esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Phenological Metric Detection  
Using the curve-fitting method of TIMESAT with the same the seasonal amplitude thresold (30%), different dynamic ranges of VIs can cause a shift of phenology detection results, as we can see in Figure 8. Despite the uncertainty of VI time series, the application of TIMESAT achieved consistent results from the three VIs for most vegetation types. Agreeing with past studies [12, 30], the three VIs in this study proved their effectiveness for phenological metric detection in dryland enviroment.  
However, there are still some discrepencies in phenology detection results. The uncertainty of EOS detected from MODIS VIs in our study is higher than SOS (Table 4), which is in agreement with previous studies [47, 54]. The largest deviation of phenological metrics and the overestimation of EOS are observed from NDVI time series. Due to its sensitivity to soil background and atmospheric conditions, it exhibited high levels of variation. The disagreement among histogram distributions of phenological metrics from the three VIs can be caused by their different sensitivities to the variation of soil background. Similarly,Walker _et al._[16] revealed the spatial variability of land surface phenology extracted from NDVI is higher than EVI in dryland areas in Arizona, USA. The discrepancy of the peak greenness from EVI and NDVI has been attributed to their different sensitivities to the physiological characteristics of vegetation types [16].  
In order to better assess the suitablity of VIs for phenological detection in dryland enviroment, more detailed information is needed. Verification of satellite-extracted phenology metrics with CO\({}_{2}\) measurements of flux tower footprint or _in situ_ VI measurements will be helpful to our understanding of ecosystem processes over arid lands [55, 56]. The inaccessibility of _in situ_ data precludes further analysis in our study. In addition, we only compared three commonly used VIs in our analysis. The effectiveness of other vegetation indices such as EVI2, MSAVI and OSAVI and combined usage of these indices for vegetation dynamic investigation in arid and semi-arid dryland can be addressed in further studies.
## 3 Results and Discussion
### Influence of seawater Mixing on Stabilized Soft Soil  
According to the previous investigation, the salinity of the seawater around the source of the soft soil used was about 30%. To simulate the requirements of actual coastal construction, the influence of SWC after seawater mixing on the compressive strength of soft soil was evaluated. Cement and SWC with salinities of 0% (pure water), 30% (salinity of seawater), and 60% (twice the salinity of seawater) were used to solidify soft soil, and the curing ages were 3 d, 7 d, and 28 d.  
As shown in Figure 5, with the increase in seawater salinity, the compressive strength of cement soil and stabilized soil also increased, and the compressive strength of stabilized soil was much higher than that of cement soil. In the early curing stage, when the salinity increased from 0% to 30%, the 7 d compressive strength of cement soil, stabilized soil G4, and stabilized soil G7 increased from 1.68 MPa, 4.92 MPa, and 4.61 MPa to 1.90 MPa, 6.16 MPa, and 5.70 MPa, respectively, increasing by 0.22 MPa, 1.24 MPa, and 1.09 MPa. In the late curing period, the 28 d compressive strength of cement soil, stabilized soil G4, and stabilized soil G7 increased from 2.64 MPa, 5.91 MPa, and 5.97 MPa to 3.26 MPa, 7.03 MPa, and 7.52 MPa, respectively, increasing by 0.62 MPa, 1.12 MPa, and 1.55 MPa.  
Figure 5: Compressive strength of GC (**a**), G4 (**b**), and G7 (**c**) mixed with seawater of different salinities.  
Whether in the early stage or the late stage, after seawater mixing, the compressive strength of the samples of cement soil and stabilized soil increased rather than decreased, and the compressive strength of the samples of stabilized soil increased more than that of cement soil. This indicates that the preferred SWC formulations have better adaptability to seawater.
## 4 Results and Discussion
### Solution Ranking and Selection Using TOPSIS  
The TOPSIS method ranked Pareto-optimal solutions based on actual process constraints and performance metrics (Table 4). This involved normalizing the solutions (S15), identifying the ideal and negative-ideal solutions (S16), computing separation measures (S17), and finally ranking the solutions based on their relative closeness to the ideal solution (S18).  
