecules. As shown in Figure 8, the peak at 1753 cm\({}^{-1}\) is a stretching vibration mode of carbon-oxygen double bond (C=O), because the PLA contains the functional group of aliphatic carbonate esters. The peak at 1183-1088 cm\({}^{-1}\) is the stretching vibration mode of carbon-oxygen-carbon ester group (C-O-C) [23; 24; 25; 26]. The characteristic peak from the carbonyl groups of aliphatic carboxylic acids (C=O stretching vibration) at 1711 cm\({}^{-1}\) in spectrum of the shellac nanofibers does not change before and after the treatment [27]. The peaks at 2929 cm\({}^{-1}\) and 1458 cm\({}^{-1}\) denote the -CH\({}_{2}\) shear vibration absorption and the -CH\({}_{2}\) symmetric bending vibration absorption overlap peak, respectively [28]. This shows that the shellac contains the functional group of aliphatic hydrocarbons. The O-H bending vibration is identified at 1250 cm\({}^{-1}\)[29]. The absorption bands at 1250-1039 cm\({}^{-1}\) may be due to the stretching vibrations of C-O and C-C bonds [30]. For PLA/shellac/PLA membranes, the characteristic peaks from the shellac completely overlap with the absorption bands of PLA, and thus these bands are not available for differentiation.  
Figure 9 shows the transparency spectra of PLA/shellac/PLA sandwich structural membrane before and after the treatment with alcohol. One can see that in the whole visible optical range the membrane after the treatment with alcohol has a higher transparency. Also, the wavelength does not affect the transparency significantly.  
Figure 8: (**a**) The normalized attenuated total reflection–Fourier transform infrared spectrometer (ATR–FTIR) spectra of samples prepared before treated with the alcoholic vapor with a low molecular weight; (**b**) the ATR-FTIR spectra of samples prepared after treated with the alcoholic vapor with a low molecular weight.
# Geophysical Study of a Large Landslide Affecting the Urban Area of Albuuuelas (S Spain)
## 3 Geophysical Methods
### Inversion of the Dispersion and H/V Curves  
The H/V and/or dispersion curves can be inverted to obtain an estimation of the shear-wave velocity profile in the site under study. These curves present different sensitivities with respect to the characteristics of the sedimentary cover. While the dispersion curve can provide more detailed information on the different sediment layers, the H/V curve can more reliably determine the total thickness of the sedimentary cover from the resonant frequency, reflecting the contrast between sediments and basement rocks. Thus, a joint-inversion of H/V and the dispersion curve (e.g., [25; 54; 55]) would be preferred, as long as possible, to obtain more detailed \(V_{\rm S}\) profiles of the subsoil.  
The inversion of the experimental data has been conducted following the neighborhood algorithm [56; 57], which is a stochastic direct research method for non-linear inversion. The inversion process explores a huge number of possible solutions or models with a certain number of sedimentary layers, whose properties (S-wave velocity, P-wave velocity, density and layer thickness) are within preset margins. In general, prior knowledge of the type of soil through some geotechnical method (e.g., borehole drills and downhole experiments) helps to restrict the number of layers and their corresponding thickness, as well as the S-wave velocity ranges. Density and Poisson's ratio have been established for all layers in the range of 1600-2200 kg/m\({}^{3}\) and 0.2-0.5, respectively, which corresponds to the characteristics of the materials in the studied landslide.  
The theoretical response of each of the models obtained as a solution (in terms of ellipticity of the fundamental mode Rayleigh wave or the dispersion curve) is contrasted with the corresponding experimental curves by means of a misfit function [58], given as the result of how much the provided solution fits the type of soil under study.
