esa-sceva/satcom-chunk-collection huggingface dataset:
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 4 Discussion  
### Issues of ERA5 Wave Data  
The following reasons can be considered as reasons for the low accuracy of ERA5 wave data. First, the ERA5 wave data was used in coastal areas. The native spatial resolution of ERA5 wave data is 0.36\({}^{\circ}\)[14], which is too large to resolve the wave data in the coastal area. Next, the ERA5 wave data does not take into account the effect of ocean currents [26]. In addition, real-time altimeter data is not as well calibrated compared to data products  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics\(\backslash\)Case** & **(a)** & **(b)** & **(c)** & **(d)** \\ \hline \(\langle H_{g}\rangle\) (m) & 1.687 & 2.044 & 1.990 & 1.652 \\ \(\langle H_{b}\rangle\) (m) & 1.540 & 1.983 & 1.807 & 1.623 \\ \(\langle H_{e}\rangle\) (m) & 1.518 & 1.920 & 1.627 & 1.748 \\ \(r_{c}(H_{g},H_{b})\) & 0.956 & 0.978 & 0.969 & 0.980 \\ \(r_{c}(H_{g},H_{e})\) & 0.827 & 0.907 & 0.965 & 0.812 \\ \(R_{d}(H_{g},H_{b})\) (m) & 0.288 & 0.258 & 0.332 & 0.190 \\ \(R_{d}(H_{g},H_{e})\) (m) & 0.504 & 0.550 & 0.531 & 0.513 \\ \(SI(H_{g},H_{b})\) & 0.147 & 0.123 & 0.139 & 0.114 \\ \(SI(H_{g},H_{e})\) & 0.281 & 0.262 & 0.195 & 0.305 \\ \(R_{a}\) (km) & 9.7 & 8.4 & 10.5 & 7.5 \\ \(N_{c}\) & 60 & 42 & 55 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparisons of GPS wave heights and ERA5 wave heights for various cases in Figure 2c. (a) Case of winds from the sea, which means \(u_{w}>0\) for the buoy C, \(u_{w}<0\) buoys for D, E, F, G, and H, and \(v_{w}>0\) for buoys J, K, P, and Q (Figure 1), where \((u_{w},v_{w})\) are ERA5 wind vectors at the JMA drifting buoy positions. (b) Case except (a). (c) Buoys C, D, E, F, G, and H. (d) Buoys J, K, P, and Q.  
from References [27, 28]. The comparison with high resolution wave data product will be necessary.
## 4 Results and Discussion
### Case Study of Melody Extraction on MedleyDB  
We evaluated the models with a tolerance of one semi-tone, following the standard melody extraction evaluation rule. However, we should note that our proposed model can predict the pitch with a higher resolution (1/16 semi-tone). Figure 6a shows the spectrogram of an audio clip (top) and the corresponding melodic pitch prediction along with the ground truth (bottom). Our proposed model can track nearly continuous pitch curves, preserving natural singing styles such as pitch transition patterns or vibrato.  
While the proposed model achieved improved performance in singing melody extraction, the overall accuracy was still below 90%. We found that errors occurred more frequently in particular cases. Figure 6b,c gives the examples of bad cases where VR and RPA were less than 60%. In both examples, the failures were mainly attributed to voice detection errors.  
In Figure 6b, the harmonic patterns of the vocal melody were not clearly distinguished from background music because the vocal track was relatively softer than the accompanying music track. This weak vocal volume was investigated as a cause of bad singing detection in [46]. Since our melody extraction model was trained in a data-driven way, this could be addressed to some degree by augmenting the training data, for example adjusting the mixing of vocal gains (if they are in separate tracks).  
In Figure 6c, a strong reverberation effect was imposed on the singing voice; thus, the harmonic patterns of the singing voice appeared even after the voice became silent. The algorithm then detected the reverberated tone as vocals and predicted the pitch from it. This case is somewhat controversial because this could be seen as a problem of the ground truth notation. When we excluded these types of heavily-processed audio clips in MedleyDB (\"PortStWillow-StayEven\" and \"MatthewEntwistle-Lontano\"), we observed a significant increase in performance (about 5% in OA on MedleyDB).  
