esa-sceva/satcom-chunk-collection huggingface dataset:
# Special Issue on Light Communications: Latest Advances and Prospects
## 3 Indoor Systems
The APMAC protocol involves association, data transmission, and dissociation phases. While assigning bandwidth to the visible light nodes (VLNs), the visible light access point establishes a polling table that contains the identity, buffer size, and round-trip time of each VLN that issued a bandwidth request. The protocol is investigated numerically in terms of average waiting time, packet collision, system utilization, and fairness. Finally, [PERSON], et al. [21] have proposed a channel and bit adaptive power control strategy for uplink non-orthogonal multiple access VLC systems by jointly considering the channel state information and the transmission bit rate. Under this adaptive power control strategy, it is proved that the received signal at the photodiode receiver constitutes a sizable pulse amplitude modulation constellation and low-complexity maximum likelihood detection is admitted.
## 5 Conclusions  
This paper presents a novel approach that combines particle filters and reinforcement learning to optimize handover mechanisms in NTN. By leveraging particle filters for precise estimation of the distance between the UE and NTN satellites and employing reinforcement learning to dynamically optimize the hysteresis, _TTT_, and handover decisions in LBH mechanisms, the proposed approach achieves significant improvements in handover efficiency. Simulation results demonstrate that, compared to LBH and RBH approaches, our approach outperforms in terms of the average number of handovers, average ping-pong rate, and handover failure rate. These findings highlight the effectiveness of our approach in enhancing NTN handover stability and performance, offering a reliable and efficient solution for mobility management in NTN environments. In future work, the proposed LBH-PRL approach can be further evaluated in more diverse and complex NTN scenarios to test its robustness and adaptability. Potential application environments include dense LEO constellations, high-mobility conditions such as high-speed trains and aircraft, and maritime and polar regions where traditional terrestrial connectivity is limited. These environments often involve more frequent handovers, variable link quality, or signal obstruction challenges, requiring enhanced adaptability of the handover algorithm. Evaluating the LBH-PRL approach in such conditions would provide deeper insights into its practical deployment value across the full range of NTN use cases.  
Conceptualization, L.-S.C., S.-H.L. and H.-H.C.; Methodology, L.-S.C., S.-H.L. and H.-H.C.; Formal analysis, L.-S.C.; Investigation, L.-S.C., S.-H.L. and H.-H.C.; Writing--original draft, L.-S.C. and S.-H.L.; Writing--review & editing, H.-H.C.; Supervision, L.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are contained within the article.  
This work was supported by the National Science and Technology Council of Taiwan (R.O.C.), under Contract NSTC 113-2222-E-197-001.  
The authors declare no conflicts of interest.
## 5 Computational Experiments
### Comparison with Existing Distributed Algorithms
For small-scale instances, algorithm differences are relatively small due to the limited solution space. However, as the instance scale increases, variations between algorithms become more pronounced. For example, in instances with {_E_, 100}, the objective value \(F(\mathbf{\theta})\) of PITO is decreased by 22.63% (\(\approx\)(\(1.2925-1)/1.2925\)) and 11.49% (\(\approx\)(\(1.1298-1)/1.1298\)) compared to CNP and DETS, respectively. This is because the CNP and DETS are more easily trapped into local optima [33] due to reliance on direct neighbor information (as discussed in Section 4.1), which consequently impacts offloading assignment quality. In contrast, PITO adopts a consensus process, where satellites share comprehensive information vectors with their neighbors, enabling PITO to escape from local optimum. Algorithm ADMM sidesteps these local optimum issues by centrally updating global variables [27]. However, it still faces challenges for discrete-continuous transformation and binary variables recovery [27], which affect assignment quality, especially for large-scale instances. Figure 6b shows the \(aCT\) of all algorithms. PITO achieves the smallest values in all instances, indicating its low communication burden. This is because satellites in PITO only communicate with neighbors during the consensus process, and the adopted vectors further reduce the required communication times. The CNP and DETS rank second and third, respectively. The diffusion mechanism in DETS necessitates more communication times, compared to CNP. The ADMM performs the worst because the global variables update in each iteration, requiring local variables from all satellites. This results in a significant communication burden, which also increases with the number of satellites and tasks. Figure 6c illustrates the \(aRT\) of all algorithms. CNP has the shortest running time due to its simplicity. Notably, CNP, DETS, and PITO show a slight increase in running time as the scale grows, while ADMM exhibits a significant ascending trend. This is attributed to ADMM relying on the iteration of convex optimization algorithms or CVX tools [27], which are time-consuming, especially for large-scale instances. PITO has a certain increase of \(aRT\) in large-scale instances, due to the higher number of satellites and tasks requiring more iterations to reach consensus, and this issue can be addressed by dividing tasks into groups or clustering satellites. Based on Figure 6, PITO consistently achieves the best assignments with minimal communication burden, although taking slightly more time than DETS and CNP. Generally, the slight increase in runtime for better assignments is worthwhile. Consequently, we conclude that PITO outperforms existing methods in solving task offloading problems in SEC.
