esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Comparison between DL-LET and LET Joints
### Testing  
The LET joint and DL-LET joint prototypes were fabricated to verify the performance of the proposed designs, as shown in Figure 9, and on the left is the DL-LET joint. The measured dimensions of the LET and the DL-LET joints are 24.97 mm \(\times\) 49.89 mm and 24.96 mm \(\times\) 49.90 mm, respectively. On a Universal Testing Machine, clamping the joints were clamped on both ends and stretched at the speed of 0.01 mm/min, and the force sensor recorded the tensile force. The experimental setup is shown in Figure 10.  
When the tensile load is 30 N, the measured deflections of the LET and the DL-LET joints are 0.432 mm and 0.072 mm, respectively. It can be concluded that the deformation of LET is about 6.0 times larger than that of the DL-LET. The deflections of the two joints under different tensile loads are compared in Figure 11. The discrepancy between the experimental and FEA results could be attributed to bonding imperfections between the beryllium bronze and H18 aluminum foil layers. Furthermore, the combined effects of various factors will affect the measurement results, such as manufacturing error, equipment error, etc. Although the experimental results are not as perfect as the FEA results, the trend of experimental results is in line with the expected design requirements.  
Figure 8: Comparison of FEA results of DL-LET and LET joints, (**a**) Bending deformation and (**b**) Tensile deformation.  
Figure 7: Tensile deformations of DL-LET and LET joints obtained by FEA.  
Figure 10: Testing Machine.  
Figure 9: Prototypes of a DL-LET joint and a LET joint.
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 5 Conclusions  
We developed an object-based benthic mapping approach based on high spatial resolution Planet Dove satellite imagery. This approach combines bottom reflectance retrieval, bathymetry estimation, and object-based classification using Dove images. Our approach was successfully applied at three study sites in the southeastern Dominican Republic. The combined inputs of surface reflectance, bottom reflectance, and bathymetry helped to accurately identify multiple benthic habitat classes with relatively similar surface reflectance values. Our study found that bathymetry estimation and benthic habitat classification are affected by the poor image quality in the NIR band over ocean regions where the signal is low. The NIR band was applied to remove the water surface effects, and poor quality NIR band values led to the abnormal values in the visible bands. Moreover, the low signal-to-noise ratio of CubeSat imagery also affected the accuracy of bathymetry estimation. In future studies, we will explore a solution to reduce the NIR band effect. Finally, the dense cloud coverage, high turbidity waters, breaking waves all led to the challenge of tropical benthic composition mapping.  
Conceptualization, J.L., S.R.S., and G.P.A.; methodology, J.L. and D.E.K.; software, J.L., S.R.S., and D.E.K.; validation, S.R.S.; formal analysis, J.L., S.R.S., and G.P.A.; investigation, J.L., S.R.S., and G.P.A.; resources, S.R.S. and G.P.A.; data curation, J.L. and D.E.K.; writing--original draft preparation, J.L., S.R.S., and G.P.A.; writing--review and editing, J.L., S.R.S., D.E.K., and G.P.A.; visualization, J.L.; supervision, G.P.A.; project administration, G.P.A.; funding acquisition, G.P.A.  
This research was funded by The Nature Conservancy and the Avatar Alliance Foundation.  
The authors thank The Nature Conservancy Caribbean Division, [PERSON] from the University of Southern Mississippi, Grupo Punta Cana, and Fundacion Dominicana de Estudios Marinos (FUNDEMAR) for field data collection support. All authors appreciate the reviewers' constructive suggestions.  
The authors declare no conflicts of interest.
Figure A1. (a) Satellite derived bathymetry validation at Catalina Island, Dominican Republic. (b) Satellite derived bathymetry validation at east Dominican Republic. (c) Satellite derived bathymetry validation at Saona Island, Dominican Republic.
# Control of a Variable Blade Pitch Wind Turbine Subject to Gust Wind and Actuators Saturation
## 3 Results  
### Fuzzy Model Validation  
The performance of the fuzzy controller depends on the accuracy of the fuzzy model with in return depends on the number of fuzzy rules used to approximate the nonlinear dynamic model. On the other hand, many rules will increase the computational cost. Fuzzy systems modeling has been verified to be universal approximators [45]. Nonetheless, for the approximation to be accurate, a large number of fuzzy rules is needed, in particular when the input membership functions are defined on a space with high dimensions. In general, a large rule number may lead to inefficiency and difficulty in system implementation. A fuzzy system with many rules may be hard to design and have high computation complexity, and poor convergency in parameter tuning [46].  
