esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Perception Technology
The environmental perception sensors installed on a single unmanned tugboat have varying performance and can create shaded areas depending on the environment during the mission. If a sensor fails within the swarm of tugboats, it can cause disruptions in mission execution in both berthing and towing modes. Therefore, it is crucial to have situation awareness technology based on the integration and fusion of perception sensors such as cameras, LiDAR, and radar sensors. Using the sensor fusion technique, if one sensor fails, the information from the other sensors can be integrated and fused to accurately perceive the environment, ensuring no disruption in mission execution during approach or navigation. Ref. [42] reviewed the state of the art of sensor fusion technology, regulations, and performance indices related to autonomous vessels and translated them into operational requirements. After that, a sensor fusion technology was suggested by adopting artificial intelligence technology. In [43], a new concept called ISAS (Intelligent Situational Awareness System) was introduced to detect maritime objects by fusing information from multiple sensors and tracking object motion. A deep learning algorithm was utilized to detect objects, while PDA ((Probabilistic data association) and JPDA (Joint Probabilistic Data Association) were used to associate data in the tracking process. The ISAS environmental awareness sensor is composed of a camera, LiDAR, and radar. In order to improve situational awareness, the Mask R-CNN (Region-based Convolutional Neural Network) algorithm, an instance segmentation method, was applied to detect overlapping objects on the sea when using camera-based objects. Moreover, a lightweight module-based image noise reduction model was designed and trained using radar images captured in the ocean environment to eliminate noise during radar-based object detection. Figure 11 illustrates the noise reduction outcome for the radar image. The study conducted sensor fusion of camera and radar data. Figure 10: Overview of the system design of the AVMS proposed by [PERSON] et al. [41].
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## Appendix A  
Figure 11a shows a map of pre-seismic coherence (image pair 5 April 2015, 17 April 2015) for the region of Jharlang. An example of a slope along which pixels have been distorted is indicated, where the pixels are visibly elongate and the image appears striped. In order to identify these distorted pixels, we calculated the geographic area which contributed to each pixel in radar geometry. Across our study area, this pixel contributing area ranged between 0 and 19,000 m\({}^{2}\). In contrast, each multilooked pixel in geographic coordinates is around 400 m\({}^{2}\) in size. We masked pixels with a contributing area above a given threshold. This threshold was incrementally lowered from 5000 m\({}^{2}\) to 2000 m\({}^{2}\) to 1000 m\({}^{2}\) (Figure 11b-d respectively). With the threshold set at 1000 m\({}^{2}\), visibly distorted pixels were removed from the image (Figure 11d). We therefore did not lower the threshold further, as this would have removed data without justification.
# Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming
## 3 Experiment and Results
The operating points of experiments 1-8 were selected to fill the design space by maximizing the minimum distance [43] between the operating points, i.e., \(d^{*}=\underset{D}{\text{maxmin}}\left\|p_{i}-p_{j}\right\|\), where \(p_{i}\in[0,\ 1]\times\{0,0.5,1\}\) was the scaled design space, as the payload mass could only take on the discrete values of \(\{10,30,50\}\) kg while the rope length could vary continuously in the interval \([0.8,2.0]\) m. The operating point for each experiment is given in Table 1. The input signal u was a sequence of step functions with varying amplitudes in order to excite the underactuated part of the overhead crane, as shown in Figure 9. The data used for identification were normalized by dividing the input sequence by 10 and the overhead crane velocity by 30. The grammar used for representing individuals consisted of a terminal set, which included lagged input and output variables, the mass of the payload \(m\), the rope length \(l\), and several integers, while rational and irrational constants could be obtained by the division and square root operator, respectively. The mass of the payload \(m\) and the rope length \(l\) were assumed as the measurable parameters. The grammar constructed to derive the velocity and sway model is presented in Tables 2 and 3, respectively, using the Backus-Naur form (BNF) [33], while the G3P-SR hyperparameters used to obtain both models are given in Table 4. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Operating Point** & **Exp 1** & **Exp 2** & **Exp 3** & **Exp 4** & **Exp 5** & **Exp 6** & **Exp 7** & **Exp 8** & **Exp 9** & **Exp 10** \\ \hline \(l\) [m] & 0.8 & 1.4 & 2.0 & 2.0 & 1.7 & 1.4 & 0.8 & 1.1 & 1.7 & 1.1 \\ \(m\) [kg] & 10 & 10 & 50 & 10 & 30 & 50 & 50 & 30 & 10 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operating points of experimental data used in identification. Figure 8: Overhead crane dynamic model composed of two submodels, denoted as velocity and sway models. Figure 9: Control input to the PLC for (**a**) experiment 9 and (**b**) experiment 10. The comparative study was divided into two parts. In the first part, we discussed the choice of the \(\lambda\) hyperparameter value and compared the performance of the proposed approach with MGGP. The second part provided a comparison of the G3P-SR method with the crane dynamic models developed using the most commonly used approaches in the crane identification literature--ANN and TSF.