\begin{table}
\begin{tabular}{r c c c c c} \hline \hline \multicolumn{5}{c}{**(b) MIREX05 (Vocal)**} \\ \hline
**Method** & **VR** & **VFA** & **RPA** & **RCA** & **OA** \\ \hline [PERSON] et al. & **93.6** & 42.8 & 76.3 & 77.3 & 69.6 \\ Su & 95.1 & 41.1 & 83.1 & 83.5 & 74.4 \\ Lu \& Su. & 87.3 & 7.9 & 82.2 & 82.9 & 85.8 \\ [PERSON] et al. & 84.9 & 13.3 & 75.4 & 76.6 & 79.5 \\ Proposed & 90.9 & **2.4** & **87.0** & **87.5** & **90.7** \\ \hline \hline \multicolumn{5}{c}{**(c) MedleyDB (Vocal)**} \\ \hline
**Method** & **VR** & **VFA** & **RPA** & **RCA** & **OA** \\ \hline [PERSON] et al. & **88.4** & 48.7 & 72.0 & 74.8 & 66.2 \\ Su & 78.4 & 55.1 & 59.7 & 63.8 & 55.2 \\ Lu \& Su & 77.9 & 22.4 & 68.3 & 70.0 & 70.0 \\ [PERSON] et al. & 73.7 & **13.3** & 65.5 & 68.9 & 79.7 \\ Proposed & 80.4 & 15.6 & **74.8** & **78.2** & **80.5** \\ \hline \hline \multicolumn{5}{c}{**(d) RWC**} \\ \hline
**Method** & **VR** & **VFA** & **RPA** & **RCA** & **OA** \\ \hline Proposed & 92.4 & 5.4 & 85.4 & 86.2 & 90.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
# Numerical Simulation of Anchor Pullout and Shear Tests Using a Regularized Damage Model
## 3 Simulation of Single-Stud Tests
### Discussion on the Fracture Energy in Compression  
The main originality of the proposed model is to include a regularization in compression. It is, thus, worthwhile to examine the impact of fracture energy in the case of a rigid stud [15] and a flexible stud [3]. To study the impact of \(G_{fc}\) in both stud cases, two values of \(G_{fc}\) (an initial one and a lower one of 30,000 N\(/\)m) are considered (Table 3).  
It is important to first compare the significance of the damage due to tension and the damage due to compression. In the case of a rigid stud and for a \(G_{fc}\) of 50,000 N\(/\)m, the tensile damage \(Dt\) from Equation (3) mainly follows the trajectory of the crack cone (Figure 9a), while the compressive damage represents the crushing of the concrete above the stud head (Figure 9b). Tensile damage remains predominant, but compressive damage accounts for 14% of the damaged volume at the end of the calculation, which is not negligible. In the case of a more flexible stud, it is clear that the total damage in Figure 5 is mainly due to tensile damage (Figure 10a), as the compression damage (Figure 10b) remains very limited (only 2% of the damaged volume).  
This difference in behavior can be explained for a flexible stud (Table 4); either the concrete fails in a cone shape or the stud itself breaks, but the elements do not have time to experience compression. On the other hand, with a rigid stud, since the stud cannot deform, concrete can be crushed in compressed areas (near the stud) if the embedment length is long enough. The impact of the concrete fracture energy (\(G_{fc}\)) is influenced  
Figure 8: Results of the experiment and simulation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **E (GPa)** & **f\({}_{\text{c}}\) (Mpa)** & **f\({}_{\text{t}}\) (Mpa)** & **G\({}_{\text{fc}}\) (N/m)** & **G\({}_{\text{ft}}\) (N/m)** \\ \hline Rigid stud & 43 & 40 & 3 & 30,000\(/\)50,000 & 145 \\ \hline Flexible stud & 43 & 50 & 3 & 30,000\(/\)60,000 & 117 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of concrete parameters.  
Figure 9: Damage tension/compression, \(G_{fc}\) = 50,000 N\(/\)m, rigid stud.  
by the material properties of the stud. For studs with lower stiffness and a lower yield strength, compressive damage in the concrete cannot develop before the steel begins to yield, meaning the impact of \(G_{fc}\) is minimal. In this case, \(G_{fc}\) has little influence. For stiffer studs, however, compressive damage in the concrete does develop, making the impact of \(G_{fc}\) more significant.  
The results in Figure 11 show that for the flexible stud, the impact of \(G_{fc}\) is very low, while for the rigid stud, the impact is significant. The proposed methodology, including regularization in both tension and compression, can thus reproduce the experimental evolutions, whatever the stiffness of the stud.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 1 Introduction
In fact, there are few reports in the literature applying the CFD model to unmanned ship-wind and wave coupling. In this paper, an unmanned ship-wind and wave coupling CFD model is established, and the berthing safety of unmanned ship is studied considering factors such as ship speed and direction, wind speed, and the distance between ship and shore. Based on the established coupling model, a multi-factor berthing safety study is carried out on an actual unmanned ship.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 6 Analysis
### Security Analysis
#### 6.2.1 Lemma 1 for Double-Spending Attack  
Double-spending in CrossDeFi occurs when an asset is illicitly spent more than once by the same user. This lemma explores two potential scenarios for a double-spending attack within the CrossDeFi environment.  