Figure 10: Pareto front solutions showing the trade-off between Operational Efficiency (f\({}_{1}\)) and Environmental Impact (f\({}_{2}\)), where lower emissions indicate better sustainability. The red point denotes the baseline scenario, while blue points represent optimized solutionsRanking Insights  
The TOPSIS ranking reveals that solution P9 (92.3% efficiency, 1758.2 kg CO\({}_{2}\)/day) achieves the optimal balance between operational efficiency and environmental impact. This solution represents a 5.1% efficiency improvement and a 12.4% emission reduction compared to the baseline. The top-ranked solutions (P8-P11) cluster around 91-93% efficiency with emissions between 1731 and 1776 kg CO\({}_{2}\)/day, indicating a sweet spot where significant improvements in both objectives are achievable. Solutions beyond P14 show diminishing returns, where marginal efficiency gains are offset by an increased environmental impact.
## 2 Related Work
### Embedding Example  
The following example shows the method of TLE. Assuming that a gray-scale image \(I\) has 3 \(\times\) 3 pixels, its pixel value is shown in Figure 2. First, set \(n=3\) is used to divide the cover image \(I\) into 2 planes, namely \(I_{HiSB}\) image and \(  
Assume this example uses predictor method 1, that is, \((p_{1},p_{2})=(x_{\pi(1)}\), \(x_{\pi(4)})=(19,\,22)\). Additionally, suppose \(s_{1}=1\) and \(s_{2}=1\), then the prediction error \(e_{1}=x_{c}-p_{1}=20-19=1\) is calculated according to Equation (2); therefore, \(x_{c}^{(1)}=x_{c}+s_{1}=20+1=21\), and this completes the secret data embedding of the first layer. Then, the prediction error \(e_{2}=x_{c}^{(1)}-p_{2}=21-22=-1\) is calculated according to Equation (3) as well as \(x_{c}^{(2)}=x_{c}^{(1)}-s_{2}=21-1=20\), and this completes the second layer embedding. The embedded \(x_{c}^{(1)}\) is combined with the pixel value of \(I_{LoSB}\) to complete the embedding process, as shown in Figure 4.  
The TLE method has the possibility of keeping the pixel value unchanged after embedding. It not only reduces the distortion of the image but also increases the embedding capacity and then uses the secret data algorithm to extract the secret message to achieve the purpose of information hiding.
# The Limitations of 5f Delocalization and Dispersion
## 5 X-ray Absorption Spectroscopy: Angular Momentum Coupling and Perturbations
### Crystal Field Modifications of Spherically Symmetric Systems
The spreads of pre-peak structure over the range of 10 eV is caused by the differences in coulombic repulsion associated with different total angular momentum states. It is this type of energy that needs to be overcome by E\({}_{\rm cf}\). Thus, an approximation to the actual situation may correspond to something like that shown in the upper right panel, where the states from \(\Delta\)E\({}_{\rm so}\) = 10 eV have been shifted to align with the initial values of \(\Delta\)E\({}_{\rm so}\) = 1 eV. This gives us the case of a first order perturbation with linear variations of the energies with E\({}_{\rm cf}\) and very little mixing, but centered upon a pure spin-orbit splitting case of \(\Delta\)E\({}_{\rm so}\) = 1 eV. One quirk of this analysis is that it appears that the combination of jj coupling and a first order perturbation from the crystal field gives rise to a mixing of the 5f\({}_{7/2}\) and 5f  
Figure 9: Plots of the Eigenstate energies for various values of \(\Delta\)E\({}_{\rm so}\) and E\({}_{\rm cf}\) are shown here. The states originating at E\({}_{\rm cf}\) = 0 from the 5f\({}_{7/2}\) (5f\({}_{5/2}\)) manifold are shown in blue (red). Each line is doubly degenerate. In black, the lines (Lij) corresponding to 1 m | E\({}_{\rm cf}\) are also shown. At E\({}_{\rm cf}\) > 0, there is mixing of the pure 5f\({}_{7/2}\) and pure 5f\({}_{5/2}\) states. See text for details. states much like the results of the intermediate coupling model. Further work is required to quantify this hypothesis. Our simple model is a single electron picture and real systems, such as uranium dioxide and metallic Pu, are multi-electronic and involve complicated angular momentum coupling. Nevertheless, the same trends should follow in multi-electronic systems. Russel-Saunders or LS coupling involves summing the orbital angular momenta first, followed by coupling to the sum of the spins. Generally, the spin-orbit interactions are smaller, tending towards zero in the limiting case. The sum of the orbital angular momenta should usually be dominant and the solution states should retain the fundamental property of being labile to separation into components consistent with the crystal field, with less extensive mixing required. On the other hand, jj coupling involves summing the spin and orbital components first, then summing the individual electron sums into the total angular momentum. The spin-orbit interaction tends to be larger. The crystal field will need to overcome the spin-orbit splitting interaction, enforced by the coulombic repulsion, whether for a single electron or multiple electrons in order to drive extensive mixing of 5f\({}_{5/2}\) and 5f\({}_{3/2}\) states. Thus, the mixing of the different j states will be strongly hindered and limited. The upshot of this is that Hund's Rules [31], which are so powerful in the Russell-Saunders limit, become more like guidelines in jj coupling.