# Compensating Uncertainties in Force Sensing for Robotic-Assisted Palpation
## 2 Robotic palpation with Force Based Approach
### Stiffness Analysis Through Young's Modulus  
The mechanical property of soft tissue has drawn considerable research interests, and several soft tissue models have been developed. The Finite Element Method (FEM) is a popular tool to describe soft tissue deformation, in which the tissue is divided into a serial of discrete elements, and these elements form a mesh to describe the tissue deformation [34]. The FEM provides a reasonably accurate depiction of the soft tissue deformation, but it is difficult to implement such a model in real-time due to the high computation cost. Moreover, the boundary conditions of the FEM model is very difficult to be defined in vivo, thus limiting its clinical application [35].  
In order to quantitatively describe the mechanical property of soft tissue, the Young's or elastic modulus is used to evaluate the perceived stiffness of soft tissue in robotic-assisted palpation [22]. Since the spherical indentation tip is used to interact with the tissue to obtain the contact force, Young's modulus (YM) of the soft tissue can be derived as [36]:  
\[E=\frac{3f(1+v)}{8d_{in}\sqrt{rd_{in}}} \tag{1}\]  
in which \(E\) is YM, \(v\) is the Poisson's ratio of the soft tissue phantom, \(f\) is the measured interaction force from the sensor, \(r\) is the radius of the indenter, and \(d_{in}\) is the indentation depth. The YM is used to determine the mechanical property of the tissue with or without hard inclusions. In YM, the main factors used are the interaction force \(f\) and the indentation depth \(d_{in}\). The interaction force can be measured directly through the force sensor, but the indentation depth should be verified by the robotic Cartesian vectors or with external devices.  
Figure 1: Schematic of robotic palpation with hard inclusions embedded in a silicone soft tissue phantom. A 3D printed probe is attached to the force sensor, and the force sensor is attached to the robot end effector. The probe can be controlled to contact the soft tissue phantom, in which three hard inclusions (in blue) are considered to be tumors (stiffer than the phantom). The red line denotes the distance between the probe and the surface of the tissue phantom.
