esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Scenario and Specifications
### LoRa Specifications  
To determine the LoRa communication parameters used in this study, the following steps were undertaken. First, the bandwidth (BW), a fundamental parameter in LoRa communication, was set. Although BW can be chosen among 125 kHz, 250 kHz, and 500 kHz, this study selected 125 kHz--the narrowest available option--because a higher bandwidth would require a higher bit rate for the same spreading factor, thereby reducing the link margin in the power-constrained IoT-NTN environment. Additionally, considering the typically low data transmission requirements in AloT applications, the 125 kHz option is deemed appropriate. The ISM band was set to 920 MHz in accordance with South Korean regulations.  
Next, the bit rate (\(R_{b}\)) was calculated using the following equation [12]:  
\[R_{b}=SF\cdot\frac{BW}{2^{SF}}\cdot\text{RateCode}, \tag{2}\]  
Figure 3 shows the relationship between the spreading factor (SF) and the bit rate for LoRa communication. Note that this experiment is a simulation study. For this study, \(SF=8\), the coding rate (CR) was set to 4/5, and \(BW=125\) kHz, yielding a bit rate of 2604 bps [15; 28].  
\begin{table}
\begin{tabular}{l l} \hline
**Parameter** & **Value** \\ \hline Platform & MATLAB R2024a \\ \hline Simulation Period & 1 January 2025–14 January 2025 \\ \hline Time Step & 1 min \\ \hline Elevation Mask & 5\({}^{\circ}\) \\ \hline Orbit Altitudes & 200 km and 300 km (VLEO); 600 km (LEO) \\ \hline Ground Stations & Seoul, Berlin, Oslo, and Houston \\ \hline Constellation Pattern & Dual-group Walker–Delta (i, p, n, F) \\ \hline Link Margin Threshold & \(\geq\)0 dB \\ \hline \end{tabular}
\end{table}
Table 1: Key simulation parameters.  
Figure 3: Relationship Between SF and Bitrate(BW = 125 KHz).  
The receiver sensitivity was set to \(-126\,\mathrm{dBm}\) for \(SF=8\). Sensitivity is determined by the signal-to-noise ratio (SNR), which decreases with increasing SF. The SNR was calculated as follows [12]:  
\[SNR_{(dB)}=-7.5\,\mathrm{dB}-(SF-7)\cdot 2.5\,\mathrm{dB}. \tag{3}\]  
For \(SF=8\), the SNR was set to \(10\,\mathrm{dB}\). The required \(E_{b}/N_{0}\) was then computed as follows [12]:  
\[(E_{b}/N_{0})_{\mathrm{required}}=SNR+10\log\biggl{(}\frac{BW}{R_{b}}\biggr{)}. \tag{4}\]  
Using \(SNR=10\) dB, \(BW=125\) kHz, and \(R_{b}=2604\) bps, the required \(E_{b}/N_{0}\) was calculated as \(6.8\,\mathrm{dB}\). The transmission power for the LoRa uplink (UL) was set to \(-10\) dBW (\(0.1\,\mathrm{W}\)), ensuring efficient power usage while maintaining reliable data transmission.  
The system temperature and \(G/T\) were computed as follows [12]:  
\[\begin{split} G/T&=G_{\mathrm{ant}}-10\log(T_{ \mathrm{sys}}),\\ T_{\mathrm{sys}}&=T_{\mathrm{ant}}+T_{\mathrm{rec}}, \\ T_{\mathrm{rec}}&=\left(10^{\frac{\mathrm{W}}{10}}-1 \right)\cdot\frac{290}{G_{\mathrm{ant}}}.\end{split} \tag{5}\]  
The calculated system temperature (\(T_{\mathrm{sys}}\)) was \(693.7\,\mathrm{K}\), and the \(G/T\) value was \(-26.27\,\mathrm{dB}/\mathrm{K}\).