## 4 Performance and Simulation of 2D Maximum Energy Search Approach
### Selection of Processing Parameters  
Among all of the processing parameters, the coherent integration time \(\Delta T\) and the full incoherent integration time \(T\) play critical roles for the carrier detection performance. According to the recommendation from [PERSON], et al. [8], a rule of thumb for FFT resolution is that the maximum frequency change during the coherent interval in which the FFT is computed shall be smaller than a quarter of the FFT resolution, which leads to Equation (20):  
\[\Delta F>\sqrt{4\hat{f}_{\max}}, \tag{20}\]  
The value of the incoherent integration time is decided according to the frequency deviation due to the second order Doppler rate not exceeding the length of the frequency cell, which could be written as Equation (21):  
\[\frac{1}{2}\hat{f}_{\max}T^{2}<\Delta F\Leftrightarrow T<\sqrt{2\frac{\Delta F }{\hat{f}_{\max}}}\,, \tag{21}\]  
While the constraint for the incoherent integration time is obvious, the constraint for coherent integration time needs more investigation. According to the analysis by [PERSON], et al., [21], the residual frequency dynamic would result in performance degradation in coherent summation. The influence of residual Doppler dynamics on energy loss in the coherent summation could be computed by the following coherence loss function [12]:  
\[G(\Delta\mathbf{f})=20\times log_{10}\bigg{\{}\frac{1}{N_{FFT}}\sum_{i=-\frac{N_{ FFT}}{2}}^{\frac{N_{FFT}}{2}-1}\exp\left[-2\pi j(\Delta\mathbf{f})\right] \bigg{\}}, \tag{22}\]  
\[\Delta\mathbf{f}=2\pi\bigg{(}\Delta ft_{i}+\frac{1}{2}\Delta\dot{f}t_{i}^{2}+ \frac{1}{6}\Delta\ddot{f}t_{i}^{2}\bigg{)}, \tag{23}\]  
where \(\Delta f\), \(\Delta\dot{f}\), and \(\Delta\ddot{f}\) are the residual Doppler frequency, residual Doppler rate, and residual second-order Doppler rate, respectively.  
A numerical computation is made on Equation (23) to evaluate the influence of the residual Doppler rate. The complex sampling frequency is set to 200 kHz to cover the total Doppler frequency variation. The FFT resolutions are 10 Hz and 100 Hz, respectively. The energy loss due to the residual Doppler rate is plotted in Figure 7.  
As the coherent integration time increases from 0.01 to 0.1 s, the energy loss due to the 500 Hz/s residual Doppler rate increases from 0.0005 dB to about 6 dB, which is very significant. So, we take Equation (20) as the criteria to determine the FFT resolution.
