esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Local-Global Dynamic Multi-Graph Convolutional Neural Network Model  
This study refers to multi-step prediction models [31; 38; 39] and adopts the encoder-decoder structure. The ST-Conv block proposed by [PERSON] et al. effectively extracts spatiotemporal features. The combination of GCN and Gated TCN in this block allows for prediction without relying on recurrent neural networks [40]. However, the STGCN model is limited to single-step prediction. Inspired by this research, our study employs an improved \"GCN + Gated TCN\" structure in the encoder to capture spatiotemporal features. Considering station dynamics and multiscale spatial dependencies, a Global-Local Dynamic Spatiotemporal Graph Convolutional Module is proposed. [PERSON] et al.'s study directly uses GCN + Gated TCN to learn spatiotemporal features and introduces a Pattern-Aware Adjacency Matrix (PAM) module to consider pattern similarity features in input time series [38]. In contrast, the dynamic adjacency matrix in this study primarily considers the impact of station dynamic changes on the demand of surrounding stations. Therefore, a dynamic multi-graph generator is designed to generate dynamic multi-graphs containing multiple spatial semantic features and information about station changes. In the decoder, this study not only incorporates conventional external time features but also considers the severity of COVID-19's impact on bike-sharing demand in the post-pandemic era.  
The overall architecture of the proposed model is shown in Figure 5. The architecture consists of an encoder and a decoder. The encoder is composed of a Global-Local Dynamic Spatiotemporal Graph Convolution Network (Global-local Dynamic ST-GCN), capturing dynamic spatiotemporal features to improve prediction accuracy. The decoder comprises Temporal Attention (TA), Time Feature Fusion (TFF), and Gated Recurrent Unit (GRU) to further optimize prediction results by focusing on the dynamic nature of time. Additionally, rich spatial and temporal multi-source features are, respectively, incorporated into the encoder and decoder, providing abundant spatiotemporal semantic information for predictions.
## 4 Backgrounds and Methods
### Backgrounds
#### 4.1.2 Radio Link Failures  
In some cases, the general configuration of the HO parameters is unsuitable. If the serving eNB does not initiate the HO at the correct time, an HO that is too early or too late occurs, which results in an increase in the number of RLFs. Moreover, the radio link must be re-established to the serving eNB, which causes the HO procedure to restart. Figure 8 illustrates late and early HOs with two UEs and high-speed movement between cells.  
Before the HO procedure finishes, if the signal quality of the serving cell is inadequate for the data communication requirement, a late HO occurs. Otherwise, if the HO procedure has already been completed when the signal quality of the target cell becomes inadequate for the communication requirement, an HO that is too early occurs. In other cases, an increase in the number of RLFs is due to ineffective target cell selection by the HO decision algorithms.  
Figure 8: UE1 started the HO too early. UE2 started the HO too late.  
Figure 7: Event A3-based HO parameters in LTE networks.
## 4 Stability Analysis of Multivariable Milling Tools
### Experimental Characterization of One Degree of Freedom Milling Equation and Cutting Force Model  
#### 4.2.1 Experimental Modal Analysis  
An experimental workpiece was assembled with a 7075T6 aluminum block of 101 mm \(\times\) 172 mm supported by two thin plates (walls) with a thickness of 4.5 mm. This assembly mimics a DOF as described in Equation (23). The workpiece assembly was rigidly fixed to the workbench of a Makino F3 machining center. For modal analysis, tap testing was performed using a 352C68 PCB Piezotronics accelerometer and an impact hammer model 9722A500. The signals were acquired with a Polytec VIB-E-220 data acquisition card and processed with VibSoft signal analyzer software as shown in Figure 5a. Using the CutPro 8 software, the modal parameters were fitted resulting the values \(\zeta=0.068\), \(m_{m}=3.8\) kg, \(f_{m}=132\) Hz and \(\omega_{n}=829\) rad/s.
## 3 Results
### SEM with Microanalysis EDX Results
Figure 4: SEM micrographs of the received samples: (**a**) melted in a ceramic crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 1000 RPM; (**b**) melted in ceramic crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 800 RPM; (**c**) melted in PtIr crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 1000 RPM. It should also be noted that the expected value and, thus, the statistically most common grain size in the material for the samples from the ceramic crucibles cooled at 1000 RPM and 800 RPM is 23 nm (Figure 5a) and 34 nm (Figure 5b), respectively, which seems to confirm the assumption of the agglomeration of crystallites. Furthermore, it may indicate that the faster cooling resulted in smaller crystallites, an effect that was expected, but which could not be observed from the values determined by [PERSON]'s equation. For the samples melted in ceramic crucibles, an EDX microanalysis was performed for the presence of crucible-derived impurities. The obtained results indicate that the samples contain from 8.87 to 9.87 at.% Si and from 4.42 to 5.55 at.% Al. Considering a systematic error value of 0.14-0.38 at.%, it is fair to say that these values do not seem to depend significantly on the melting temperatures chosen in this study. Figure 5: Histograms of distributions of grain sizes of the received samples (based on SEM micrographs presented in the Figure 4): (**a**) melted in a ceramic crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 1000 RPM; (**b**) melted in ceramic crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 800 RPM; (**c**) melted in Ptlr crucible at 1000 \({}^{\circ}\)C, quenched by twin rollers method at 1000 RPM. Ordinate axis presents the relative frequency of occurrence of grains of a certain radius, and the equivalent grain radius value (nm) [23].