## 3 Framework of Forecasting AOG Models in Aviation
### Case Study: Delta Air Lines and Lufthansa Technik  
Delta Air Lines parthered with Airbus to develop predictive maintenance solutions using the Skywise platform. This system analyzes real-time data from aircraft systems with machine learning to predict component failures. Since implementing this approach, Delta has optimized maintenance schedules and reduced unscheduled repairs and maintenance-related delays by 15-20% across its fleet (Figure 5).  
Lufthansa Technik has similarly integrated predictive maintenance across various fleets, including United Airlines' Boeing 737 aircraft. This has reduced unscheduled removals and extended component \"time on the wing\", minimizing maintenance disruptions. Airlines using predictive solutions, like Prognos by AFI KLM, report maintenance delays dropping by up to 35%, with the earlier detection of failure patterns improving resource management. The impact of predictive maintenance is evident in reduced AOG events, operational disruptions, and flight cancelations. For instance, Delta achieved a 20% reduction in delays by 2023, and United Airlines reduced unscheduled removals by 22% for its Boeing 737 fleet. These examples demonstrate how real-time data integration and predictive analytics enhance efficiency and reliability in aviation maintenance.
# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 2 Structure, Health States, and Maintenance Strategies of the Offshore Wind Turbine
### Maintenance Strategy  
To reduce the downtime and the maintenance costs of the OWT, as considered in the existing maintenance models, three different maintenance strategies [26], i.e., corrective maintenance, periodic maintenance, and condition-based maintenance, are adopted. However, in this work they are further refined by considering the three new measures outlined in Section 1. The periodic maintenance will be delivered via two levels of services, namely, \"basic service\" and \"advanced service\". Since \"basic service\" is essential in keeping the turbine operating at high efficiency in the long term and preventing the acceleration of the degradation of wind turbine components, in the new PN model it will be conducted every six months and will last for one day per turbine per instance [5; 18]. Since the issues found in \"basic service\" are usually minor problems, in the PN model developed it is assumed that all of these will be fixed in one visit. By contrast, the \"advanced service\" will extend the activities to inspect the key wind turbine components. In this study, the period between the \"advanced service\" ranges from 3 months to 5 years in order to investigate the influence of this time interval on the downtime and maintenance costs of the OWT. The time required for carrying out \"advanced service\" is usually longer than the time for \"basic service\". For simplicity, \"advanced service\" is assumed to last for two days per turbine per instance in the model. To further increase the safety and reliability of the wind turbine and utilise the maintenance resources more effectively, a full inspection is arranged following a major repair. Finally, considering that condition-based maintenance is highly dependent on the fault detection capability of the CMS, the influence of the fault detection capability of the CMS on the downtime and maintenance costs are also investigated in the paper. To the best of the authors' knowledge, similar research has not been completed previously.
## 2 Materials and Methods
### Equipments  
Scanning electronic microscopy (SEM) images were obtained with a Field-emission scanning electron microscope (S-4800, Hitachi, Tokyo, Japan). High resolution transmission electron microscopic (HR-TEM) images were taken by a TECNAL G2 F20 (FEI Company, Hillsboro, OR, USA) electron microscope operating at 200 kV. Fourier transform infrared (FT-IR) spectra were obtained on a FT-IR spectrometer (NICOLET-6700, Thermo Scientific, Waltham, USA). Raman spectra were recorded on a Lab RAM HR with a 785 nm wavelength laser beam (HORIBA Jobin Yvon S.A.S., Les Ulis, France). X-ray photoelectron spectroscopy (XPS) data were measured by XSAM800 (Kratos, Manchester, UK) with an Al source for determining the composition and chemical bonding configurations. Fluorescence spectra were measured with a F-7000 spectrometer (Hitachi, Tokyo, Japan), which was operated with photomultiplier tube (PMT) voltage at 700 V, slit widths for excitation and emission 2.5 nm and 5.0 nm, respectively.
## 5 Results and Discussion
### Performance Comparison between 1D CNN and 2D CNN  
In order to verify the applicability of these two models to this subject, 100 1-D CNNs and 100 2-D CNNs were trained with 100 epochs, respectively, using the 1-D and 2-D normalized spectral data set based on the \(iwp_{1}^{2}\) sub band. The data set was re-randomly divided by a ratio of 3:1:1 each time. The results of the repeated tests are shown in Figure 18 below.  