## 3 System Model and Problem Formulation
### System Model
The channel gain \(g_{n}^{edg}\) from the TaV to the BS/RSU is modeled by  
\[g_{n}^{edg}=10^{\left(\mathrm{Ve}_{\mathrm{Ant}}^{\mathrm{Gain}}+\mathrm{B}_{ \mathrm{Ant}}^{\mathrm{Gain}}-\left(\mathrm{PL}_{\mathrm{V2I}}(\mathrm{d}_{n}^{ edg})+\mathrm{S}_{\mathrm{V2I}}\right)-\mathrm{B}_{\mathrm{No}}^{\mathrm{Fig}} \right)/10}, \tag{3}\]  
where \(\mathrm{Ve}_{\mathrm{Ant}}^{\mathrm{Gain}}\), \(\mathrm{B}_{\mathrm{Ant}}^{\mathrm{Gain}}\), and \(\mathrm{B}_{\mathrm{No}}^{\mathrm{Fig}}\) are vehicle antenna gain, BS/RSU antenna gain, and BS/RSU noise figure, respectively. The path-loss model \(\mathrm{PL}_{\mathrm{V2I}}(\mathrm{d}_{n}^{edg})\) of the V2I links is [24]  
\[\mathrm{PL}_{\mathrm{V2I}}(\mathrm{d}_{n}^{edg})=128.1+37.6\mathrm{log}_{10}( \mathrm{d}_{n}^{edg}/1000),\quad(\mathrm{d}_{n}^{edg}\quad in\quad m), \tag{4}\]  
where \(\mathrm{d}_{n}^{edg}\) represents the distance between TaV and BS/RSU. The shadowing value \(\mathrm{S}_{\mathrm{V2I}}\) denotes a Gaussian random value whose standard deviation is 8 dB. Similarly, the channel gain between TaV and its SeV is modeled by  
\[g_{n}^{n^{\prime}}=10^{\left(2*\mathrm{Ve}_{\mathrm{Ant}}^{\mathrm{Gain}}-\left( \mathrm{PL}_{\mathrm{V2V}}(\mathrm{d}_{n}^{n^{\prime}})+\mathrm{S}_{\mathrm{ V2V}}\right)-\mathrm{Ve}_{\mathrm{No}}^{\mathrm{Fig}}\right)/10}, \tag{5}\]  
Figure 1: System model. where Veh\({}_{\text{Noi}}^{\text{Fig}}\) represents vehicle noise. The path-loss models \(\text{PL}_{\text{V2V}}(\text{d}_{n}^{n^{\prime}})\) of the V2V links for line-of-sight conditions are given by [25]  
\[ \begin{split}&\text{PL}_{\text{V2V}}(\text{d}_{n}^{n^{\prime}})=\\ &\left\{\begin{array}{l}2.2.7*\log_{10}3+41+20*\log_{10}(freq/5), dist\leq 3\\ 2.2.7*\log_{10}(\text{d}_{n}^{n^{\prime}})+41+20*\log_{10}(freq/5),\text{d}_{n }^{n^{\prime}}\leq 4*(\text{H}^{\text{Veh}}-1)^{2}*freq/c\\ 40*\log_{10}(\text{d}_{n}^{n^{\prime}})+9.45-17.3*\log_{10}((\text{H}^{\text{ Veh}}-1)^{2})+2.7*\log\!10(freq/5),\text{else}\end{array}\right.,\end{split}  \tag{6}\]  
where \(\text{d}_{n}^{n^{\prime}}\), \(freq\), and \(\text{H}^{\text{Veh}}\) represent the distance between TaV and SeVs, the carrier frequency, and vehicle antenna height, respectively. The standard deviation of \(\text{S}_{\text{V2V}}\) is 3 dB. Thus, transmission rates from TaVn to BS/RSU and SeVn can be expressed as  
\[\text{R}_{n}^{alg}=\text{Blog}_{2}(1+\gamma_{n}^{alg}), \tag{7}\]  
\[\text{R}_{n}^{v2v}=\text{Blog}_{2}(1+\gamma_{n}^{v2v}). \tag{8}\]
# A Methodological Approach to the Study of Retroreflective Pavements
## 2 Materials and Methods
### Materials
#### 2.1.3. Pigments  
Various types of pigments were used to give the desired colour to the binders. No pigment was used with the 50/70 conventional binder to produce a black surface, which served as a reference, while the following pigments (see Figure 3) were used with the pigmentable synthetic bitumen:  
* Titanium dioxide (TiO\({}_{2}\)) to give a white shade.
* Iron composites of different colours: ochre, brown, and red.
* Combination of silica, sulphur, aluminium, and sodium composites to a give a blue shade.  
A qualitative elemental analysis was conducted using wavelength-dispersive X-ray fluorescence methodology (WDXRF). The analysis was conducted with a rapid scan (EZ analysis) across all elemental lines, and SQX calculations were employed to ascertain the elemental composition of each sample. Table 3 shows the main components of each pigment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Standard** & **50/70** & **Synthetic** \\ \hline Penetration at 25 \({}^{\circ}\)C (0.1 mm) & EN 1426 (2015) & 61 & 20–50 \\ \hline Softening Point (\({}^{\circ}\)C) & EN 1427 (2015) & 50.9 & \(\geq\)85 \\ \hline Fraass Breaking Point (\({}^{\circ}\)C) & EN 12593 (2015) & \(-14\) & \(\leq\)\(-20\) \\ \hline Flash Point (\({}^{\circ}\)C) & EN ISO 2592 (2018) & 280 & \(>\)270 \\ \hline \multicolumn{4}{c}{**Residue after Ageing**} \\ \hline Mass Variation (\%) & EN 12607-1 (2015) & 0.1 & \(<\)1.5 \\ \hline Retained Penetration (\% o.p.) & EN 1426 (2015) & 66 & \(>\)80 \\ \hline Softening Point Increase (\({}^{\circ}\)C) & EN 1427 (2015) & 7.6 & \(<\)10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the binders: 50/70 and pigmentable synthetic binders provided by the supplier.  
