esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Comparison of BSMH and Other Benchmarks
#### 5.4.5 Packet Delivery Ratio  
Figure 17 depicts the delivery ratio of the protocols. As shown, BSMH and LVGR achieve the best performance with the delivery ratio approximately equals to 1 in both 1-n and n-n communication types. The reason is because BSMH and LVGR forward all packets along hole-bypassing paths. As hole-bypassing paths do not intersect the holes' interior, the local minimum problem is solved thoroughly, and all packets are delivered to the destinations. GPSR achieves a good performance in topologies where the sources stay outside of concave areas of the holes, e.g., Topologies 4, 5, 6. When the sources stay inside of concave areas of the holes, e.g., Topologies 1, 2, GPSR suffer from local minimum problem and some packets are dropped. In all the cases, the delivery ratio of GPSR is higher than 97%.  
Figure 16: Average energy consumption per packet. (**a**) 1-n communication. (**b**) n-n communication.  
EDGR attains a high delivery ratio in Topologies 3, 4, 5, 6, but its performance degrades severely in Topologies 1, 2. In the worst case, i.e., Topo 2 in n-n communication, EDGR can delivery only less than 70% of packets to the destinations. We observe that the main reason for packet dropping of EDGR is because the burst packets (which are used to determine the anchor list) could not come back to the source before sending data packets.
## 4 Numerical Experiments
### Experiment Design  
Based on the simulation model, we took the design parameters \(\lambda_{a}=0.06\), \(n=3\), \(m=2\), \(N=165\) as the benchmark, changed the values of each design parameter separately to form a total of four groups of design parameter value schemes for simulation experiments. Each group includes different values of three corresponding design parameters, as shown in the first four rows of Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Parameters and Results** & **Benchmark** & **Group 1 (\(\lambda_{a}\))** & \multicolumn{4}{c}{**Group 2 (\(n\))**} & \multicolumn{4}{c}{**Group 3 (\(m\))**} & \multicolumn{4}{c}{**Group 4 (\(N\))**} \\ \hline  & \(\lambda_{a}\) & 0.06 & 0.04 & 0.05 & 1.00 & 0.06 & 0.06 & 0.06 & 0.06 & 0.06 & 0.06 & 0.06 & 0.06 & 0.06 \\ Design & \(n\) & 3 & 3 & 3 & 3 & 2 & 5 & 7 & 3 & 3 & 3 & 3 & 3 & 3 \\ parameters & \(m\) & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 1 & 3 & 4 & 2 & 2 & 2 \\  & \(N\) & 165 & 165 & 165 & 165 & 165 & 165 & 165 & 165 & 165 & 165 & 145 & 195 & 215 \\ \hline  & \(\mu_{1}\) & 452.2 & 457.3 & 448.8 & 454.5 & 409.7 & 528.1 & 576.8 & 412.6 & 507.2 & 564.1 & 407.7 & 514.6 & 566.4 \\ Fitting & \(\mu_{2}\) & 266.6 & 348.8 & 307.3 & 214.2 & 294.6 & 227.9 & 205.2 & 231.2 & 295.4 & 314.4 & 251.5 & 286.7 & 273.3 \\ parameters & \(D\) & 577 & 522 & 656 & 750 & 683 & 495 & 428 & 549 & 629 & 696 & 564 & 576 & 598 \\  & \(U\) & 816 & 782 & 839 & 885 & 839 & 782 & 736 & 747 & 857 & 902 & 736 & 908 & 989 \\ \hline  & \(C\) & 91.53 & 93.33 & 94.58 & 93.51 & 75.15 & 97.74 & 108.4 & 79.55 & 98.75 & 105.1 & 74.19 & 96.94 & 107.3 \\ Measurement & \(k_{C}\) & 0.888 & 0.852 & 0.936 & 0.960 & 0.768 & 0.876 & 0.996 & 0.828 & 0.936 & 0.972 & 0.684 & 0.960 & 1.032 \\ results & \(\varphi_{d}\)\({}^{*}\) & 281.9 & 726.6 & 159.7 & 73.00 & 39.46 & 198.5 & 704.0 & 216.6 & 174.4 & 100.2 & 242.3 & 227.4 & 217.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Design parameters and corresponding results to different schemes.  
Then we carried out simulations to obtain groups of flow-density-speed data in the roadway, and fitted the model shown in Equation (13) to obtain the fitting parameters (\(\mu_{1},\ \mu_{2},D\) and \(U\)), as shown in the middle four rows of Table 4. The measurement results (\(C,k_{C}\), and \(\varphi_{d}\)) of fitting are also shown in the last four rows of Table 4.