Figure 17: Performance comparison of time domain and frequency domain in terms of accuracy (**a**) and the loss value (**b**).  
Whether in terms of recognition accuracy or stability, the performance of 2-D CNN was significantly better than 1-D CNN. Although we could not find any specific shape features with the naked eye. The experimental results show that after training, 2-D CNN could learn effective features from 2D map and achieve better recognition accuracy than 1-D CNN. In a 1-D CNN, if the length of the feature vector in a certain convolution layer was \(m^{2}\), and the length of the filter was \(n^{2}\). Then, in the convolution operation, there were at most \(2n^{2}-2\) elements that could be associated with the element \(P_{x}\) in the feature vector. If the feature vector was folded into a feature map of \(m\,\times\,m\), and the size of the 2-D filter was set to \(n\,\times\,n\). In the 2-D convolution operation, the elements that could be associated with the element \(P_{x,y}\) were at most \(4n^{2}-4n\). Therefore, when the 2-D filter was the same as the 1-D filter in size, the 2-D convolution operation could make the elements in the feature map establish a wider relationship with each other. More importantly, because the 2-D filter spans multiple lines, the relation scope was broader. Therefore, in this paper, although the total parameters of the 1-D CNN were more than twice that of the 2-D CNN, the learning ability was still far less than the latter.
# Acquisition of Weak BDS Signal in the Lunar Environment
## 2 Materials and Methods
### Observation Scenarios
Assuming that the speed of the GNSS satellite is \(\mathbf{v}^{s}\) and the speed of the SSV user is \(\mathbf{v}_{u}\), the Doppler shift, \(f_{d}\), of the received signal can be expressed as:  
\[f_{d}=\frac{(\mathbf{v}_{u}-\mathbf{v}^{s})\cdot\mathbf{I}}{\lambda}, \tag{6}\]  
where \(\lambda\) is theavalength of GNSS signal and \(\mathbf{I}\) is the unit observation vector written in the form of  
\[\mathbf{I}=\frac{[\Delta x\quad\Delta y\quad\Delta z]}{\sqrt{\Delta x^{2}+\Delta y ^{2}+\Delta z^{2}}}, \tag{7}\]  
where \([\Delta x\quad\Delta y\quad\Delta z]\) is the observation vector and it can be calculated from the GNSS satellite position vector \([x^{s}\quad y^{s}\quad z^{s}]\) and the SSV user position vector \([x_{u}\quad y_{u}\quad z_{u}]\):  
\[[\Delta x\quad\Delta y\quad\Delta z]=[x^{s}-x_{u}\quad y^{s}-y_{u}\quad z^{s} -z_{u}]. \tag{8}\]  
When \(N\) (\(N\geq 4\)) GNSS satellites are visible, the geometry matrix \(\mathbf{G}\) is calculated as [32]:  
\[\mathbf{G}= \begin{bmatrix}\frac{\partial r^{(1)}}{\partial x}&\frac{\partial r^{ (1)}}{\partial y}&\frac{\partial r^{(1)}}{\partial x}&1\\ \frac{\partial r^{(2)}}{\partial x}&\frac{\partial r^{(2)}}{\partial y}&\frac {\partial r^{(2)}}{\partial z}&1\\ \vdots&\vdots&\vdots&\vdots\\ \frac{\partial r^{(N)}}{\partial x}&\frac{\partial r^{(N)}}{\partial y}&\frac {\partial r^{(N)}}{\partial z}&1\\ \end{bmatrix} , \tag{9}\]  
where \(r^{(N)}\) is the geometric distance between the SSV user and the \(N\)th GNSS satellite. Given the measurement matrix \(\mathbf{H}=(\mathbf{G}^{T}\mathbf{G})^{-1}\), the GDOP value is given by:  
\[GDOP=\sqrt{tr(\mathbf{H})}. \tag{10}\]
## 4 Discussion
### Voxel Size Determination  
Setting the appropriate size of each voxel was a crucial step to accurately estimate the four forest component proportions based on the VFSS algorithm. For the VFSS algorithm, the voxel size should be equal or smaller than two times the neighboring point distance, ensuring that there is only one point within each voxel. We investigated the variations of the four forest component proportions as the voxel size increasing from 0.1 m to 2.0 m in WPA and PC sites, and we found that the four forest component proportions had three distinct stages, such as the rapidly changing phase, relative stable phase, and slow-changing phase. For the WPA site, we used the plot WPA-P2 as an example. The proportion of sunlit overstory rapidly decreased from 35.5% to 21.3% as voxel sizes increased from 0.1 m to 0.4 m, were relatively stable when the voxel ranged from 0.4 m to 1.0 m, and then gradually increased up to 28.9% when the voxel size was beyond the 1.0 m. The relatively stable variations of the four forest component proportions were observed when the voxel sizes ranged from 0.4 m to 1.0 m in the WPA site.  