# Discerning Reality through Haze: An Image Dehazing Network Based on Multi-Feature Fusion
## 4 Experiment and Result Analysis
### Ablation Analysis  
To further demonstrate the advantages of MFID-Net, a detailed ablation study was conducted in this section, where the various major components of the network were separately analyzed. These components include LGCA and GPA. To ensure the fairness of the experimental results, only one network component was added or modified in each ablation experiment.  
To verify the effectiveness of LGCT in image dehazing tasks, we replaced the SE-Net channel attention mechanism used in FFA-Net with LGCT. In Table 4 and Figure 12, the results show that the model using LGCT performs better in image dehazing tasks, not only improving the values of SSIM and PSNR evaluation indicators but also reducing the number of network parameters. In Figure 12, it is evident that LGCT has successfully reduced the phenomena of halo and dark block generation when recovering large-area color blocks in images.  
Furthermore, an ablation study was conducted on the global information embedding layer in LGCT, examining the effects under three different scenarios: using \(\ell_{1}\)-Norm, \(\ell_{2}\)-Norm, and \(\ell_{1}+\ell_{2}\)-Norm, respectively. The experimental results indicate that when \(\ell_{1}+\ell_{2}\)-Norm is employed to aggregate global contextual information, the model performs best in image dehazing tasks. The related experimental results are shown in Table 5 and Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{SENet} & & & & & \\ LGCA & & & & & & \\ PA & & & & & & \\ GPA & & & & & & \\ \hline SSIM & 36.13 & 37.21 & 36.58 & **37.42** \\ PSNR & 0.9842 & 0.9852 & 0.9845 & **0.9890** \\ Parameter(MB) & 14.20 & **14.03** & 14.20 & 14.07 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons on SOTS indoor testset for different configurations.  
Figure 12: Comparison chart of results from ablation experiments on different module combinations conducted on IST.  
These experimental results further substantiate that MFID-Net is capable of achieving an SSIM of 37.42 and a PSNR of 0.9890, significantly higher than other methods. Simultaneously, it proves that each component involved in MFID-Net has played an essential role.
## 3 Results  
We developed and trained the ML model to estimate the wavelength-resolved SSI spectrum using only a minimal set of inputs: the spectral responses from an ensemble of low-cost sensors, remotely sensed SR, and easily computable solar geometry parameters, namely, the solar zenith and azimuth angles. The model's performance was then evaluated on the previously unseen testing set that was held out during training to assess its predictive accuracy and generalization capability. In this section, we present the results.  
The scatter plots of estimated vs. true irradiance (i.e., irradiance values observed from the reference instrument) in Figure 5 depict the predictive performance of the model on the training (green) and independent testing (blue) sets across the entire spectrum from 360 nm to 780 nm. The model achieved a co-efficient of determination (\(R^{2}\)) of 0.999 on the  
Figure 4: The geographical route of a data collection trip taken on 2025-05-24. The red pointers indicate the route taken.  
training set and an \(R^{2}\) of 0.997 on the testing set with a significant majority of data points concentrated closer to the 1:1 line. This reflects strong predictive performance, with the high \(R^{2}\) value on the testing set indicating a good level of generalization to unseen data.  
We also evaluated the performance across individual spectral bins. The \(R^{2}\) values for the performance of the model on the training and testing sets in each individual wavelength bin are given in Figure 6. The model yielded \(R^{2}\approx 0.99\) across all 421 wavelength bins on the testing set.  
Figure 7 depicts the mean squared error (MSE) for each bin. The MSE across the wavelengths seems to vary in proportion to the magnitude of the values, i.e., the scale of irradiance values within that bin, roughly following the trends in the irradiance variation across the wavelength region. For example, the shortwave bins, particularly in the UV range, are significantly attenuated in the upper atmosphere before reaching the surface, resulting in lower irradiance values in those bins compared to those in the visible region. Therefore, consequently, the MSEs corresponding to these regions are lower in value. In order to gain more context, we further evaluated the relative MSE for each bin. The relative MSE contextualizes the prediction error in relation to the reference scale of the  
Figure 5: Scatter diagram comparing the SSI measurements from the reference instrument on the x-axis against the SSI estimates from the ML model on the y-axis for the whole spectrum from 360 nm to 780 nm.  