## 3 Results and Analysis
### Comparison with a Precise Ephemeris
The red line shows the results of Group 1, while the blue line shows the results of Group 2 in (**a–d**). The white blocks and black blocks in (**e**) represent the RMS of Group 1 and Group 2 on each day, respectively. Figure 9: Comparison between the kinematic orbits of GRACE-C and the precise orbits provided by the GFZ. (**a**) 3D-RMS of the comparison results; (**b**) Difference in Radial direction; (**c**) Difference in Tangent direction; (**d**) Difference in Normal direction; (**e**) Average 3D-RMS statistics of the comparison results. Where the red line shows the results of Group 1 while the blue line shows the results of Group 2 in (**a–d**). The white blocks and black blocks in (**e**) represent the RMS of Group 1 and Group 2 on each day, respectively. Figure 10: Comparison between the kinematic orbits of GRACE-D and the precise orbits provided by the GFZ. (**a**) 3D-RMS of the comparison results; (**b**) Difference in Radial direction; (**c**) Difference in Tangent direction; (**d**) Difference in Normal direction; (**e**) Average 3D-RMS statistics of the comparison results. The red line shows the results of Group 1, while the blue line shows the results of Group 2 in (**a–d**). The white blocks and black blocks in (**e**) represent the RMS of Group 1 and Group 2 on each day, respectively. According to the comparison of Group 1 and Group 2, different levels of jumping phenomena occurred in the kinematic orbit for both Swarm and GRACE-FO satellites, some of which reached the decimeter-level, and some even reached a meter-level. However, the results of Group 1 show that the quality control method can effectively suppress the orbital jumps. The Swarm-C satellites (on DOY 162 in 2021) and GRACE-D satellites (e.g., on DOY 153, 157, 164, and 165 in 2021) with more frequent orbital jumps are taken as an example. According to Figures 8e and 10e, after we took quality control measures, the total 3-D RMS of the kinematic orbit for Swarm-C on DOY 162 was decreased from 5.5 cm to 4.4 cm with about 20%. The 3D-RMSs of kinematic orbits for GRACE-D on DOY 153, 157, 164, and 165 were decreased from 5.5 cm, 4.0 cm, 3.8 cm, and 4.5 cm to 4.6 cm, 3.4 cm, 3.3 cm, and 3.4 cm, respectively with about 22 %. This shows that the quality control for kinematic orbits adopted in this paper has a significant effect. Meanwhile, from Table 5, after quality control for the kinematic orbit, the 3D-RMS of the kinematic orbit is reduced from 3.57 cm to 3.53 cm for Swarm-A, from 4.11 cm to 4.03 cm for Swarm-B, and from 3.61 cm to 3.52 cm for Swarm-C. Therefore, the quality control method adopted in this paper improved the kinematic orbit accuracy by reducing the frequency and degree of orbital jumps, which can be seen in Figures 6-10.
###### Abstract  
Recent developments in both optical wireless communication (OWC) systems and implanted medical devices (IMDs) have introduced transdermal optical wireless (TOW) technology as a viable candidate for extremely high-speed in-body to out-of-body wireless data transmissions, which are growing in demand for many vital biomedical applications, including telemetry with medical implants, health monitoring, neural recording and prostheses. Nevertheless, this emerging communication modality is primarily hindered by skin-induced attenuation of the propagating signal bit carrier along with its stochastic misalignment-induced fading. Thus, by considering a typical modulated retroreflective (MRR) TOW system with spatial diversity and optimal combining (OC) for signal reception in this work, we focus, for the first time in the MRR TOW literature, on the stochastic nature of generalized pointing errors with non-zero boresight (NZB). Specifically, under these circumstances, novel analytical mathematical expressions were derived for the total average bit error rate (BER) of various system configurations. Their results revealed significant outage performance enhancements when spatial diversity was utilized. Moreover, taking into consideration the total transdermal pathloss along with the effects of stochastic NZB pointing errors, the critical average signal-to-noise ratio (SNR) metric was evaluated for typical power spectral-density values.  