In terms of the PC site, the variations of the four forest component proportions held the relative stable variations when the voxel sizes varied from 0.6 m to 1.3 m. Taking the plot PC-P1 as an example, the sunlit forest background rapidly increased from 32.4% to 44.6% as voxel sizes changed from 0.1 m to 0.6 m, and then decreased when the voxel size was beyond the 1.3 m. By analyzing the effects of voxel size on the four forest component proportions in both WPA and PC sites, it is recommended that the voxel size should be equal or smaller than two times the neighboring point distance, ensuring that there is only one point within each voxel.
# A Survey of Vehicular VLC Methodologies
## 3 Vehicular VLC Overview
### General Context
Vehicular VLC seeks to achieve concurrent illumination and information data transfer by equipping vehicles with a light-emitting diode (LED) because of its resistance to RF interference, jamming, and spoofing, as well as the availability of its massive spectrum without the necessity for licensing and the line-of-sight (LoS) network connectivity it offers; it is also considered a safe and alternative technology when compared to RF communications. Sometimes, it is regarded as a it to support many applications, including platoon, safety, and critical applications, which require adherence to many demanding standards. Besides LoS links in VLC systems, line-of-sight links are usually unavailable in vehicular VLC applications due to restrictions from the ground, buildings, cars, and other objects' environments. To create the line-of-sight (LoS) links for VLC systems, some studies have suggested using intelligent reflective surface techniques (IRSS) and multi-hops between vehicles and users, in addition to digital twin technology (DT), which was recently used to provide effective improvement and to meet various requirements for emerging applications. It also helps in improving the phase, capacity, and frequency transformations of IRS elements installed in the infrastructure of roads, cars, bus stations, and buildings. Moreover, weather conditions like rain, fog, ice, and snow affect the strength of the LoS links, and these effects can also cause reflectivity [23; 69]. The typical flow of traffic that vehicular VLC can accommodate is seen in Figure 4, and vehicles can communicate with cars with several other automobiles close to them. As a result, they can establish tail-to-head and head-to-tail connections while they are moving, along with head-to-head linkages while meeting one another at intersections. There is also the possibility of a tail-to-tail link; however, this type of connection is not very prevalent in normal driving conditions. Figure 4 shows the I2V relationship, which includes infrastructure elements using LEDs, including roadside streetlights, traffic lights, and signs, where digital information is sent to and from vehicles via a VLC connection, and necessary and visible information is sent to drivers [23; 70; 71].