A numerical simulation of the nonlinear model represented by Equation (6) was conducted and compared to validate the (T-S) fuzzy model described by Equation (12) using a set of 2 rules for each premise variable and a constant control input \(u=[0\ 0.1]^{T}\) and initial conditions \(x_{0}=[0\ 0\ 0.8\ 0.8\ 0]^{T}\). The membership functions of the rules \(1,2\) are illustrated in Figure 3 and the physical parameters of the wind turbine are shown in Table 1.  
The results of the simulation of the nonlinear and the fuzzy model shows that with a set of eight rules, the (T-S) fuzzy model present an adequate approximation, however the result can be improved further by adding more rules. The plots of the wind speed, blade pitch, the angular displacement, and the angular velocity of the rotor and the generator are presented in Figures 4-6.  
Figure 3: Fuzzy membership function.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Symbole** & **Values** \\ \hline Rotor radius & R & 21.65 m \\ Rotor inertia & \(J_{r}\) & 34.4 kg\(\cdot\)m\({}^{2}\) \\ Generator inertia & \(J_{g}\) & 34.4 kg\(\cdot\)m\({}^{2}\) \\ Shaft damping coeff & \(B_{Is}\) & 9500 N\(\cdot\)m/rad\(\cdot\)s\({}^{-1}\) \\ Shaft stiffness coeff & \(K_{Is}\) & 2.691\(\times\)10\({}^{5}\) N\(\cdot\)m/rad\(\cdot\)s\({}^{-1}\) \\ Rotor friction coeff & \(B_{r}\) & 27.36 N\(\cdot\)m/rad\(\cdot\)s\({}^{-1}\) \\ Generator friction coeff & \(B_{g}\) & 0.2 N\(\cdot\)m/rad\(\cdot\)s\({}^{-1}\) \\ Gearbox ratio & \(n_{g}\) & 43.165 \\ Air density & \(\rho\) & 1.225 kg/m\({}^{3}\) \\ time delay & \(\tau\) & 0.1 s \\ torque coeffs & \(c_{1}\ldots c_{6}\) & 0.5176, 116, 0.4, 5, 21, 0.0068 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the wind turbine.  
Figure 4: Wind speed and blade pitch angle.  
Figure 5: Angular displacement.
## 4 Results and Discussion
### Data Analysis
#### 4.2.5 Soil Moisture  
As can be seen in Figure 16, the average values of soil moisture data were observed to be 25.8%, 26.6%, 27.1%, and 27.0% for the respective months. The average soil moisture exhibited an increasing trend from 25.8% in June to 27.1% in August and then remained stable at 27.0% in September. The minimum soil moisture levels were observed to be 24.5%, 24.8%, 25.0%, and 19.2% for the respective months, with September recording the lowest minimum soil moisture. Conversely, the maximum soil moisture levels were observed to be 27.0%, 28.9%, 30.5%, and 32.1%, respectively, with September recording the highest maximum soil moisture.  
Figure 16: Soil Moisture data.  
Figure 14: CO\({}_{2}\) data.  
Figure 15: Soil Temperature data.