# Study on the Effectiveness of Multi-Dimensional Approaches to Urban Flood Risk Assessment
###### Abstract  
Increasing frequency and severity of urban flooding, driven by climate change and urban population growth, present major challenges. Traditional flood control infrastructure alone cannot fully prevent flood damage, highlighting the need for a comprehensive and multi-dimensional disaster management approach. This study proposes the Flood Risk Index for Building (FRIB)--a building-level assessment framework that integrates vulnerability, hazard, and exposure. FRIB assigns customized risk levels to individual buildings and evaluates the effectiveness of a multi-dimensional method. Compared to traditional indicators like flood depth, FRIB more accurately identifies high-risk areas by incorporating diverse risk factors. It also enables efficient resource allocation by excluding low-risk buildings, focusing efforts on high-risk zones. For example, in a case where 5124 buildings were targeted based on 1 m flood depth, applying FRIB excluded 24 buildings with \"low\" risk and up to 530 with \"high\" risk, reducing unnecessary interventions. Moreover, quantitative metrics like entropy and variance showed that as FRIB levels rise, flood depth distributions become more balanced--demonstrating that depth alone does not determine risk. In conclusion, while qualitative labels such as \"very low\" to \"very high\" aid intuitive understanding, FRIB's quantitative, multi-dimensional approach enhances precision in urban flood management. Future research may expand FRIB's application to varied regions, supporting tailored flood response strategies.  
urban flood; risk; inundation; disaster Article
## 2 Satellite-Based Cloud and Radiation Climate Data Record  
In this study, we use the data from the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data, CLARA-A3 [21]. This climate data record has a rich history of dedicated, continuous developments and improvements dating back to its beginning 25 years ago in the framework of EUMETSAT's Satellite Application Facility on Climate Monitoring [46]. CLARA-A3 provides the retrievals of global cloud fraction, cloud top and physical properties, surface albedo, and incoming surface solar radiation, as well as the radiation at the top of the atmosphere. Furthermore, CLARA-A3 offers substantial improvements to its previous version, CLARA-A2. It employs cloud probabilistic detection based on the Naive Bayesian theory, while the cloud top property algorithms employ artificial neural networks. A number of recent studies have documented the theoretical basis, validations, and improvements in the CLARA-A3 climate data record [21; 26; 47; 48; 49].  
In this study, we use both the Level 2b daily means and the Level 3 monthly means of cloud and radiation products that are available at the 0.25 degree spatial resolution globally. The AVPOS (AVHRRs from all polar satellites) aspect of this dataset is analysed here. This means that the Level 2b and Level 3 data are prepared using quality-controlled retrievals from AVHRR sensors flying onboard all available NOAA and MetOp satellites instead of using only one prime morning or afternoon NOAA and MetOp satellite at a time. The CLARA-A3 CDR currently covers the period from 1979 to 2020 with an interim, near-real-time CDR thereafter. In this study, we evaluate different geophysical variables, namely total cloud fraction, daytime and nighttime cloud fraction, low cloud fraction, cloud top pressure, and incoming surface solar radiation.
## 3 Results and Discussion
### Effect of Interstitial Scattering and Absorption  
The difference when including interstitial IOPs (scattering and absorption in the water around and within the coral shape) in the modelling of reflectance averaged over a 50% mix of coral and substrate was in general, less than 3% in any band for wavelengths less than 690 nm (Table 3), and for most coral shapes of the order of 2% or less. This showed that the previous back-of-envelope calculation that suggested a 4% error at 700 nm, based on absorption over path lengths of 10 cm, was quite reasonable. Beyond 700 nm, absorption by pure water is high and the discrepancy when omitting interstitial IOPs became larger, up to 5%. However, in a practical application, unless corals almost touch the water surface, the absorption in the water column above the canopy will dominate over the within-canopy effect, so the discrepancies above 690 nm are of minor consequence. The cost of 2% or 3% in accuracy when omitting the interstitial IOPs comes at the advantage, not only of substantially faster computation in the 3D model, but also the BRDF of the canopy can be decoupled from the IOPs of the overlying water column. That is, a BRDF can be computed based on coral canopy structure and surface reflectance, and then the overlying water column can be specified with independently varying IOPs. Therefore, this answers point 4 in Table 1, and is the strategy that was taken in the BRDF and water column modelling activities (5 and 6 in Table 1). However, this result is scale dependent and only holds in this case because the coral structures were small. With larger corals and reef structures, where the vertical distances can be a meter or more, the effect of interstitial water scattering absorption is likely a more significant component of the reflectance.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 3 Proposal Method and Discussion  
The EE optimization problem presented in (8) poses a significant computational challenge due to its NP-hard nature. This difficulty arises from the discrete variable \(\mathbf{\rho}\) and the unit modulus constraint imposed on \(\mathbf{\Phi}\). Finding optimal solutions for such problems is generally infeasible within reasonable time frames. To address these complexities, we propose a sub-optimal solution strategy using an AO framework.  