**Case 1:** Concurrent Spending on Multiple Users  
Proof.: Consider a scenario within CrossDeFi where a user, [PERSON] from DeFi X, attempts to spend the same asset, Ass, concurrently on two different users, UY in DeFi Y and UZ in DeFi Z. The success probability of double spending is expressed in the following equation:  
\[(ATC_{n})_{\text{DeFi},X}=(ATC_{n})_{\text{DeFi},Y} \tag{12}\]  
In this case, the ATC (asset transfer confirmation) numbers received by the miners in both DeFi X and DeFi Y are the same. According to Requirement 3, an asset is recreated at DeFi Y after receiving proof for asset burning at DeFi X, preventing the double-spending attack against malicious users.  
CrossDeFi secures against such attacks by ensuring immediate transfer confirmation from the recipient DeFi chain back to the sender. Consider \(n\) as the number of nodes in each blockchain within CrossDeFi. Each miner \(Mi\) from each DeFi blockchain sends an ATC message in the opposite direction to the sender's blockchain network, fostering trust among parties. This process involves each miner securing \(n\) verifications to validate the legitimacy of the miners and build trust across the network. CrossDeFi employs a traceable ring signature to sign the ATC message.  
If the verification count \(\text{VC}(\text{Msg}_{\text{ATC}})=n\), the miner's status is trusted, and it is deemed valid. If BMiX from DeFi X receives \(n\) verifications from DeFi Y, and BMiY from DeFi Y receives \(n\) verifications from DeFi X, trust is effectively established. This arrangement significantly enhances the security against double-spending attacks within CrossDeFi.  
**Case 2:** Duplicate Asset Spending  
Proof.: Consider a scenario where a malicious user (MU) in DeFi X attempts to send duplicate or conflicting assets to the same user, [PERSON] in DeFi X. If MU \(\
ot\in\sum_{i=1}^{n}\) attempts to spend asset Ass X and its duplicate, Ass X*, then CrossDeFi performs a check to verify asset ownership to confirm that a single user holds the asset. Through efficient asset representation policies, CrossDeFi ensures that each asset can only be owned by one user at a time, thereby preventing the possibility of double-spending.  
This rigorous approach to security in the CrossDeFi framework not only prevents the occurrence of double-spending but also establishes a trust mechanism that supports the integrity of transactions across multiple DeFi platforms.
## 2 Materials and Methods
### Model Development  
Models can effectively explore the relationship between variables and targets through transformation, projection, and data mapping, and their performance is affected by different operations. In this study, four algorithms were employed to predict coal quality indicators.  
Partial Least Squares Regression (PLSR) is a robust multivariate statistical method widely used in spectroscopic analysis [11; 13; 15]. It efficiently addresses multicollinearity and high-dimensional data by extracting latent variables that capture the maximum covariance between features and targets. PLSR provides fast learning, good predictive performance, and a strong ability to handle noise and missing data.  
Random Forest (RF) is a powerful ensemble learning technique that employs the bagging method to enhance decision tree performance [47]. Bagging methods create multiple data subsets through random sampling and train regression trees on each subset, eventually integrating all the predictions to obtain the final goal. The integrated and stochastic nature of RF contributes to its fast training, robustness to outliers, and strong resistance to overfitting.  
Back Propagation Neural Networks (BPNN) are feed forward neural networks based on the back propagation algorithm, which is composed of an input layer, a hidden layer, and an output layer [48]. The neurons in the hidden layer containing weights and biases can capture and represent the complex patterns of the data through nonlinear variations, which is sensitive to the model effect; the learning rate in BPNN controls the updating step of neuron weights to achieve model convergence, which is crucial for model training.  
Least Squares Support Vector Regression (LSSVR) is an important regression method based on support vector machines (SVM), characterized by the fast learning speed, strong generalization ability, and simple implementation [39]. The approach simplifies the traditional SVM training process by introducing the least squares loss function, transforming the problem into solving a set of linear equations. The kernel function in LSSVR mapsthe input data to a high-dimensional space to address nonlinear relationships, which is available with many options such as linear, rbf, etc.