# Beyond Manual Media Coding: Evaluating Large Language Models and Agents for News Content Analysis
## 3 Materials and Methods
### Evaluation Metrics
To assess the LLMs' tagging performance against human-annotated ground truth, we designed an evaluation process using the unified metric of the weighted F1-score across all question types (binary, categorical, and multi-label) to ensure consistency and comparability despite their differing structures. Rather than relying on accuracy for binary questions, weighted F1 was chosen to better handle class imbalances. The same weighted F1 metric was applied to categorical questions to evaluate how well the model distinguishes mutually exclusive labels and is adapted for multi-label questions by treating each label as a separate binary classification, aggregating performance accordingly. Precision measures the correctness of positive predictions, while recall gauges the model's ability to capture all relevant instances. Both metrics are derived from the counts of True Positives (\(TP\)), False Positives (\(FP\)), and False Negatives (\(FN\)), as presented by their respective mathematical equations in Equation (1) below:  
\[\text{Precision}=\frac{TP}{TP+FP}\qquad\text{Recall}=\frac{TP}{TP+FN} \tag{1}\]  
The F1-score presented in Equation (2) harmonizes these metrics to provide a balanced performance measure:  
\[\text{F1-score}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{2}\]  
By standardizing on weighted F1, shown in Equation (3), our evaluation framework ensured consistent, interpretable, and unbiased comparisons across all question formats, accounting fairly for variations in answer distribution and question complexity. \[\text{Weighted F1-score}=\sum_{c\in\mathbb{C}}\frac{n_{c}}{N}\cdot F_{1}^{( c)} \tag{3}\]  
where  
* \(c\): Each of the binary, categorical, and multi-label classes. * \(C\): Set of all classes. * \(F_{1}^{(c)}\): F1-score for class \(c\). * \(n_{c}\): Number of true instances (support) for class \(c\). * \(N=\sum_{c\in\mathbb{C}}n_{c}\): Total number of instances across all classes.
## 3 Hardware Implementation
### Hybrid ECG Classifier  
Conventional classification systems often employ an always-on architecture, which consumes significant time and energy processing events that are not of interest, especially during long-term biosignal monitoring scenarios. Nevertheless, biosignals exhibit sparsity, allowing a multi-stage processing paradigm to notably decrease the frequency of activating high-precision processing systems, thereby reducing power consumption during system standby.
## 5 Experiments and Discussion
### Verification and Results Discussion
Specific experimental results of the MATLAB/Simulink-CarSim co-simulation, including the maximum lateral acceleration and maximum lateral error, are summarized in Table 2. We can make a comparative analysis of the three methods participating in the comparative experiment from the vertical and horizontal dimensions in Table 2. The two variables listed are very relevant to the process of lane change. The maximum lateral acceleration and maximum lateral error can reflect the driving comfort and the accuracy of trajectory tracking during the process of lane-changing, respectively. All test data of the proposed method are shown in bold in Table 2. As can be seen from Table 2, when the vehicle with the lane-changing controller proposed in this paper tracks the corresponding trajectory under different scenarios, the lateral acceleration changes stably without sharp points, and meets the requirements of the vehicle when performing lane-changing actions. Taking the maximum lateral acceleration as an example, the maximum values of the proposed method in the two scenarios are 1.269 and 1.272, respectively. Compared with SMC and TSMC, the new method achieves significant improvement of 58.6% and 34.1% in scenario A, respectively. Furthermore, in scenario B, the new method achieves a significant improvement of 58.5% and 35.3% over SMC and TSMC, respectively. A similar effect can be seen from the maximum lateral error. In scenario A and scenario B, the new method achieves results of 0.118 and 0.137, respectively, which is significantly better than the other two representative methods. Compared with SMC and TSMC, the new method achieves significant improvement of 58.0% and 53.2% in scenario A, respectively. In scenario B, the new method achieves a significant improvement of 63.7% and 58.6%. Regardless of the scenario, the vehicle using the proposed method can adjust its state in a short amount of time, and the lateral error is always kept within the smallest range, demonstrating that the vehicle has good handling stability for the lane change trajectory described in the paper. In conclusion, compared with SMC, the proposed method has a performance improvement of at least 58.0% and a performance improvement of at least 34.1% compared with TSMC. In conclusion, the results of the above two experimental scenarios show that the lane changing controller designed by the method proposed in this paper achieves ideal effects and is competent for complex lane changing scenarios. The results show that the designed lane change control method meets the requirements of lane change maneuver and has pretty good handling and stability, which verifies the effectiveness of the lane change control strategy. Ensure that the vehicle can meet the passenger's requirements for speed and comfort in the actual lane change.