In our AO-based approach, we iteratively optimize two sets of variables: the OFDMA resources \(\rho_{k,n}\) and \(p_{k,n}\) and the RIS reflection coefficients \(\mathbf{\Phi}\). Crucially, we keep one set of variables fixed while optimizing the other. This alternating optimization scheme allows us to tackle the intricate EE problem more effectively.  
In the initial stages of our approach, we employ sub-optimal solutions. This choice is motivated by the computational expense associated with obtaining closed-form optimal solutions for all variables simultaneously. Instead, by initially focusing on one set of variables, we streamline the optimization process. Subsequently, in the following step of our AO framework, we introduce the PSO algorithm to further enhance the optimization process.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture  
Today many mega-constellations are already operating, such as Starlink from SpaceX [12], Lightspeed from Telesat [13] or WorldVu from OneWeb [14]. Recently, Amazon has received Federal Communications Commission (FCC) approval for its satellite mega-constellation project, Kuiper [15]. Moreover, the Spanish and China governments have started projects: Startical, for air traffic control by the hand of Enaire [16] and GW from China Satellite Network Group [17].  
There are some studies about LEO satellite constellations from SpaceX, Telesat, OneWeb and Amazon. In [2,18] a comparison of the four systems is made from a technical point of view, estimating the throughput and the number of gateways. In [19] a comparison of OneWeb and SpaceX systems is conducted, verifying the impact of beam coverage on the performance of LEO satellite systems.  
This section makes a brief study of the four gateway systems for the constellations SpaceX, Telesat, OneWeb and Amazon. They are described according to their FCC filings as of September 2022, including pending changes.
# Partial Energy Transfer Model of Lamb Waves Scattering in Materially Isotropic Waveguides
## 3 Numerical Verification of PET Model  
The PET approach is verified based on an application example, for which the geometry is presented in Figure 2, where the partial energy transfer functionality is applied to sub-waveguide No. 3. The incident mode is in the form of a fundamental antisymmetric Lamb wave (A\({}_{0}\)) of central frequency equal to 150 kHz. The selection of the incident mode in the form of the A\({}_{0}\) Lamb wave mode was realized purely from a practical point of view, because it has versatile use in real applications. The overall thickness of the aluminum plate (\(p=2660\) kg/m\({}^{3}\), \(c_{L}=6295\) m/s, \(c_{T}=3100\) m/s \(\rightarrow\) [PERSON]'s modulus E = 68.5 GPa, Poisson's ratio \(\upnu\) = 0.34 [-]) was 10 mm, and the thickness of waveguides 2-4 was equal to 4,3 and 3 mm, respectively, and width \(L\) equal to 0.01 mm. The reference values of the energy scattering coefficients were obtained in the same manner as in [43], i.e., by utilizing the hybrid approach, which combines FEM with the general orthogonality relation. Using the FEM, it is possible to extract the through-thickness stress tensor and displacement vector components of the incident (subscript \(IN\)), reflected (subscript \(R\)), and transmitted (subscript \(T\)) modes at a given distance from the discontinuity (see Figure 3), namely one millimeter. The FEM simulations were realized in COMSOL Multiphysics software in frequency domain. The scattering region and its close vicinity were meshed using linear triangular  
Figure 3: Scattering of Lamb wave at the vertical notch of width L—application of hybrid method. elements with a maximum allowable size of 0.2 mm, whereas the remainder of the geometry, including the perfectly matched layer (PML), which served to eliminate the undesired reflections, was meshed using linear rectangular elements with a size of 0.4 mm (see Figure 4). The excitation of the incident A\({}_{0}\) mode was utilized with Hanning-windowed toneb-urst in the form of normal force, which was applied on opposite surfaces. It should be noted that the applied excitation on given boundaries is antisymmetric with respect to the mid-plane axis.
# Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
## 5 Results and Discussions
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{3}{c}{RMSE} & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{MSE} & \multicolumn{3}{c}{Corr} \\ \hline  & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** & **Min** & **Avg** & **Max** \\ \hline MLR & 0.4072 & 0.5227 & 0.5903 & 4.4745 & 5.0024 & 5.2940 & 0.5178 & 0.5409 & 0.6281 & 20.0212 & 25.024 & 28.0272 & 0.6091 & 0.6998 & 0.7703 \\ PMR & 0.6479 & 0.7324 & 0.8261 & 3.2093 & 3.3903 & 3.8639 & 0.4212 & 0.324 & 0.5472 & 10.2993 & 11.4943 & 14.9299 & 0.6503 & 0.7477 & 0.8451 \\ SVR & 0.8022 & 0.8432 & 0.8792 & 2.3015 & 2.6190 & 2.7020 & 0.0936 & 0.0753 & 0.1063 & 5.2969 & 6.8592 & 7.3010 & 0.8502 & 0.8734 & 0.8904 \\ DT & 0.7882 & 0.8161 & 0.8577 & 2.1743 & 2.2942 & 2.6169 & 0.0851 & 0.1066 & 0.1380 & 4.7276 & 5.2635 & 6.8480 & 0.8300 & 0.8606 & 0.8789 \\ RF & 0.8837 & 0.9948 & 0.9384 & 1.4678 & 1.9786 & 2.2862 & 0.0825 & 0.0946 & 0.1064 & 2.1545 & 3.9147 & 5.2266 & 0.8785 & 0.9028 & 0.9451 \\ KNN & 0.8636 & 0.9111 & 0.9532 & 1.5418 & 1.7558 & 2.2647 & 0.0742 & 0.0857 & 0.0969 & 2.3772 & 3.0829 & 5.1288 & 0.9048 & 0.9281 & 0.9567 \\ ANN & 0.8528 & 0.9093 & 0.9374 & 1.4144 & 1.7297 & 1.9756 & 0.0797 & 0.0801 & 0.0894 & 2.0006 & 2.9918 & 3.9030 & 0.9187 & 0.9382 & 0.9685 \\ LSTM & 0.7537 & 0.8509 & 0.8971 & 1.4146 & 1.4149 & 1.4160 & 0.0793 & 0.0893 & 0.0969 & 2.0011 & 2.0021 & 2.0051 & 0.8845 & 0.9062 & 0.9441 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance metrics after removing the antenna height from the input features of the models. Figure 9: Training and validation loss for both ANN and RNN models. Future research will be directed toward developing a ML-based path loss model that provides better accuracy and stability than other well-known methods. Using the results from this study that the best performance was achieved by the neural networks (i.e., ANN and RNN-LSTM), prediction based on deep neural networks such as using a convolution neural network (CNN) will be investigated. Finally, the impact of the LOS probability will also be considered to provide probabilistic path loss models based on neural networks.
## 4 Discussion
### Impact of Light Energy and Irradiation Time  
An energy density of 20 J/cm\({}^{2}\) is generally sufficient for polymerizing bulk-fill composites to a 4 mm depth [47; 69]. Adjusting irradiation time and power is crucial; reducing them can compromise the degree of cure (DC) and mechanical properties, while increasing them may improve curing depth but risks overheating and shrinkage stress accumulation [39; 40; 69]. Optimizing these parameters based on composite type and clinical needs is essential for success. [PERSON] and [PERSON] [8] found that two light-cured bulk-fill composites were under-cured at a 5 mm depth (<80%) when cured with a monowave LCU for 20 s, while dual-cured composites exceeded 80%. Extending the curing time to 40 improved the light-cured materials but did not affect the dual-cured ones, and extending beyond 40 s did not have any significant effect [8; 28; 31; 52; 53]. Therefore, the irradiation time used in this study was 20 s for uniformity purposes since it was above the manufacturer's recommendations and the emitting energy was not normalized for each LCU.  
Overall, monowave LED LCUs, such as Demi Ultra, provide better light dispersion and effective polymerization for most bulk-fill composites, particularly those with CQ as their primary photoinitiator. Polywave LCUs often do not outperform monowave LEDs in composites with multiple photoinitiators due to limited violet light penetration beyond 2.5 mm in viscous materials [39; 41; 42; 68]. These findings align with the research by [PERSON] et al. [39], [PERSON] et al. [64], [PERSON] et al. [46], [PERSON] et al. [41; 66], and [PERSON] et al. [42], highlighting the need to select the appropriate LCU and curing protocol based on the composite's properties to optimize outcomes.
