esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Structure Description
### The FE Model Construction  
This study used a single-beam fishbone model to simulate the cable-stayed bridge system. Based on the parameters listed in Table 1, finite element models for two types of  
Figure 4: Comparison of equivalent elastic modulus.  
Figure 3: Comparison of two schemes of stay cables: (**a**) section area; (**b**) cable diameter.  
schemes were established using ANSYS 2021. Among them, the pylon and auxiliary pier were modeled using Timoshenko beam elements (BEAM188), while the main beam was modeled using Euler-Bernoulli beam elements (BEAM4). The stay cable was simulated using LINK10 units divided into 10 segments. The structure was a fully floating system, so only lateral wind-resistant bearings were provided between the pylon and the main beam, allowing the main beam to undergo longitudinal displacement relative to the pylon and auxiliary piers. The weight of the bridge deck system and the main beam diaphragm was simulated using the MASS21 mass unit. This model contained a total of 4657 nodes and 5254 units, as shown in Figure 5.  
After performing cable tension adjustment and optimization in Midas Civil 2022 using the stiffness reduction method and unknown load factor method, the final cable force distributions for both schemes under identical dead load conditions are shown in Figure 6. The results demonstrate that, compared with the steel cable scheme, the CFRP cable scheme exhibited significantly reduced cable forces in long-span cables, while short cables showed only minor force reductions. This phenomenon primarily stems from the substantially higher self-weight of steel cables compared with CFRP cables. To counteract their own weight, steel cables require significantly higher initial stresses, leading to a sharp increase in cable forces as the cable length increases.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **Material** & \begin{tabular}{c} **Tensile Strength** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Modulus** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Density** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} &
\begin{tabular}{c} **Area (m\({}^{2}\))** \\ \end{tabular} \\ \hline \multirow{2}{*}{Stay cable} & \(1860\) HS steel CFRP & \(1860\) & \(195\) & \(0.3\) & \(8005\) & \((0.63\)–\(1.41)\times 10^{-2}\) \\  & CFRP & \(2550\) & \(180\) & \(0.28\) & \(1500\) & \((0.55\)–\(1.30)\times 10^{-2}\) \\ \hline Main girder & Q345 & \(345\) & \(200\) & \(0.3\) & \(8005\) & \(2.1\)–\(3.53\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of primary structural members.  
Figure 5: ANSYS finite element model: (**a**) isometric view; (**b**) pylon anchorage zone; (**c**) bottom of the pylon and pylon–girder constraint; (**d**) auxiliary pier–main girder constraint.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Dimensioning of Communication Networks  
Dimensioning a communication network involves determining the necessary resources to meet service demands while optimizing cost and performance. This process focuses on calculating the required capacity of network elements, such as links, switches, and servers, to ensure that optimal performance parameters, like latency, throughput, and packet loss, are within acceptable limits. It involves analyzing traffic patterns, predicting future demand, and applying mathematical models and simulation tools to estimate resource requirements. Key factors considered include the number of users, the types of applications being used, and the desired level of reliability. Dimensioning aims to prevent congestion, over-provisioning, and under-provisioning, thereby ensuring efficient utilization of network infrastructure.  
However, the typical dimensioning of optical networks is associated with the cost estimation of the network [3; 4]. This is required at the planning stage of the optical network design and deployment [4]. In these processes, all the network requirements are analyzed, and based on that, the costs are estimated using different cost estimation parameters. Very often, the statistical analysis and design are essential for such exercises as they exploit the key characteristics of the optical networks. Several similar techniques have been developed in recent years [3; 4; 27; 28; 31; 32; 33].
## 3 Simulation Results and Interpretation
### Scenario 1: The Effect of Distance
#### 3.1.2 Simulation with Clutter Model 2  
We present the simulation of two targets using clutter model 2 (targets embedded in Rayleigh distribution clutter). The shape parameter b of the Rayleigh distribution is confined to the interval [0,300] (Figures 19-21).  
Figure 17: Targets simulated with clutter model 1 (a = 200 and b = 0.9) using (a) conventional OS-CFAR and (b) proposed detector.  
Figure 18: Targets simulated with clutter model 1 (a = 200 and b = 0.3) using (a) conventional OS-CFAR and (b) proposed detector.  