## 4 Proposed Algorithm
### PSO for Choosing Routing Paths
A well-known fact is that the nodes near the sink node are prone to consume more energy than the nodes far away. Those nodes burden heavier transmission tasks and are easier to convey their data to the sink repeatedly because they are more likely to be relay nodes. So to obtain a balanced network, we introduce a factor \(E_{b}\) to  
Figure 6: An example of hierarchical priorities initialization for part \(b\) and its corresponding topological routing path in the cross-section view. describe a energy consumption ratio of different layer. \(E_{b}=E_{1}/E_{4}+E_{2}/E_{3}\), where \(E_{i}\) represents the remaining energy of the \(i\)- th layer. \(E_{b}\) should be as large as possible as the lower layer tends to remain more energy in a balanced network than in an uneven network. In a conclusion, the final fitness function consists of two parts as below,  
\[f_{2}=W_{1}B_{r}+W_{2}E_{s} \tag{25}\]  
where \(W_{i}\) is a weighting factor and \(W_{1}+W_{2}=1,W_{i}\geq 0,i\in\{1,2\}\). In the simulation, \(W_{1}\) is chosen randomly from the range \([0.4,0.6]\) while \(W_{2}=1-W_{1}\).
# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 4 Discussion
Furthermore, the number of image processing tools and classification algorithms is limited to what is available in the API. The differences observed between the products compared in Figures 6 and 7 can be explained in several ways. One of the main reasons lies in the temporal variations of image data, with each method using images acquired at different times. This temporal difference influences the characterisation of floods, with the processing chain detecting floods at a specific time, while Copernicus EMS relies on older data, which impacts the results. Furthermore, the specific mapping methods and algorithms of each method are major sources of divergence. These divergences highlight the need for contextual analysis for accurate interpretation. The temporal complementarity between the approach focused on speed and extensive spatial coverage and official products, such as those from Copernicus, allows for a better understanding of flood evolution. The approach developed stands out for its responsiveness and capacity to map the entire territory of Madagascar, whether for research purposes or operational applications in times of emergencies. This responsiveness holds considerable potential for improving flood response and disaster management. The contribution of our approach to flood management relies on the complementarity among various approaches, including those of Copernicus and the International Charter Space and Major Disasters. Sentinel-1 images are used for their ability to provide valuable information, even in unfavourable weather conditions. Our approach stands out for its speed, providing near-real-time data complementary to Copernicus products. The involvement of local stakeholders in data production and risk management enhances the capacity of communities to respond appropriately to floods. This collaboration contributes to more efficient flood management and rapid response in times of disaster. Furthermore, the utilisation of Google Colab significantly eases collaboration. The processing chain is easily shareable, requiring no installation, and the Python notebook contains both code and formatted text, facilitating comprehension. This seamless integration of collaborative tools enhances the accessibility of our approach, promoting wider engagement and cooperation among stakeholders in addressing flood-related challenges.
# User Association Performance Trade-Offs in Integrated RF/mmWave/THz Communications
## 5 Algorithm Description
* Monitor the network over time:  
- Track the SINR and throughput per user. - Update the blockage models and user profiles (categories) periodically. * Balance the BS loads if needed:  
- Transfer users from overloaded to underloaded BSs. * Repeat periodic reassociations:  
- Recompute the network state.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 4 Performance Evaluation and Discussion: Example of GEO SAR Design