Figure 3: Pigments used in this work.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element (\% Mass)** & \multicolumn{5}{c}{**Pigment**} \\ \cline{2-6}
**(\({}^{\circ}\))** & **Ochre** & **Blue** & **White** & **Brown** & **Red** \\ \hline Fe & 95–99 & - & - & 42–47 & 95–99 \\ Ti & - & - & 95–99 & - & - \\ Ca & - & - & - & 48–53 & - \\ K & - & residual & - & - & - \\ S & residual & 20–25 & - & residual & - \\ Si & - & 31–36 & residual & residual & residual \\ Al & - & 19–24 & residual & residual & - \\ Mg & - & - & - & residual & - \\ Na & - & 16–21 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main components of the pigments obtained from the XRF analysis.
# Coexistence in Wireless Networks: Challenges and Opportunities
## 4 Spectrum Sharing and Coexistence  
The mm Wave band, boasting higher frequencies and a wider spectrum than traditional frequency bands, proves to be an ideal candidate for future wireless networks, emphasizing high-speed, short-range communication [54]. As an emerging technology, millimeter wave is expected to be interoperable with existing systems and capable of collaborating with other technologies to enhance overall network performance. Consequently,  
Figure 4: Multi-layer satellite networks [53].  
addressing coexistence challenges has become a crucial aspect of next-generation wireless communications [55].  
The coexistence of wireless networks is essential for effective communication between various technologies, such as satellite systems, 5G, and mm Wave. For each of these, specific strategies are needed to reduce interference and maximize spectrum utilization. Because of their narrow propagation ranges and great sensitivity to interference, millimeter-wave coexistence in 5G presents difficulties. While Dynamic Spectrum Sharing (DSS), as demonstrated by Verizon's implementation, allows for smooth cohabitation across mm Wave and sub-6 GHz bands, beamforming and massive MIMO reduce signal overlap. Dynamic spectrum access strategies have been shown to improve dependability in real-world urban microcell experiments [56].  
Spectrum sharing techniques like Carrier Aggregation (CA), which enable LTE and 5G to function on different carriers while maximizing throughput, are necessary for 5G and LTE coexistence. Operators such as NTT Docomo have implemented network slicing, which reduces interference by allowing independent service operation within shared networks. Spectrum sharing issues arise when satellites and terrestrial networks interact. Multi-operator studies have shown that Reverse Spectrum Allocation dynamically reallocates frequencies in order to reduce interference [57]. Furthermore, AI-based power control reduces interference in terrestrial 5G networks by regulating satellite uplink transmissions.  
Figure 5 illustrates the coexistence of the following three major communication systems: radar communication, terrestrial communication, and satellite communication. The central \"Coexistence\" node signifies the interplay and spectrum sharing between these technologies. The arrows indicate bidirectional interactions, showing how each system influences and depends on others.
## 4 Design, Development, and Implementation
### Functioning of Code Boxes (Blueprints)  
To illustrate the practical functioning of the programming system used in the development of the virtual reality (VR) experience, several examples are presented to demonstrate how interactions and animations are generated for user engagement during immersion. Figure 11 displays six implemented functionalities, contrasting the programming environment with user interaction and visualization in VR.  
Figure 11a shows the interactive dropdown menu, which includes three main buttons: Restart Level, Exit Experience, and Return to Last Saved Point. This menu is designed to support quick decision-making during the simulation and is programmed using Blueprints, with each button linked to a specific in-game action, ensuring a fluid and user-friendly interface. Figure 11b illustrates the wheelchair movement logic within the virtual environment. The user can move forward, backward, and rotate, simulating realistic wheelchair dynamics. The programming integrates colliders and physics that replicate real-life maneuverability, allowing the evaluation of accessibility parameters such as turning space and path clearance in different areas of the house.  
Interactions with common objects are highlighted in Figure 11c and Figure 11d, which depict doors and light switches, respectively. In the case of Figure 11c, doors open in response to proximity and interaction via the virtual hands, activating animations that simulate a 90\({}^{\circ}\) opening, aligned with accessibility regulations. Figure 11d shows how light switches can be toggled on and off, enhancing immersion and mimicking routine daily  
Figure 10: Flowchart made to link VR virtual reality glasses with computer.  
actions. Figure 11e presents widgets--visual elements placed throughout the experience that deliver instructions, tasks, or reminders to the user. These are designed to improve understanding of the navigation and actions to be performed, supporting autonomy within the virtual environment. Finally, Figure 11f displays the implementation of save points, which allows the user to return to a safe location in the environment in case of errors, collisions, or technical issues. This feature, also developed using Blueprints, ensures uninterrupted evaluation sessions and improves the overall user experience.  
Together, these functionalities are seamlessly integrated into the simulation, offering an immersive, functional, and user-centered experience. At the same time, they allow for objective measurement of accessibility performance--such as the number of collisions or time spent in key areas--supporting design validation and decision-making prior to physical implementation.