We gradually increased the shape parameter b (b = 100, b = 200, and b = 300). In Figures 19b, 20b and 21b, we observe the ability of the proposed detector to successfully reduce the false alarms and clearly distinguish the two target positions, with the first target at M = 36 and the second target at M = 43. On the other hand, in Figures 19a, 20a and 21a, the conventional OS-CFAR detector detects multiple targets, one of which is located  
Figure 19: Targets simulated with clutter model 2 (b = 100) using (**a**) the conventional OS-CFAR detector and (**b**) the proposed detector.  
Figure 20: Targets simulated with clutter model 2 (b = 200) using (**a**) conventional OS-CFAR and (**b**) proposed detector.  
at a range of 84 km, while the remaining targets are false alarms. We clearly observe the differences between target amplitudes in the case of the conventional OS-CFAR detector and the target amplitudes in the case of our detector, which allow us to show the efficiency of the proposed method.  
To assess the limits of the proposed detector in this case of clutter model 2, we increase the shape parameter b \(>\) 300. We set the shape parameter b at 600 (b = 600) and ran the simulation three times.  
After the simulations, we observe that, in Figure 22b, the proposed detector shows the presence of two targets at M = 40 and M = 44, while the conventional OS-CFAR detector shows only one target, and the rest are false alarms. When we repeat the simulation with the same configuration (Figures 23 and 24), the proposed detector once again detects the two targets but at different positions M = 36 and M = 45 for Figure 23b, then M = 39 and M = 43 for Figure 24b. The conventional OS-CFAR detector still distinguishes several targets where one is at M = 83 while the others are false alarms.  
Figure 23: Targets simulated with clutter model 2 (b = 600) using (a) conventional OS-CFAR and (b) proposed detector.  
Figure 22: Targets simulated with clutter model 2 (b = 600) using (a) conventional OS-CFAR and (b) proposed detector.
# Validation of Copernicus Sea Level Altimetry Products in the Baltic Sea and Estonian Lakes
## 5 Conclusions
However, the offshore water level data from the NEMO model was highly accurate (\(r\) = 0.99, ME = \(-\)0.09 m), while the satellite sea level accuracy was similar to that of the coastal areas. Conceptualisation, A.L., T.K., S.R., R.U. and K.-H.T. ; Methodology, A.L., T.K., S.R. and K.-H.T. ; Software, K.-H.T. and S.R. ; Validation, A.L. and T.K. ; Formal analysis, A.L., T.K. and S.R. ; Investigation, A.L., T.K. and S.R. ; Data Curation, A.L., T.K. and S.R. ; Writing--Original Draft Preparation, A.L., T.K. and S.R. ; Writing--Review and Editing, A.L., T.K., S.R., K.-H.T. and U.S.; Visualization, S.R., K.-H.T. ; Funding Acquisition, A.L., R.U., K.-H.T., and U.S. All authors have read and agreed to the published version of the manuscript. This study was partially supported by the Estonian Research Council grants PUT 1553 (Joint Estimation of Geocentric Sea-Level Rise and Vertical Crustal Motion of the Baltic Sea Using Multi-Mission Satellite Altimetry Over the Last Seven Decades') and PUT1439 ('Future marine climate and ecological risks in the Baltic Sea'), and by the European Regional Development Fund within the National Programme for Addressing Socio-Economic Challenges through R&D (RITA1/02-52-08, RITA1/02-52-04): 'Use of remote sensing data for elaboration and development of public services'. It was also supported in part by the Ministry of Science and Technology (MOST), Taiwan, under projects 108-2621-M-008-008 and 108-2911-1-008-507. We would like to thank Assistant Editor [PERSON], and two anonymous reviewers for their thorough and critical reviews which significantly improved this paper. The authors are grateful to the Estonian Environmental Agency and the Department of Marine Systems, Tallinn University of Technology ([PERSON], [PERSON]) for the tide gauge data. The authors declare no conflict of interest.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 4 Network Performance Optimization Based on Dynamic Game Theory