## 3 Results
### Cell Viability after Printing  
Cell viability was assessed up to 14 days after bioprinting (Figure 8). The viability was lowest directly after printing (91.2 \(\pm\) 7.8%) and the greatest variability was observed after the full 14 days (95.1 \(\pm\) 14.6%). Compared with the viability directly after printing,  
Figure 7: Temperature-dependent gel strength of the carrier material and cell-laden bioinks. The carrier material is shown in black and the bioinks in red (10\({}^{5}\) cells-mL\({}^{-1}\)), green (5 \(\times\) 10\({}^{5}\) cells-mL\({}^{-1}\)), and blue (10\({}^{6}\) cells-mL\({}^{-1}\)). (**A**) The overall temperature sweep for the samples in oscillation mode. (**B**–**E**) Zoomed segments of the temperature curve. (**F**) The corresponding loss factors. Data are means \(\pm\) SD (\(n=3\)).  
we observed significant differences on days 1, 3, and 14, with day 3 showing the most significant difference.  
Transmitted light images were captured during the live/dead cell assay (Figure 9). The largest proportion of dead cells was found directly after printing (right corner), whereas fewer dead cells were detected over time. Meanwhile, the increasing green signal indicated cell expansion. The longer the incubation time, the more cells appeared within the bioink (the exposure time had to be reduced at day 14). The presence of round cell aggregates on day 7 indicated the formation of spheroids.  
Figure 8: Viability of cells directly after printing and at several time points thereafter compared with viability before printing (set at 100%) determined in a live/dead cell staining assay. Data are means \(\pm\) SD (\(n=4\) grids, 3 images each; Kruskal–Wallis test, \({}^{*}p<0.1\), \({}^{**}p<0.05\), \({}^{***}p<0.01\)).
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
###### Abstract  
A new slot-based antenna design capable of producing horizontal polarization for unmanned aerial vehicle (UAV) ground control station (GCS) applications is outlined in this paper. The proposed antenna consists of oversize coaxial cylinders, slots, and slot-feed assembly. Each of the four vertical slots, arranged periodically around the antenna's outer cylinder, emits a horizontally polarized broad beam of radiation, in phase, to produce an omnidirectional pattern. The antenna possesses a low-ripple \(\pm 0.5\) dB in azimuth gain (yaw) due to its symmetric axis shape and an enclosed feed within itself, which does not radiate and interfere with the main azimuth pattern. This is crucial for a UAV GCS to symmetrically extend its coverage range in all directions against yaw planes. Simulation and measurement results reveal that the antenna maintains stable gain in the omnidirectional pattern (+0.5 dB) over the entire operational frequency band (2.55 GHz to 2.80 GHz), where S11 is lower than \(-10\) dB. A further advantage of this configuration is its enhanced polarization purity of \(-40\) dB over the full frequency band. The direct-current (DC) grounding approach used in this antenna is beneficial due to its electrostatic discharge (ESD) and lightning protection. Furthermore, its aerodynamic, self-supporting, and surface-mount structural shape makes this antenna a good and worthy choice for a UAV GCS.  
horizontal polarization; UAV ground station; Omni-directional 2021
## 4 Simultaneous Localization of AUV and Fixed Docking Station  
The states of AUV and DS are estimated simultaneously by the FastSLAM2.0 algorithm in the process of homing an AUV to the fixed DS. FastSLAM algorithm assumes that the position and attitude of AUV follow the known hypothesis distribution, and uses particle filter to estimate the posterior probability distribution of the position and attitude of AUV, where each particle represents a possible position and attitude. An EKF filter is used to estimate the posterior probability distribution of features in the environment. The joint status of SLAM is expressed as follows:  
\[p\left(s^{k},\Theta\mid z^{k},u^{k}\right)=p\left(s^{k}\mid z^{k},u^{k}\right) \times\prod_{m=1}^{M}p\left(\theta_{m}\mid s^{k},z^{k},u^{k}\right) \tag{19}\]  
where \(s^{k}=s_{1},s_{2},\cdots s_{k}\) represents the state sequence of AUV, and \(s_{k}=[x_{k},y_{k},\Psi_{k}]^{T}\) are the position and heading angle information of AUV, respectively. \(\Theta_{m}=\theta_{1},\theta_{2},\cdots,\theta_{m}\) are the features in the environment. \(z^{k}=z_{1},z_{2},\cdots,z_{k}\) represents the measurement sequence. \(u^{k}=u_{1},u_{2},\cdots,u_{k}\) represents the input sequence. FastSLAM algorithm includes the following four basic iteration steps:  
1. Sampling new trajectory particles according to the motion model of AUV.
2. When new measurement is received, the EKF algorithm is used to update the state of feature in each particle.
3. Calculating the importance weight of each particle.
4. Resampling.  
Figure 16: The regression result of the variance of random noise and working distance in the process of AUV docking with mobile DS.  
Figure 15: The regression result of system noise and working distance in the process of AUV docking with mobile DS.  
The formulas corresponding to the above algorithm are sorted out in Table 2:  
The motion model of AUV and measurement model are shown in Equations (2) and (3).