Figure 6: \(R^{2}\) values on each of the 421 spectral bins evaluated on the training and testing sets.  
target variable. Figure 8 gives the relative MSEs evaluated on the testing set across the 421 wavelength bins. The relative MSE remains consistently and significantly below unity across all wavelength bins, indicating that the model's predictive errors are reasonably small in magnitude relative to the corresponding spectral solar irradiance values in each bin. This reflects reliable performance across the entire spectral range.
## 3 Results
### Quantitative Synthesis
#### 3.5.2 Effect of VRBR on Knee Function  
Seven studies [61; 62; 63; 64; 65; 66; 67] provided data from 330 participants to assess the effect of VRBR in reducing knee disability and increasing knee and lower extremity function. At immediate assessment [61; 62; 64; 65; 67], our findings showed a medium effect (SMD = \(-\)0.51; 95% CI \(-\)0.75 to \(-\)0.27; \(p\) < 0.001) favoring VRBR, compared to the control interventions, in reducing knee disability (Table 4, Figure 3). No risk of publication bias (Egger \(p\) = 0.91; adj SMD = \(-\)0.51) and heterogeneity (I\({}^{2}\) = 0%; Q = 3.9; df = 4; \(p\) = 0.42) were present. Finally, sensitivity analysis did not detect important variations regarding the original effect size.  
According to the follow-up assessment, our meta-analysis showed that the effect of VRBR followed being major that control interventions at 3 [66; 67] (SMD = \(-\)0.78; 95% CI \(-\)1.11 to \(-\)0.44; \(p\) < 0.001) and 6 months [63; 66] (SMD = \(-\)0.65; 95% CI \(-\)1 to \(-\)0.28; \(p\) < 0.001) (Table 4). Our findings also reported a similar effect when VRBR was used in complement with CT [61; 67] (SMD = \(-\)0.52; 95% CI \(-\)1.03 to \(-\)0.01; \(p\) = 0.046) or isolated [64; 65] (SMD = \(-\)0.64; 95% CI \(-\)0.99 to \(-\)0.27; \(p\) = 0.001), both in comparison to CT (Table 4, Figure S2). Finally, we found that immersive [61; 67] (SMD = \(-\)0.51; 95% CI \(-\)0.78 to \(-\)0.23; \(p\) < 0.001) and non-immersive VR devices [62; 64; 65] (SMD = \(-\)0.52; 95% CI \(-\)1.03 to \(-\)0.1; \(p\) = 0.046) are similarly effective (Table 4, Figure S3).  
Figure 2: Forest plot of the immediate effect of VRBR on knee pain [58; 63; 64; 65; 66; 67].
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods  
### Biomedical Devices  
The Medical Internet of Things (Medical IoT) represents a groundbreaking integration of medical devices with individuals, utilizing wireless communication to enable the exchange of healthcare data, facilitate remote patient monitoring, and significantly improve the quality of life for patients [4]. This innovation not only promises to enrich lives but also aims to enhance the quality of care and introduce cost efficiencies within healthcare institutions. The implementation of the Medical IoT in the healthcare sector is rapidly expanding on a global scale, though it encounters a variety of obstacles that need to be addressed to widen its application. These challenges include, but are not limited to, scalability, mobility, cost, complexity, management, trust, security, and interoperability.  