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
## 3 Results and Discussion
### U(VI) Adsorption on the GO-MIL-101(Fe)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**T (k)**} & \multicolumn{3}{c}{**[PERSON]**} & \multicolumn{3}{c}{**[PERSON]**} \\ \cline{2-7}  & **\(q_{max}\) (mg g\({}^{-1}\))** & **\(b\) (L mg\({}^{-1}\))** & **\(R^{2}\)** & **\(K_{F}\) (mol\({}^{1-}\) L\({}^{n}\) g\({}^{-1}\))** & **\(n\)** & **\(R^{2}\)** \\ \hline MIL-101(Fe) & 68.93 & 0.053 & 0.991 & 7.66 & 2.03 & 0.969 \\ GO15-MIL-101(Fe) & 106.89 & 0.132 & 0.989 & 24.96 & 2.824 & 0.973 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The simulated adsorption isotherm parameters of U(VI) on MIL-101(Fe) and GO15-MIL-101(Fe) composites at 298 K. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{3}{c}{**\(\Delta G^{0}\) (kJ mol\({}^{-1}\))**} & \multirow{2}{*}{**\(\Delta G^{0}\) (kJ mol\({}^{-1}\))**} & \multirow{2}{*}{**\(\Delta S^{0}\) (J mol\({}^{-1}\) K\({}^{-1}\))**} \\ \cline{2-7}  & **298 K** & **313 K** & **328 K** & & & \\ \hline GO15-MIL-101(Fe) & \(-\)23.16 & \(-\)24.71 & \(-\)26.29 & 7.96 & 104.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The simulated adsorption isotherm parameters of U(VI) on GO15-MIL-101(Fe) composite. Therefore, nitric acid was used to desorb the uranyl ions in the regeneration experiment. As shown in Figure 6, GO15-MIL-101(Fe) showed the negligible reduction of adsorption efficiency in the first two reabsorption process. The adsorption decay became obvious after 4 cycles but gradually became steady in further tests. The performance decrease may arise from the incomplete desorption of uranyl ions and the partial aggregation of the composite sample (Figure S7). Because of the growth of MIL-101(Fe) on the surface, the aggregation did not deteriorate further, giving rise to the gradually steady reusing performance. After 8 cycles the samples still maintained \(\sim\)75% of the original adsorption efficiency. The results implied that GO15-MIL-101(Fe) was a promising composite adsorbent with a good reusability.
# Analysis of a Collision-Energy-Based Method for the Prediction of Ice Loading on Ships
## 4 Results
### Ice Crushing Strength and Pressure  
The considered full-scale ice load measurements (data set A-B) do not include any data to which we can compare our calculated values of ice crushing strength \(\sigma_{cr}\). Thus, for the validation of our calculated \(\sigma_{cr}\) values, we apply full-scale ice crushing pressure measurements conducted on MV Arctic and IB Kigoriak in Canadian waters, and on IB Sampo in the Baltic Sea [8]. We compare the calculated data points of ice crushing pressure (see Equation (10)) with the full-scale measurements as shown in Figures 9 and 10.  
Accordingly, we find that for different collision scenario assumptions, calculated ice crushing pressures \(p\) values decrease with the size of the contact area. For both ship-ice field and ship-ice floe impacts, the best correlation with the measurements from the Baltic Sea (IB Sampo) and the Canadian waters (MV Arctic and IB Kigoriak) are obtained by applying angular- and round-contact geometries, respectively. We also find that the calculated pressure points tend to have a higher spread when calculated for an assumed ship-ice floe impact (Figure 10) than when calculated for an assumed ship-ice field impact (Figure 9). This is explained by the fact that in the case of ship-ice floe impacts, the ice load estimate depends on the ice floe size, which varies significantly as shown in Figures 3 and 4.  
We calculated the pressure-area points presented in Figures 9 and 10 based on data set B. A similar analysis for data set A resulted in similar findings. It should be pointed out that [PERSON], et al. [9] do not specify any value of ice crushing pressure. The pressure-area relationship in accordance with PC5 is plotted in Figures 9d and 10d. For comparison, in Figures 9d and 10d we also plot the pressure-area relationship as determined in accordance with PC5. Because the PC rules only consider angular contact geometry, we do not plot any PC definition of ice crushing pressure for other contact geometries. As per Figures 9d and 10d, we find that the slope of the PC5 pressure-area relationship is less steep than that of the pressure-area relationships determined based on full-scale measurements. Specifically, as indicated by the figures, the ice crushing pressures assumed by PC5 appear to be conservative for larger contact areas above approximately 5 \(m^{2}\).  