optical wireless communications; implanted medical devices; transdermal optical wireless communication; modulated retroreflective link; pointing errors with nonzero boresight; transdermal pathloss; diversity; average bit error rate; optimal combining Article
## 2 Materials and Methods
### Multi-Mode-Transceiver  
In cooperation with a German avionics manufacturer [26] an EASA/FAA certified Mode A/C/S transponder including ADS-B In/Out capability was extended by a miniaturised embedded system [27] and a mobile radio interface [28]. The board serves as a control center between the individual components of the MMT and also provides the computing power required for operation as a mission computer. Depending on the current flight phase, individual communication channels can be switched on and off in order to meet the requirements of the respective airspace. For the internal communication between the components, serial interfaces (RS232) were used. Additional external interfaces (USB, Ethernet) were added for external communication, e.g., with the Flight Management System (FMS) of the UAV. To support variable input voltage, the integrated power supply was modified, enabling the MMT to be operated with a standard 4S lithium polymer accumulator (14.8 V to 16.8 V). Figure 1 gives an overview of the MMT components and their interconnections.
## 3 Methodology
### Prediction Using GP
#### 3.2.2 Bpnn  
BPNN is a widely used AI technique that was developed to mimic the human brain. In this, there is an input layer that takes impulses from the outside environment as inputs to the network. These inputs \(x_{k}\) are weighted by connecting weights \(w_{k}\) and relayed to the hidden layer. The hidden layer contains processing units called neurons which transform the weighted input by a transfer function, \(t\). It is noteworthy that biases \(b\) are added to the transfer function before the transformation process. The hidden layer's output is subsequently conveyed to the output layer, which is transformed by a transfer function operating inside the hidden layer. The network's predicted values are then derived from the output, \(\hat{y}\) from the output layer as shown in Equation (12).  
\[\hat{y}=t\left(\sum_{k=1}^{m}w_{k}x_{k}+b\right) \tag{12}\]  
In training the BPNN, a training algorithm is used in updating weights and biases based on the backpropagation error, \(e\) (divergence in true and predicted value) as shown in Equation (13) so as to produce a network with a minimum propagation error.  
\[e=y-\hat{y} \tag{13}\]  
Several training algorithms have been developed for such purposes. However, the Levenberg-Marquardt algorithm [80] is the widely used training function due to its high convergence speed and accuracy and thus was used in this study.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 3 Bistatic Scattering RFI Impact on GEO SAR
### Doppler Filtering
\begin{split} E=A^{2}\int\limits_{-\frac{T_{d}}{2}}^{\frac{T_{d} }{2}}\bigg{|}\frac{T_{d}}{\int\limits_{-\frac{T_{d}}{2}}^{\frac{T_{d}}{2}}} \sin\mathrm{c}^{2}\bigg{(}\frac{v_{g}L_{g}}{\lambda R_{0}}u\bigg{)}\exp\bigg{[} j\pi\bigg{(}\frac{4R_{0,I}}{\lambda}+f_{dc,I}u+f_{dr,I}u^{2}\bigg{)}\bigg{]} \cdot rect\Big{(}\frac{t-u}{t_{s}}\bigg{)}\exp\Big{[}-j\pi f_{dr,g}(t-u)^{2} \bigg{]}du\bigg{|}^{2}dt.\end{split}  \tag{19}\]  
Expanding the exponent terms and combining them, ignoring the impact of the antenna pattern for simplicity, we have  
\[ \begin{split} E\approx\frac{A^{2}}{\Big{|}f_{dr,I}-f_{dr,g} \Big{|}}\int\limits_{-\frac{T_{d}}{2}}^{\frac{T_{d}}{2}}\bigg{|}\exp\Bigg{[}j \frac{\pi}{\Big{(}f_{dr,I}-f_{dr,g}\Big{)}}\bigg{(}\frac{f_{dc,I}-f_{dc,g}}{2} +f_{dr,g}t^{2}\bigg{)}^{2}\Bigg{]}\Bigg{|}^{2}dt\approx\frac{A^{2}T_{a}}{\Big{|} f_{dr,I}-f_{dr,g}\Big{|}}.\end{split}  \tag{20}\]  
In the meantime, the energy of the effective GEO SAR signal \(E_{m}\) after Doppler filtering is expressed as  