## 2 Theory and Method
### Numerical Algorithms  
The coupled flow model offers theoretical formulations for the 3D multi-step horizontal salt cavern leaching process. The adaptive meshing method addresses the moving boundary challenge within the coupled calculations, as shown in Figure 5. Initially, at time T\({}_{0}\), the geometric model undergoes adaptive meshing following the input of geological parameters, after which the mathematical model is initialized for simulation. A maximum mesh element is set, and the geometric model is meshed, followed by iterative calculations, with each step recording the mesh quality. At the subsequent moment \(T_{0}+\Delta T\), the distribution of the brine concentration, temperature field, and flow field are determined, facilitating cavern boundary expansion via the brine concentration and temperature field. At the next time \(T_{0}+\Delta T\), the brine concentration distribution, the temperature field, and the flow field can be obtained, and then cavern boundary expansion is achieved through the brine concentration distribution and the temperature field. The new geometric model is extracted after the cavern boundary expansion. At the time \(T_{0}+\Delta T^{\prime}\), the new geometric model is re-meshed adaptively based on the previously set maximum mesh element when the mesh quality is less than 0.2. If not, the calculation will continue.  
The numerical algorithms of the coupled model are shown in Figure 6. The segregated algorithm is used to solve the equation sequentially. The flow field is solved first using the Navier-Stokes \(\kappa\)-\(\varepsilon\) equations to obtain the velocity field, pressure distribution, turbulent kinetic energy, and turbulent dissipation. Then, the temperature field is calculated using the heat transfer equation. The brine concentration field can be obtained using the species transport equation. Next, the displacement of the boundary is calculated using the interface movement equation, and the new cavern boundary is obtained by updating geometry and re-meshing at the same time. After iterative calculation, the cavity shape of one stage can be obtained.  
Then, the brine concentration field is calculated using species transport equations based on the parameter of the flow field. Next, the displacement of the boundary is calculated based on brine concentration distribution, and the new cavern boundary is obtained by updating the mesh at the same time. After iterative calculation, the cavity shape of one stage can be obtained. After the calculation of one stage, the last cavern shape is exported, and the new initial and boundary conditions are defined and calculated iteratively until all the stages have ended. After multi-step cyclic iterative calculation, the cavern expansion, the brine concentration distribution, the temperature distribution, and the velocity distribution are finally obtained.  
Figure 5: The process of the adaptive meshing in the calculation.
# Investigation of the Performance of Basalt Fiber Reinforced Asphalt Mixture
## 3 Results
### Water Sensitivity  
Figure 7 shows the tensile strength ratio (TSR) of asphalt mixture containing various dosages of basalt fiber. When the dosage of basalt fiber is less than 0.4%, as the amount of basalt fiber increases, the value of TSR increases. When the basalt fiber dosage is 0.4%, the TSR reaches the maximum value. It indicated that the optimum dosage of basalt fiber for the water sensitivity of the asphalt mixture is 0.4%. In contrast with control sample, the value of TSR of asphalt mixture containing 0.4% basalt fiber is increased by 8.3%. The main reason for this phenomenon is that the surfaces of the basalt fiber and asphalt are alkaline and acidic, respectively. Additionally, the specific surface area of the fiber is relatively large. Basalt fiber absorbs the light components in asphalt and increases the thickness of asphalt film and improves the bonding strength between the asphalt and the aggregate, which is beneficial for preventing moisture from entering the interface between the asphalt and aggregate [11]. After the dosage of basalt fiber is more than 0.4%, the value of TSR is reduced greatly. The reason may be that excessive fiber dosage leads to uneven fiber dispersion and excessive porosity of the mixture, which reduces water stability of the asphalt mixture.  
Figure 5: The maximum bending strain of asphalt mixture containing various dosages of basalt fiber.  
Figure 6: SEM micrographs of basalt fiber-reinforced asphalt.
## 3 Proposed Methodology
### Estimation of Link Delay
#### 3.3.2 Computing Average Contention Delay per Channel  
Finally, by adapting (modifying and applying) the average backoff time for each link type from [61] in Equation (14), the average contention delay for CSMA-CA (\(\overline{t_{cont,WL}}\)) and for CSMA-CD (\(\overline{t_{cont,WI}}\)) can be formulated, as given in Equations (23) and (24), respectively:  
\[\overline{t_{cont,WL}} =t_{frame\;space}+\overline{BO_{WL}}+\overline{t_{collision,WL}} \tag{23}\] \[=DIFS+\frac{CW_{min}}{8}[\frac{2-2\overline{\rho_{WL}}-(2 \overline{\rho_{WL}})^{E}}{1-2\overline{\rho_{WL}}}]T_{slot}+0\]  
In Equation (23), \(CW_{min}\) is the minimum contention window, \(T_{slot}\) is the slot time, and \(E\) is the maximum number of retransmission attempts given by \(E=log_{2}(\frac{CW_{max}}{CW_{min}}+1)\), where \(CW_{max}\) is the maximum contention window [61].  