Figure 9: Measured vs. calculated ice crushing pressure-area relationships. Collision type: Ship–ice field impact. Contact geometries: (**a**) Right-apex oblique; (**b**) Round; (**c**) Symmetric v wedge; (**d**) Angular. The ice crushing pressure-area relationships determined based on measurements conducted on MV Arctic and IB Kigoriak, and on IB Sampo, were determined based on Kujala [8].
## 5 Calculation Method for Torsional Fatigue Life of Titanium Alloy Turning Surface
### Comparison and Analysis of Experimental Methods and Measurement Results
As can be seen from Figure 31, after turning processing, the element content of \(\alpha\)-phase elements C and N is significantly increased, and the element content of Al decreased by  
Figure 31: Content of different surface elements. Figure 30: SEM scan results and distribution of surface scanning elements for Scheme 1 and Scheme 2: **(a)** The results of SEM and EDS surface scanning are shown in Scheme 1, **(b)** SEM and EDS surface scan measurement results of Scheme 2. The \(\alpha\)-phase element ratios for \(a_{1}\) to \(a_{5}\) are 17.06%, 16.54%, 17.20%, 18.5%, and 15.09%, respectively, while the \(\beta\)-phase element ratios are 4.77%, 4.89%, 3.74%, 3.58%, and 5.04%. The \(\alpha\)-phase element ratios for both \(a_{3}\) and \(a_{4}\) are higher than those of the other points, and the \(\beta\)-phase element ratios are lower than those of the other points, indicating that the brittleness increases between these two points, reducing ductility and toughness, leading to fatigue fracture first. The element content of the machining surface at different positions under the same process parameters shows obvious inhomogeneity. The uneven distribution of surface element content in this titanium alloy machining further affects the difference in its mechanical properties. Scanning electron microscopy (SEM) analysis of the fracture surface reveals fine crack initiation sites and microstructural defects, including non-uniform particle distribution and micropores. These imperfections serve as stress concentration points. Pre-existing defects and localized stress concentrations predispose the material to accelerated crack propagation and fracture under subsequent torsional fatigue loading. This fracture mechanism primarily arises from cumulative micro-defect aggregation and localized stress intensification during cyclic loading. In Scheme 2, point scanning spectroscopy was used to detect different characteristic points on the surface of turning. The test results are shown in Figure 34.  
The \(\alpha\)-phase element content of \(a_{1}\)-\(a_{5}\) was 13.27%, 14.64%, 16.54%, 12.56%, and 12.06%, respectively, and the \(\beta\)-phase element content was 8.12%, 5.11%, 4.49%, 7.46%, and 7.96%, respectively. The \(\alpha\)-phase element ratio of \(a_{2}\) and \(a_{3}\) is higher than that of other points, and the \(\beta\)-phase element ratio is lower than that of other points, resulting in the earliest fatigue fracture in the interval between two points. Compared with the fracture interval detection results of Scheme 1, there was no obvious non-uniform particle and hole formation on the surface of the titanium alloy workpiece of Scheme 2. Although dislocations and other phenomena still existed in some local areas, there were no obvious microcracks.
## 4 Flight Tests  
A prototype platform is built to conduct the external field test to verify the simulation model's feasibility and the calculations' accuracy. The prototype platform is shown in Figure 7. Specifically, the tethered UAV platform is selected to have a frame wheelbase of \(L\) = 450 mm and a blade of JMRRC 8045. By replacing different materials, the tethered cable is chosen to have a density of \(\rho\) = 0.92 g/cm\({}^{3}\) and \(\rho\) = 1.38 g/cm\({}^{3}\) for the comparative test. It should be added here that the flight control system of the test prototype built in this paper adopts APM2.8, and the ground control station is Missionplanner. The MP ground station operation interface and the flight log are shown in Figure 11. After the MissionPlanner ground station has planned the flight mode and trajectory of the tethered UAV, the tethered cables of different densities are replaced to carry out the autonomous mission. The tethered UAV carries out the mission autonomously, as shown in Figure 12, and the tethered UAV goes through the three phases of takeoff, hovering, and landing.  