# Agreement Between Tele- and Face-to-Face Assessment of Neuromotor Development in High-Risk Children
## 4 Discussion
### Limitations  
The limitations in this study include that the participants were recruited from the same hospital. This fact may affect the external validity of the results given that they may not reflect broader demographic, socioeconomic, or cultural diversity, limiting the applicability to other settings. Future studies should include multi-site designs. Only Spanish-speaking families were included in this study. This may have introduced selection bias by excluding families who speak other languages and affected the generalizability of the findings. In our study, there were no patients excluded from this criterion. However, future studies should include participants from diverse linguistic and cultural backgrounds. In addition, the quality of the Internet connection at home was not the same in the different assessments. Although the participants included were familiar with an Internet connection, in some cases, some difficulty following the children required moving the camera to see the information needed for scoring. Given that the assessment was performed according to the AIMS and NEM, the results obtained cannot apply to other scales that evaluate the acquisition of motor milestones in children. Additionally, although it is frequently used in clinical practice, the lack of information regarding predictive validity and interrater reliability from previous studies of the NEM assessment is a limitation that may increase the risk of bias and limit the generalizability of the results to broader populations or contexts.
# Fundamental Matrix Computing Based on 3D Metrical Distance
## 5 Experiment
### Reconstructed 3D Points  
The visual results of Figure 5 show the 3D reconstruction with respect to our 3D metric projection error criterion based on \(E\) from \(F\). However, eight-point [6], seven-point [6], and Gold [6] methods can only compute the projective \(F\) and cannot reconstruct the metrical 3D result directly.  
In our experiment, the eight-point method always has good reconstruction results for each criterion. Every point of \(X\) is recovered precisely. The projection errors are 1.50, 0.5, and 0.45 for the three different scenarios, respectively.  
On the other hand, for the seven-point method, no matter how carefully the parameters are adjusted, the reconstruction result is not good enough because seven-point method is very sensitive to noise. The reconstructed 3D points \(X\) of \(Simu\) almost lie on one plane which in fact are two planes. The reason is that restriction of \(\det(F)=0\) is too arbitrary and only has mathematical meaning. For the reconstruction here, \(\det(F)=0\) has a kind of side effect when computing \(E\). In our experiment, \(\det(F)=0\) was a useful and necessary but weak constraint. It is useful because it adds one equation to reduce one point pair  
Figure 5: 3D metric reconstruction result with ours metric gold criterion for (**a**) _Dino_, (**b**) _Zhanglan_ and (**c**) _Simu_. The red and blue ‘\(+\)’ are the positions of two cameras. In (**c**), we rotate the scene to a special view point to see that the 50 points lie at a same plane and thus nearly only a line can be seen at the lower part of the figure, which validate that our method is reasonable and robust. The precision analysis is described in the last paragraph of Section 5.4.  
input and the epipole has to be calculated under this constraint. It is weak because that it affects the result significantly when this constraint is applied. Most of the time without this constraint, the better projective error or average epipolar lines distance are obtained, although this does not mean they will result in better 3D construction results. Actually, \(\det(F)=0\) limits the space of the solution of \(F\). When this constraint is applied to the algorithms above, there is an advantage to compute the epipoles on corresponding images.  
The appended material are the result from the five-point method that computes \(E\) as output directly. So the epipolar criterion can not be applied to five-point method. Compared with the other two methods over their distance to the epipolar line, Gold and Ours almost have results similar to the seven-point method. The five-point method's projection error of \(X\), which is listed on the fourth row of Table 1, is obviously bigger than the other four. The five-point method is not robust or reliable enough in our experiments.  
For the scene \(Simu\), if only 0.5 pixel random projection error or less is added on \([u,u^{\prime}]\), it could have better reconstruction than the other scenarios because the optical center of camera lies at image center \([w/2,\ h/2]\) precisely.
## 2 Thermodynamics and Operational Conditions of the Proposed System
### Thermodynamics Models and Equations  
To establish a thermodynamics model for the system proposed, a number of assumptions were made, such as (1) both the ambient temperature and the sea water temperature were 298 K; (2) the pinch point was 30 K in the evaporator of the ORC and was 5 K in the other heat exchangers; (3) the isentropic efficiency of the expander, compressors, and pump was 0.7, 0.7, and 0.8, respectively; (4) the temperature of the exhaust gas exiting boiler was higher than the acid dew point of 393 K [36]; (5) both heat and pressure loss in all heat exchangers and pipes were assumed to be negligible; (6) the combined cycles were operated under steady state conditions; (7) the mechanical loss of the expander/compressor coupling was negligible.
## 4 Numerical Results
### Behavior of Magnetic Spring Force Term  
The non-dimensional magnetic spring force term can be expressed as  
\[f_{\rm Si}(y_{i})=b_{\rm i}\Big{[}\frac{1}{\{1+D_{1}(\delta_{1}-y_{i})\}^{4}}- \frac{1}{\{1+D_{1}(\delta_{1}+y_{i})\}^{4}}\Big{]} \tag{6}\]  
where, \(\{i=1,2\}\).  