Medical IoT applications can range from simple biological metrics, such as blood pressure [6] or temperature [16], to more complex measurements like the concentrations of certain substances [17]. The devices responsible for this monitoring need to be sufficiently small to be implantable [6], ingestible in the form of a capsule [18], or wearable [16]. Beyond the real-time wireless monitoring of various physiological parameters, a variety of emerging applications have been highlighted in recent studies. These include:  
* The management/treatment of brain disorders [19] through brain-computer interfaces (BCIs) that enable direct communication between the brain and an external device [20; 21];
* The Tongue Drive System (TDS), which allows for control of devices through tongue movements [22];
* Leadless pacemakers that provide cardiac rhythm management without the need for wired leads [23];
* Neurostimulators that deliver electrical stimulation to targeted areas of the brain or nervous system [24];* Techniques for imaging blood vessels to support diagnosis and treatment planning [25];
* Drug delivery systems designed to release medications in a controlled manner at targeted locations within the body [26].  
These advancements illustrate the broad and evolving scope of the Medical IoT in enhancing healthcare delivery and patient care.
# TrustShare: Secure and Trusted Blockchain Framework for Threat Intelligence Sharing
## 3 Materials and Methods  
In this section, we propose an innovative framework for secure and efficient threat intelligence sharing, leveraging advanced technologies such as Structured Threat Information eXpression (STIX), Trusted Automated Exchange of Intelligence Information (TAXII), Ciphertext-Policy Attribute-Based Encryption (CP-ABE), the Interplanetary File System (IPFS), the MITRE ATT&CK framework, the GDPR, and the right to be forgotten (RtbF). The methodology integrates these technologies to address the current challenges in cybersecurity, ensuring real-time, trusted, and collaborative information exchange among organizations. The following sections detail the materials used and the step-by-step methodology followed to develop and validate this framework.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Framework** & \begin{tabular}{c} **Access Control** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Privacy** \\ **Compliance** \\ \end{tabular} & **Trust Model** & \begin{tabular}{c} **Enforcement** \\ **Mechanism** \\ \end{tabular} & \begin{tabular}{c} **Unique Innovation/** \\ **Comments** \\ \end{tabular} \\ \hline \begin{tabular}{c} Existing \\ Systems \\ \end{tabular} & \begin{tabular}{c} Role-based \\ or None \\ \end{tabular} & Limited or None & Binary/Static & Off-chain or Manual & \begin{tabular}{c} Limited fine-grained \\ control and weak privacy \\ enforcement \\ \end{tabular} \\ \hline \begin{tabular}{c} TrustShare \\ (This Work) \\ \end{tabular} & \begin{tabular}{c} CP-ABE \\ (Attribute-Based) \\ \end{tabular} & \begin{tabular}{c} GDPR-aligned, \\ Auditable \\ \end{tabular} & \begin{tabular}{c} Hybrid Dynamic \\ Trust Scores \\ \end{tabular} & \
\begin{tabular}{c} Smart Contract- \\ Automated \\ \end{tabular} &
\begin{tabular}{c} Fine-grained encryption \\ policies, trust-aware \\ access, automated \\ compliance enforcement \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary comparison of TrustShare with existing CTI sharing frameworks.