### Network Performance Model
#### 4.1.3 Maximum Saturation Performance  
Take the derivative of Equation (19) with respect to \(p_{0}\) to maximize bandwidth utilization. Setting it equal to zero, we obtain the optimal approximate probability solution \(p_{option}\) as follows:  
\[p_{option}=\frac{\sqrt{[n+2(n-1)(\frac{T_{k}}{T_{l}}-1)]/n}-1}{(n-1)(\frac{T_ {k}}{T_{l}}-1)}\approx\frac{1}{n\sqrt{T_{c}/2T_{l}}} \tag{25}\]  
The data packet collision optimal probability is:  
\[p_{coll\_option}=1-(1-\frac{1}{nk})^{n}-\frac{1}{k}(1-\frac{1}{nk})^{n-1} \tag{26}\]  
Referring to \(\left(1-\frac{1}{nk}\right)^{n}=x\) and \(\lim\limits_{nk\rightarrow+\infty}(1-\frac{1}{nk})^{nk}=\frac{1}{e}\), the optimal collision probability limit value is calculated when the number of stations approaches infinity:  
\[p_{coll\_limit}=1-\frac{k+1}{k\sqrt[k]{e}} \tag{27}\]  
The stations send the data packets by \(p_{option}\), reduce the collision probability, and maximize the bandwidth utilization. However, channel asymmetry and noise interference are not considered. In addition, the performance is affected by selfish stations. Inspired by the game theory, this study proposes an improved adaptive \(p\)-persistent CSMA-based dynamic game for improving the network saturation performance.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Dataset Selection
In addition, we use some low-light images in the SPEED-UE-Cube dataset to verify the method proposed in this paper. The comparison before and after processing is shown in Figure 11.  
This experiment processed 3940 satellite images containing the Earth and 51,671 individual satellite images, totaling 55,611 deep-space background images. In terms of experimental design, a multi-dimensional evaluation method was employed to compare and validate against traditional image enhancement techniques and deep learning models. The control group for the experiment consisted of: (1) classic traditional methods, such as histogram equalization and gamma correction; (2) traditional methods based on Retinex theory; and (3) advanced deep learning models, NeRCo and RetinexFormer, which represent cutting-edge approaches in the realm of low-light enhancement.
## 1 Introduction
[15] compared the aerodynamic characteristics of single-pole and self-supporting telecommunication towers at different heights, finding that self-supporting towers have smaller lateral displacements under the same loads. Although the study of wind load characteristics on slender towers has always been a focal point of interest among scholars both domestically and internationally, existing research and industry standards mostly target individual tower structures [16; 17; 18; 19; 20; 21; 22; 23]. With the acceleration of modernization, some slender tower structures need to have ancillary facilities installed around them to meet usage requirements. The arrangement of these ancillary facilities significantly impacts the wind load characteristics of slender towers. For instance, since 2019, with the widespread adoption of 5G networks, to reduce construction costs, most 5G telecommunication towers have been established by adding new equipment or antennas to existing 4G and 3G telecommunication towers [24; 25; 26; 27; 28; 29; 30]. Adding telecommunication antennas and other ancillary facilities to telecommunication towers significantly alters their original aerodynamic shape, leading to a substantial increase in wind loads, which might exceed the original structural capacity and threaten the safety of the structure. In light of these considerations, existing research, while extensive, offers limited guidance on assessing the impact of ancillary facilities such as antennas on the wind loads experienced by slender towers. Therefore, this study employed three-dimensional numerical simulation methods to systematically analyze the impact of various antenna arrangement parameters, such as arrangement number, arrangement form, and arrangement layers, on the structural wind loads of telecommunication towers. It also summarized a calculation method for these wind loads considering the impact of antenna arrangement parameters, providing a reliable basis for the structural design and safety assessment of telecommunication towers in practical engineering applications.