If we plot \(f_{\rm S1}(y_{1})\) and \(f_{\rm S2}(y_{2})\) with the variation of the non-dimensional displacements \(y_{1}\) and \(y_{2}\), we obtain the Figure 4. Please note that, we have increased the non-dimensional displacements from \(-\delta_{1}\) to \(+\delta_{1}\) along the \(x\)-axis. The magnetic spring force value varies exponentially both in positive and negative directions of \(y_{i}\), although at the neighborhood of 0, the curves of \(f_{\rm S1}(y_{1})\) and \(f_{\rm S2}(y_{2})\) are almost flat, parallel to the \(x\)-axis.  
Figure 3: Plots of the resistance force terms \(f_{\rm R1}(\dot{y}_{1})\) and \(f_{\rm R2}(\dot{y}_{2})\) corresponding to the (**a**) state-variable \(\dot{y}_{1}\) and (**b**) state-variable \(\dot{y}_{2}\), respectively. In each figure, the \(x\)-axis is the non-dimensional velocities (i.e., state-variables \(\dot{y}_{1}\) and \(\dot{y}_{2}\)) and the \(y\)-axis is the non-dimensional force due to the resistance term. The parameter values are: \(a_{1}=5.8548\times 10^{-3}\), (**a**) \(\zeta_{1}=0.0868\), (**b**) \(\zeta_{2}=0.1036\). (Color Online).  
Figure 2: Evolution of the Dry-Friction terms with varying the (**a**) state-variable \(\dot{y}_{1}\) of the first oscillator and (**b**) state-variable \(\dot{y}_{2}\) of the second oscillator, respectively. In each figure, the \(x\)-axis is the non-dimensional velocities (i.e., state-variables \(\dot{y}_{1}\) and \(\dot{y}_{2}\)) and the \(y\)-axis is the non-dimensional dry-friction terms. The parameter values are: \(a_{1}=5.8548\times 10^{-3}\), (**a**) \(\zeta_{1}=0.0868\), (**b**) \(\zeta_{2}=0.1036\). (Color online).  
The expression of the Equation (6) is complicated to implement in any physical system, like in an electronic circuit. So, we consider other functions that will yield the exact behaviors, but the expression is more straightforward.  
\[f_{\text{S1}}(y_{1})=s_{1}y_{1}+s_{2}y_{1}^{3} \tag{7}\]  
and,  
\[f_{\text{S2}}(y_{2})=s_{3}y_{2}+s_{4}y_{2}^{3} \tag{8}\]  
Now, if we compare the Equations (7) and (8) with the Equation (6), we can obtain the constant values of \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{4}\) as 5.476, 40.0594, 6.7078, and 47.6527 respectively.  
The black curves in the Figure 5 show the function plot of the Equations (7) and (8). From the Figure 5a,b, we can say that the Equations (7) and (8) are well agreed with the Equation (6) and provide the same kind of behaviors.
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
## 5 Prognosis
### Cardiac MRI  
There are limited data on the ability of CMR parameters to define long-term cardiovascular risk in FD. In a longitudinal study of 73 adults with FD over 4.8 +/ - 2.4 years, serial CMR was performed to investigate the potential for fibrosis detected using LGE to predict risk of malignant ventricular arrhythmia (incorporating non-sustained and sustained ventricular tachycardia and sudden cardiac death). In those without LGE, there were no reports of ventricular arrhythmia on Holter monitoring compared with 13 episodes of ventricular arrhythmia in those with LGE, of whom 5 experienced sudden cardiac death [46]. This finding is consistent with the established prognostic value of LGE in identifying ventricular arrhythmia risk in coronary artery disease [47] and other cardiomyocytesopathies [48].  
Imaging studies investigating prognostication in FD have tended to be small, single-centre reports and further work is needed to clarify 'what to expect' from the future in each FD patient, a process which is essential for decision-making in therapy, particularly in relation to selection of the patient for device implantation. It has long been recognized in FD that direct genotype-phenotype correlation is lacking, and the development of the phenotype is altered by a multitude of factors other than enzyme activity.  
Summary points:  
* Standard imaging parameters of MWT, LVH and diastolic function are predictive of cardiovascular outcome in FD;
* Advanced parameters, including GLS and MW, may independently predict risk of MACE in FD, consistent with data from other non-ischaemic cardiomyopathies but further work is required;
* Data on prognosis in FD based on CMR are limited but presence of LGE is associated with risk of malignant ventricular arrhythmia.