## 3 Results and Discussion
### Response Analysis and Interpretation by Box-Behnken Design (BBD)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **Sum of Squares** & **Mean Square** & **F Value** & **\(p\)-Value Prob \textgreater{} F** \\ \hline Model & \(5.009\times 10^{-3}\) & \(5.566\times 10^{-4}\) & \(8.34\) & \(0.0053\) \\ \(X_{1}\) & \(1.830\times 10^{-5}\) & \(1.830\times 10^{-5}\) & \(0.27\) & \(0.6166\) \\ \(X_{2}\) & \(3.011\times 10^{-3}\) & \(3.011\times 10^{-3}\) & \(45.13\) & \(0.0003\) \\ \(X_{3}\) & \(1.001\times 10^{-3}\) & \(1.001\times 10^{-3}\) & \(15.01\) & \(0.0061\) \\ \(X_{1}X_{2}\) & \(1.332\times 10^{-5}\) & \(1.332\times 10^{-5}\) & \(0.2\) & \(0.6685\) \\ \(X_{1}X_{3}\) & \(4.096\times 10^{-5}\) & \(4.096\times 10^{-5}\) & \(0.61\) & \(0.4590\) \\ \(X_{2}X_{3}\) & \(1.092\times 10^{-4}\) & \(1.092\times 10^{-4}\) & \(1.64\) & \(0.2416\) \\ \(X_{1}{}^{2}\) & \(1.251\times 10^{-6}\) & \(1.251\times 10^{-6}\) & \(0.019\) & \(0.8590\) \\ \(X_{2}{}^{2}\) & \(1.873\times 10^{-4}\) & \(1.873\times 10^{-4}\) & \(2.81\) & \(0.1377\) \\ \(X_{3}{}^{2}\) & \(5.808\times 10^{-4}\) & \(5.808\times 10^{-4}\) & \(8.71\) & \(0.0214\) \\ Residual & \(4.670\times 10^{-4}\) & \(6.672\times 10^{-5}\) & - & - \\ Lack of Fit & \(4.670\times 10^{-4}\) & \(1.557\times 10^{-4}\) & \(19459.06\) & \textless{}0.0001 \\ Pure Error & \(3.200\times 10^{-8}\) & \(8.000\times 10^{-9}\) & - & - \\ Cor Total & \(5.477\times 10^{-3}\) & - & - & - \\  & \(r^{2}\) = 0.914, Adjust \(r^{2}\) = 0.905 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The analysis of variance (ANOVA) results for the adsorption capacity of lime sand bricks towards Cu(II) (mg/g). Figure 10: The regression plots of the actual data against the predicted values from the response surface models describing.
# Probing Ferroic States in Oxide Thin Films Using Optical Second Harmonic Generation
## 4 Future Perspectives  
### Remaining Challenges in 3D Resolution in Thin Films  
In standard ferroelectrics, 180\({}^{\circ}\) domain walls used to be considered Ising-like due to the strong correlation between strain and polar distortion [123]. The observation of tilted mixed Ising-Neel walls in thin films is suggesting new opportunities at these functional interfaces--one can push the analogy further, to magnetic domain walls, and expect to probe a Bloch behavior at a ferroelectric thin film domain wall. In LiTaO\({}_{3}\) crystals [122], the detection of a polarization component within the plane of the wall by SHG allowed the identification of the Bloch-like nature of 180\({}^{\circ}\) domain walls, as shown in Figure 16a. The observation of such a wall type in thin films is still unreported.  
In bulk materials, imaging techniques are gaining increasing interest with the push towards the internal structure analysis of single domain walls. The full mapping of ferroelectric domain walls can now be performed during their creation at the ferroelectric phase transition and in three dimensions using temperature dependent confocal SHG microscopy [124; 125; 126], as shown in Figure 16b in the case of ferroelectric triglycine sulfate (TGS) [127].  
Regarding thin films, the optical investigation of domain walls remains challenging due to the optical resolution limit and the limited SHG active domain wall volume. Recent progress dealing with laser-scanning SHG microscopy [128] and near-field SHG nano-imaging [129; 130] where tip-enhanced  
Figure 15: Polar behavior of a ferroelectric domain wall. **(a)** SHG imaging a stripe \(a\)-domain pattern in a PZT thin film. A tip-induced switching event is visible on the right side (+V and –V indicate where a positive and negative voltage was applied by the scanning tip, respectively). **(b)** Piezoresponse force microscopy and SHG intensity line scans over the section indicated by the red line in **(a)**. **(c)** High-angle annular dark-field (HAADF) STEM image overlaid with polarization vectors showing a smooth Néel-like rotation combined with an Ising-like polarization decrease across the domain wall. Reproduced with permission from [97], Copyright Wiley-VCH Verlag GmbH and Co. KGaA, 2016.  
SHG signals are collected with a spatial resolution approaching the submicron range [131; 132] has been achieved and opens new avenues towards the symmetry analysis of nanoscale objects.