\[\overline{t_{cont,WI}} =t_{frame\;space}+\overline{BO_{WI}}+\overline{t_{collision,WI}} \tag{24}\] \[=\overline{E}\times IFG+\frac{\overline{\rho_{WI}}CW_{min}}{4}[ \frac{2-2\overline{\rho_{WI}}-(2\overline{\rho_{WI}})^{E}}{1-2\overline{ \rho_{WI}}}]T_{slot}+\overline{E}\times T_{c}\]  
In Equation (24), \(CW_{min}\) is the minimum contention window, \(T_{slot}\) is the slot time, \(E\) is the maximum number of retransmission attempts given by \(E=log_{2}(\frac{CW_{max}}{CW_{min}}+1)\), \(T_{c}\) is the collision duration given by \(T_{c}=\frac{frame\;size}{data\;rate}\), and \(\overline{E}\) is the average number of retransmission attempts given by \(\overline{E}=[\sum_{k=1}^{E-1}k(\overline{\rho_{WI}})^{k}(1-\overline{\rho_{ WI}})]+E(\overline{\rho_{WI}})^{E}\)[61].
## 4 Range-Based Algorithms
### Maximum Likelihood (ML)  
Maximum Likelihood is one of the most used approaches to localisation, resulting in a non-convex, non-linear optimisation problem. The object of ML is to maximise the likelihood function:  
\[\operatorname*{arg\,max}_{X}\quad p(X|z)\,. \tag{13}\]  
The LS minimisation of the observation errors normalised by measurement variances gives the ML solution.  
\[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\left( \frac{RSS_{ij}-\left(P_{0i}-10\alpha\text{log}_{10}(||X_{j}-S_{i}||)\right)}{ \sigma_{ij}}\right)^{2}, \tag{14}\] \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\left( \frac{TOA_{ij}c-||X_{j}-S_{i}||}{\sigma_{ij}}\right)^{2},\] (15) \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\left( \frac{TDOA_{ij}c-||X_{j}-S_{i}||+||X_{j}-S_{1}||}{\sigma_{ij}}\right)^{2},\] (16) \[\operatorname*{arg\,min}_{X}\sum_{j=1}^{N}\sum_{i=1}^{M}\left( \frac{AOA_{ij}-\arctan(\frac{X_{ii}-S_{ii}}{X_{ii}-S_{ii}})}{\sigma_{ij}} \right)^{2}. \tag{17}\]  
Solving it is not trivial and imposes a high computational cost. Subsequently, variant relaxations criteria are used to deal with the complexity, such as semi-definite programming (SDP) relaxation and second-order cone programming (SOCP) relaxation to approximate the complex problem, or numerical approaches such as Newton-Raphson are employed. In a joint-ML scheme, location along with channel parameters are estimated [37; 38]. Ref. [38] also explores the combination of multi-lateration with ML to compromise between performance and complexity where multi-lateration is exploited for location estimation, and ML is utilised for channel parameters estimation. The paper then compares it with the joint ML method. The effect of the fault and various noises are taken into account in [39; 40]. Ref. [39] addresses byzantine fault and NLOS effect. Byzantine fault is considered by including non-Gaussian interference noise corrupting the transmission data, and the NLOS effect is modelled by adding a bias term to the propagation model. First-order Taylor series expansion is utilised to simplify the model so that some transformation turns it into a generalised trust-region sub-problem (GTRS). Additive and multiplicative noises are handled in [40] using ML, taking advantage of iterative expectation-maximisation.