## 4 Analysis
### Imputation Performance
It was found that the proposed approach consistently improved the performance against the existing methods. Especially, the proposed model showed better performance in the SBTSs including the congested section state, which indeed was the highlight of the proposed model. In terms of the percentage differences of RMSE between the proposed method and other methods, the range was about 12.2% to 69.3%. The KNN method showed the largest difference while the ANN showed the smallest difference. Figure 7 shows the real data and imputation results of the proposed model (Case 5) and other compared models for a particular weekday (20 July 2016). The proposed model was found to capture the precursor of transitions significantly faster. Unlike the proposed model, the two existing models with which the proposed model was compared with, tended to provide only the average value of free-flow and congestion when the transition started. This shows that it is promising to adopt the proposed approach of categorizing traffic data by each SBTS, using \(Ang_{t}\) and selected variables that would improve the performance of missing value imputations. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(a) Missing ration 10\%**} \\ \hline \multirow{2}{*}{Model} & \multicolumn{2}{c}{RMSE (km/h) of Validation (Percentage Difference)} \\ \cline{2-4}  & SBTS II & SBTS III & Aggregate \\ \hline Linear Interpolation & 13.501 (36.5\%) & 15.276 (39.7\%) & 14.542 (38.5\%) \\ ANN & 11.102 (12.2\%) & 12.745 (16.5\%) & 12.068 (14.9\%) \\ KNN & 16.263 (64.4\%) & 16.352 (49.5\%) & 16.314 (55.4\%) \\ Case 5 & 9.891 & 10.936 & 10.500 \\ \hline \multicolumn{4}{c}{**(b) Missing ration 20\%**} \\ \hline \multirow{2}{*}{Model} & \multicolumn{2}{c}{RMSE (km/h) of Validation (Percentage Difference)} \\ \cline{2-4}  & SBTS II & SBTS III & Aggregate \\ \hline Linear Interpolation & 15.815 (42.9\%) & 16.165 (46.0\%) & 15.990 (44.4\%) \\ ANN & 12.691 (14.6\%) & 12.799 (15.6\%) & 12.713 (14.8\%) \\ KNN & 18.304 (65.3\%) & 17.479 (57.9\%) & 17.858 (61.3\%) \\ Case 5 & 11.070 & 11.072 & 11.071 \\ \hline \multicolumn{4}{c}{**(c) Missing ration 30\%**} \\ \hline \multirow{2}{*}{Model} & \multicolumn{2}{c}{RMSE (km/h) of Validation (Percentage Difference)} \\ \cline{2-4}  & SBTS II & SBTS III & Aggregate \\ \hline Linear Interpolation & 16.182 (41.7\%) & 15.490 (37.2\%) & 15.781 (39.1\%) \\ ANN & 13.494 (18.2\%) & 12.907 (14.4\%) & 13.153 (16.0\%) \\ KNN & 19.338 (69.3\%) & 17.851 (58.2\%) & 18.482 (63.0\%) \\ Case 5 & 11.420 & 11.286 & 11.342 \\ \hline \hline \end{tabular} NOTE: ANN is artificial neural networks and KNN is k-nearest neighbor.  
\end{table} 
Table 3: Comparison result according to missing ratio.
# Femtosecond Laser-Inscripted Direct Ultrafast Fabrication of a DNA Distributor Using Microfluidics
## 2 Methods  
### PDMS Sample Preparation  
This study focused on the fabrication of microfluidic channels for DNA distribution using femtosecond laser micro-scribing in PDMS (Sylgard 184 silicone elastomer kit; Dow Corning, Midland, MI, USA). To prepare a PDMS block (length \(\times\) width \(\times\) thickness of 75 mm \(\times\) 25 mm \(\times\) 5 mm) for femtosecond laser ablation, the silicone elastomer was mixed with the curing agent at a ratio of 10:1. The mixed silicone elastomer was poured into a glass petri dish, placed in a vacuum desiccator (DURAN Group GmbH, Mainz, Germany) to degas the PDMS for 20 min, and then cured in an oven at 80 \({}^{\circ}\)C for 1 h.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### LoRa Sensor Nodes
#### 3.4.2 Subsurface Node (SN)  
The SN and LCI are both subsurface attachments to the base module. After they are installed in the subsurface on site, the base module can simply be installed on top of the housing with a simple 3D-printed adapter (see Section 6). Inside the rod of the SN, one inclination sensor is included to be able to detect tilting of the rod. The casing of the rod can be made of PVC or stainless steel, but steel casings are preferable since they can be installed without pre-drilling. The detailed sensor- and connection-layout is shown in Figure 4.  
The inclination measurement is performed by a triaxial accelerometer (BMA456, 0.02\({}^{\circ}\) precision). It is connected to the basic node via the I2C bus. To house this sensor, a 3D-printed housing has been developed. It provides safety for the sensor, as well as a steady fixture for accurate readings. The sensor unit itself and the cable connections are waterproofed with a 2-component sealing gel. The SN is usually installed into depths of up to about 2 m.  