Figure 10: Lyapunov exponential spectra for different cable densities during the landing stage.  
The flight log data in the flight control are shown in Figures 13 and 14. The flight log data are processed, and the results are shown in Figure 15. Analyzing Figure 15, it can be learned that when the density of the tether cable is \(\rho=0.92\) g/cm\({}^{3}\), the position change of the tethered UAV in the x and y directions is smaller than that when \(\rho=1.38\) g/cm\({}^{3}\), which can be used to validate the results in Figure 8 as well as Table 2. This proves that the simulation model built in this experiment and the prototype model are correct. It also proves that increasing the tether cable's density will harm the tethered UAV's stability.  
Figure 11: MissionPlanner ground station interface and flight log interface.  
Figure 12: Real-life flight test of a tethered UAV.  
Figure 13: Displacement in x-direction during flight: (**a**) \(\rho=0.92\), (**b**) \(\rho=1.38\).
# Analysis of Acoustic Emissions for Determination of the Mechanical Effects of Scratch Tests
## 1 Introduction
They used strips of 304 L stainless steel formed over a powdered metallurgy (PM) tool steel and two different lubricants and found the AE signature for galling to be 20 to 160 kHz [18]. [PERSON] et al. conducted research to distinguish between abrasive and adhesive wear mechanisms using AE monitoring techniques. [PERSON] et al. reported the frequency peak of the AE signals occurs at around 1100 kHz for adhesive wear, whilst the frequency peaks are distributed in the region from 250 kHz to 1000 kHz for abrasive wear; thus, wear mechanisms can be recognized from the features of the AE frequency spectrum. [PERSON] et al. investigated wear in stamping and showed that adhesive wear ranged from 100 to 500 kHz, and abrasive wear from 100 to 200 kHz [19]. [PERSON] et al. and [PERSON] investigated the AE response of different aerospace materials (NI-based alloys and steel alloys) when undergoing single grit scratch tests [20; 21]. By analyzing the AE signal with Short-time Fourier Transform (STFT) methods, [PERSON] et al. and [PERSON] showed that each of the four materials resulted in distinct frequencies from 100 kHz to 575 kHz. This result contradicts the findings of [PERSON] et al., which state that all materials should behave similarly. For the SG tests, the prominent AE feature frequencies of the scratches are expected to fall in the range 100-550 kHz, which are similar to the AE feature frequencies in grinding tests experienced in previous work. [PERSON] et al. correlated the acoustic emission to the deformation mechanisms associated with the damage mechanisms during SMF [22]. Their key results showed the specific damage mechanism in relation to the amplitude and duration of the waves and frequency content (see Figure 1). According to these works, the frequency response for sliding wear galling could be anywhere between 60 kHz and 1100 kHz, depending on the wear mechanism and/or test set up. However, it is expected that the initial signs of galling wear result in signals at the lower end of this range. None of these works report on the AE response during \"normal\" or \"pre-galling\" operating conditions, which will be key for developing the AE technology and techniques for detecting and reacting to the development of galling wear. Therefore, our research uses scratch testing to investigate the AE response of different materials when the applied load is insufficient to produce galling, utilizing Fourier analysis, mean frequency, centroid frequency, and Shannon entropy. The results show that the AE frequencies and behavior are specific to the setup and the materials/geometries used.
## 4 Experimental Results  
In this section, in order to test the systems against data provided by other measuring devices, two different events are examined, both pertaining to summer 2018, one in July and the other in August.  
In particular, a comparison between the estimates of rainfall intensity returned by the WR described in Section 3.2, a RG located on Monte Gazzo, and the proposed SRS sensors is provided. The radar reference data for the comparisons carried out in the following are obtained by evaluating the estimated rain intensity in the map's pixel that contains the Monte Scarpino landfill.