\[ \begin{split} E_{m}=\frac{\frac{1}{f_{dc,I}a}}{\int\limits_{f_ {dc,I}a}^{\frac{1}{2}}}\bigg{|}\int\limits_{-\frac{T_{d}}{2}}^{\frac{T_{d}}{2}} \sin\mathrm{c}^{4}\bigg{(}\frac{v_{g}L_{g}}{\lambda R_{0}}u\bigg{)}\exp\Big{[} j\pi\bigg{(}\frac{4}{\lambda}R_{0}+f_{dc,g}u+f_{dr,g}u^{2}\bigg{)}\bigg{]} \cdot rect\Big{(}\frac{t-u}{t_{s}}\bigg{)}\exp\Big{[}-j\pi f_{dr,g}(t-u)^{2} \Big{]}du\bigg{|}^{2}dt.\end{split}  \tag{21}\]  
Simplifying the equation, we have  
\[ \begin{split} E_{m}\approx\frac{0.9T_{a}}{\Big{|}f_{dr,g}\Big{|} }.\end{split}  \tag{22}\]  
The ratio between the energy of the GEO SAR signal and the energy of the RFI after Doppler filtering is shown as  
\[ \begin{split}\Re=\frac{E_{m}}{E}\approx\frac{0.9}{A^{2}}\frac{ \Big{|}f_{dr,I}-f_{dr,g}\Big{|}}{\Big{|}f_{dr,g}\Big{|}}\cdot\frac{PRF_{g}}{f_{ dr,I}T_{a}},\end{split}  \tag{23}\]  
where \(PRF_{g}\) is the pulse repetition frequency of GEO SAR. If GEO SAR uses a small PRF, the Doppler spectrum of the RFI will be aliased, leading to a small value of \(\Re\), that, in turn results in a small SINR. The difference of the Doppler rates of GEO SAR and LEO SAR is the parameter to describe the attenuation of LEO SAR RFI signal after Doppler filtering. A higher difference between the Doppler rates of GEO SAR and LEO SAR will be helpful to raise \(\Re\). A higher \(\Re\) indicates a better imaging performance of GEO SAR in the presence of LEO SAR RFI. In the case of the distributed target, the LEO SAR illuminates it with a PRF that causes the backscattered returns to be independent, white. The LEO-GEO bistatic scattering will then be nearly white and performance of the Doppler filtering will lose.
## 5 Conclusions  
The novel Mobility Management-Based Autonomous Energy-Aware Framework using ML and RL techniques is proposed to address multiple challenges including peak and off-peak time passenger ridership and future location estimations supported by mobility prediction accuracies and energy consumption of the HetNet, analysing the overall impact of HetNet CO2 emissions in a two-tier model by using cell On-Off switching and offloading scheme. In the first part of our framework, we show that the ML-based comprehensively discussed algorithms and optimal mapping of classification prediction accuracy can achieve 98.82% with KNN classifier. Comparative study of peak and off-peak time passenger ridership and future location estimations indicate adequate robustness. In the second part of our framework, we use RL-based QL algorithm to establish an optimal way of underutilised cell On-Off switching operation and SCs that emit unnecessary CO2 emissions. Based on our Energy-Aware Framework, energy savings gain coupled with carbon emissions of up to 31.83% are achieved. For future works, we would endeavour to implement further optimisation schemes for a higher number of scenarios by employing user-specific behaviours. Another promising area of the research would incorporate clustering of daily ridership in multiple bounds.  
Figure 8: Total CO2 emissions of the HetNet for various number of SCs.  
Figure 7: Results for total ECR of the HetNet and the Gain provided by different switching approaches where, (**a**) Total energy consumption, (**b**) Gain.  
**Author Contributions:** Conceptualisation, S.M.A., S.A. and M.O.; Data curation, S.M.A. and M.O.; Formal analysis, S.M.A., S.A. and S.H.; Funding acquisition, R.N.B.R. and M.A.I.; Methodology, S.M.A.; Project administration, S.H., S.A., Q.H.A. and M.A.I.; Software, S.M.A., K.D. and M.O.; Supervision, S.H., Q.H.A. and M.A.I.; Validation, Q.H.A. and M.A.I.; Visualisation, S.M.A., S.A. and Q.H.A.; Writing--original draft, S.M.A; and Writing--review and editing, M.O. and S.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was partially funded by Deanship of Graduate Studies and Research (DGSR), Ajman University under the grant number 2020-IRG-ENIT-10 and 2019-IRG-ENIT-8.  
**Conflicts of Interest:** The authors declare no conflict of interest.