The groundwater measurement is performed using a load cell together with a buoyancy rod made of PVC, which allows for continuous measurement, but only in the top part above the inclination sensor, depending on the length of the rod.
## 5 Results and Discussion
### Results for the 33-Node AC MG Operating in Grid-Connected Mode
#### 5.1.2 Statistical Analysis of the Objective Function \(FO_{2}\) (Energy Losses)  
Regarding the reduction in energy losses, the implemented methodologies achieved outstanding results, with the best solution reporting a maximum average reduction of 79.7345% when compared with the base case (3378.9199 kWh). Figure 13 illustrates the average values obtained by each methodology, highlighting PGA's 85.1408% reduction. This reduction corresponds to improvements of 23.5485%, 1.4032%, 1.7154%, 7.5312%, and 2.0005% relative to MC, PSO, JAYA, GNDO, and MVO, respectively.  
Figure 14 corresponds to a boxplot showing the 100 solutions obtained by each methodology, with the aim of analyzing the dispersion of the results in the context of the loss reduction problem for the 33-node MG operating in grid-connected mode. This analysis indicates that the methodology exhibiting the best performance is PGA, with a standard deviation of 0.0197%. This improvement corresponds to reductions of 1.1886%, 2.0995%, 2.8594%, 2.2012%, and 1.3092% relative to the standard deviations obtained by MC, PSO, JAYA, GNDO, and MVO.  
In terms of computational performance regarding \(FO_{2}\), MC reported the shortest average processing time for the 100 runs, completing the optimization in 162.5012 s. However, it is important to highlight that the proposed PGA demonstrated a highly efficient performance, ranking as the second fastest (231.4558 s) among all evaluated strategies.  
Although not the absolute fastest, PGA significantly outperformed several other advanced metaheuristics, such as PSO, JAYA, and GNDO, which required notably longer times to complete the same task. In particular, GNDO exhibited the highest processing time (360.0060 s). The average value for all methods was approximately 264.3522 s, meaning that PGA performed well below the average, which confirms its computational efficiency and practical suitability for the optimization of technical performance in MGs.  
Figure 13: Average loss reduction in the 33-node AC MG operating in grid-connected mode.
# A Design for an Internet Router with a Digital Optical Data Plane
## 1 Introduction  
A router with an optical data plane is needed to support high-bandwidth communication on the Internet. Optical packet switching can support the Internet's multiplexing efficiency and flexibility, while avoiding costly and complex conversion from optical formats required for high-bandwidth, long-distance transmission. It differs from circuit switching, which can already be accomplished optically with micro-mirrors, wavelength selection, or electro-optical switches, but which is effective only for traffic that has very long and predictable patterns. An Internet router is the most common form of packet switch used for long-distance communication, and its operation can be reduced to four core functions--hop count decrement, header lookup, checksum calculation, and packet traffic merging. This paper presents our experience implementing or simulating each function as optical processing of packets and the potential for future integration to a complete optical packet switching device, based on the existing requirements of Internet routers. Our goal is to determine the implications of an all-optical solution on emerging approaches to digital optical processing. An earlier version of this work was presented as an invited paper at SPIE Photonics West [1], updated herein with additional history (Section 1.2), significant clarifications (especially to Sections 5 and 6), a detailed discussion of the implications of our assumptions (in Sections 1.3 and 8), and details on recent related work on processing-compatible all-optical phase regeneration (Section 8).
## 3 Methodology
### Stability Analysis of NEP  
The coefficient of variation equation based on grid scale was established to measure the stability of \(NEP\) in the CGE from 2010 to 2020 [37].  
\[C_{v}=\frac{\sigma}{\overline{x}}\]  
where \(\sigma\) is the standard deviation; \(\overline{x}\) is the average value and \(C_{V}\) is the coefficient of variation, which reflects the degree of dispersion of \(NEP\). The smaller the \(C_{V}\), the less the data fluctuate with time and have high stability, and vice versa.  
According to the calculation results, the CGE was divided into five areas: \(0\leq C_{V}\leq\)0.5 indicates low, \(0.5<C_{V}\leq 1.5\) indicates relatively low, \(1.5<C_{V}\leq 2.5\) indicates medium, \(2.5<C_{V}\leq 5\) indicates relatively high and \(C_{V}>5\) indicates high.