### Impact of Bistatic Scattering RFI
#### 4.2.1 LEO SAR Bistatic Scattering RFI Case Analysis
To simplify the analysis, different satellites are assumed having the same illuminating geometry for GEO SAR. Under the non-specular reflection case, the average transmitted power requirements in all three frequencies of GEO SAR are very low. Even for an L band GEO SAR, the required average transmitted power is only 0.7 kW. However, although the probability of the specular scattering case is very small, very large average transmitted power requirements are needed for GEO SAR to reach the designed SINR regardless of the working frequencies when the specular scattering case happened. Especially for the L band system, average transmitted power requirement even turns to 1.1 \(\times\) 10\({}^{9}\) kW, which is impossible to be achieved. Deriving from the higher gain, the stronger backscatter and lower occurrence probability of the specular scattering case, a high frequency GEO SAR system will be more resistant to LEO SAR bistatic scattering RFI impact. Moreover, the results also depend on the distance between the LEO SAR beam centre and the GEO SAR beam centre. When the distance increases, the average transmitted power requirements will decrease. When the distance increases to about 5000 km, the average transmitted power requirement turns to about 2.4 \(\times\) 10\({}^{4}\) kW rather than 1.1 \(\times\) 10\({}^{9}\) kW for an L band GEO SAR. In the non-specular scattering case, as the RFI signal is very weak, the distance has the negligible impact on the design requirement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameters** & **L** & **C** & **X** \\ \hline \multirow{4}{*}{Occurrence probability (\%)} & \multicolumn{2}{c}{**Non-specular scattering**} & 44.8 & 38.1 & 39.0 \\ \cline{2-5}  & \multicolumn{2}{c}{**Specular scattering**} & \(5.0\times 10^{-2}\) & \(1.5\times 10^{-2}\) & \(2.0\times 10^{-3}\) \\ \cline{2-5}  & \multicolumn{2}{c}{**Mainlobe to mainlobe specular scattering**} & 0 & 0 & 0 \\ \hline \multirow{4}{*}{Recquired average transmitted power (\%)} & \multirow{4}{*}{**Non-specular scattering**} & \(-14.8\) & \(-9.7\) & \(-7.6\) \\ \cline{3-5}  & & 0 & 0.7 & \(6.0\times 10^{-2}\) & \(2.0\times 10^{-2}\) \\ \cline{2-5}  & \multicolumn{2}{c}{5000 km} & 0.7 & \(5.2\times 10^{-2}\) & \(1.8\times 10^{-2}\) \\ \cline{2-5}  & \multicolumn{2}{c}{0} & \(1.1\times 10^{9}\) & \(3.2\times 10^{7}\) & \(4.7\times 10^{5}\) \\ \cline{2-5}  & \multicolumn{2}{c}{5000 km} & \(2.4\times 10^{4}\) & 7.8 & \(3.7\times 10^{-2}\) \\ \cline{2-5}  & \multicolumn{2}{c}{**Mainlobe to mainlobe specular scattering**} & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Occurrence probability of the LEO SAR bistatic scattering RFI and the average transmitted power requirement in GEO SAR (HH polarization, Shrub scene, in the specular scattering case: \(\theta_{1}=\theta_{2}=30^{\circ}\), in the non-specular case: \(\theta_{1}=30^{\circ}\), \(\theta_{2}=45^{\circ}\)).
## 2 Materials and Methods
### Methods
#### 2.2.4 Fourier Transform Infrared Spectroscopy (FTIR) Test  
The FTIR spectroscopy test helps to characterize changes in functional groups and chemical alteration of base asphalt due to engine oil addition. This test was performed using JASCO 4200 spectrometer (TS Science Co., Ltd., Seoul, Korea) with Attenuated Total Reflection (ATR) accessory in the range of 4000 to 650 cm\({}^{-1}\) wavenumbers. ATR provides reliable and repeatable spectra compared to transmission configuration which is prone to erroneous readings with sample preparation and film thickness variations. Infrared radiations are bombarded on a thin sample of asphalt binder, directly placed on the ATR crystal. This excites some of the molecular bonds within the asphalt molecule according to their natural vibration frequency. Some part of the incident infrared rays is absorbed due to the bond excitations. These molecular vibrations/excitations are then detected by a detector in FTIR. Detector measures the percentage of the transmitted or absorbed radiation to the source radiation and a spectrum is processed and displayed by a computer program [33], imitating the functional groups present in the material. For each spectrum obtained, a total of 36 consecutive scans at a resolution of 4 cm\({}^{-1}\) were executed and averaged.  
Some characteristic functional groups, with their respective wavenumber ranges that can be detected with FTIR, are presented in Table 5. These functional groups can also be present in asphalt molecules which helps us to understand the basic chemistry of base, aged and rejuvenated asphalts.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sample** & **Code** \\ \hline Base Binder 1 & BB \\ Asphalt + 2.5\% Fresh Engine Oil & FR \\ Asphalt + 2.5\% Un-Filtered Waste Engine Oil & UFO \\ Asphalt + 2.5\% Filtered Waste Engine Oil 2 & FO \\ \hline \hline \end{tabular} 1 Base Binder was sheared with same mixing criteria (2000 rpm, 30 min, at 150 \({}^{\circ}\)C); 2 Filtration done by WhatmanTM filter paper.  
\end{table}
Table 4: Experimental samples and respective codes.