# Analytical Computation of the Spatial Resolution in GNSS-R and Experimental Validation at L1 and L5
## 2 Methodology
### Computation of the Electric Field Scattered by the Discontinuity between Two Media  
Assuming that an electromagnetic wave impinges over the z = 0 plane around (x,y,z) = (0,0,0), where the specular reflection point is, and:  
* The extension of the Fresnel reflection zones is small enough (a few hundred meters from a low Earth orbiter) so that the variations of the local incidence angle can be neglected over a few Fresnel zones;
* The plane z = 0 is composed of two flat half-planes of different materials, and thus exhibiting different reflection coefficients; and
* The transmission coefficient for the wave emitted by the image transmitter (Figure 5) is equal to the reflection coefficient evaluated at the local incidence angle at the origin.  
Then, as illustrated in Figure 8, the total electric field can be immediately computed as the sum of the following two terms:  
\[E_{diff}=E_{free\ space}[F(v)\cdot\rho(\varepsilon_{r,1},\ \theta_{i})+F(-v)\cdot \rho(\varepsilon_{r,2},\ \theta_{i})], \tag{6}\]  
where \(\rho(\varepsilon_{r,n},\ \theta_{i})\) is the Fresnel reflection coefficient, and \(\varepsilon_{r,n}\) is the dielectric constant of the half-plane \(n\). The received power is proportional to the square of the absolute value of Equation (6).  
\[P_{diff}=P_{free\ space}\big{|}F(v)\cdot\rho(\varepsilon_{r,1},\ \theta_{i})+F(-v)\cdot \rho(\varepsilon_{r,2},\ \theta_{i})\big{|}^{2}, \tag{7}\]  
Figure 7: Graphical representation of the concept of Babinet’s principle and the three equivalent obstacles: electric field incident on an arbitrary obstacle (**left**), associated opaque screen (**center**), and complementary screen (i.e., hole in the infinite plane; **right**).  
Following Equation (7), an example is shown in Figure 9. The red and blue lines correspond to \(\left|F(v)\right|\) and \(\left|F(-v)\right|\) in decibels. The black line corresponds to the square of the absolute value of the term within brackets in Equation (7), computed for \(\rho(\varepsilon_{r,1},\ \theta_{i})=\frac{2}{3}\), and \(\rho(\varepsilon_{r,2},\ \theta_{i})=\sqrt{0.1}\), which corresponds to the normalized peak power that would be collected by a GNSS-R instrument.
###### Abstract  
Background: Myocardial damage in myocarditis is assessed through late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR). Variability in quantifying myocarditis extent results from imprecise image segmentation and inconclusive data on quantification method selection. To improve analysis precision, segmentation steps are systematically ranked based on their inherent risks of error. Additionally, data on two distinct quantification methods are presented. Methods: Using newly developed software, four experts analyzed five LGE-CMR left ventricular (LV) short-axis (SAx) images of myocarditis patients in three sessions. Regions of interest (ROIs) (myocardial (ROImycol), reference (ROIref), and exclusion region (ROIexcl)) were identified and used to calculate LGE extent with 3\(\sigma\) (intensity above three standard deviations (\(\sigma\)) in reference) and the full width at half maximum (FWHM) method (intensity above 50% of maximum signal in reference). The reference LGE extent was calculated and the influence of the ROIs on LGE extent variability was determined. Interobserver and intraobserver variability were evaluated as 1-intraclass correlation coefficient (ICC). Results: LGE extent variability was 6.2 \(\pm\) 0.6% for 3\(\sigma\) and 4.0 \(\pm\) 0.6% for FWHM. The contributions of ROImycol, ROIref, and ROIexcl were 1.5 \(\pm\) 0.2%, 2.7 \(\pm\) 0.4%, and 2 \(\pm\) 0.3%, respectively, for 3\(\sigma\), and 1.1 \(\pm\) 0.1%, 1.6 \(\pm\) 0.4%, and 1.3 \(\pm\) 0.3%, respectively, for FWHM. LGE extent was lower in FWHM. Interobserver variability was 0.56 for 3\(\sigma\) and 0.43 for FWHM. The intraobserver variability was higher for the 3\(\sigma\) method in all four observers. Conclusion: ROIref selection contributed most to LGE extent variability. FWHM yielded lower LGE extent and lower inter- and intraobserver variability. Due to low statistical significance, the findings are only partially confirmed.  
myocardial; cardiac magnetic resonance; variability; full width at half maximum; thresholding methods; segmentation
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study
### SCLM Control in Transient Condition
#### 4.4.3 Comparison of Simulation Results  
As shown in Figures 8 and 12, in the conventional control, the HVDC system is controlled to sustain the constant power output, but when the RAS or the SCLM control is applied, the active power setpoint is changed to mitigate the overload. The reason why the active power is changed again after 2 s in the SCLM control is that the operating point is changed to minimize the system total loss within the operating range where the overload is mitigated. Additionally, Figure 9, Figure 10, Figure 13, and Figure 14 show the reactive power change at both ends of HVDC. In the conventional control and RAS control, the converters control the reactive power output to sustain the AC voltage setpoint in the pre-contingency condition, and the reactive power output fluctuated according to the system conditions. On the other hand, in the SCLM control, the reactive power output of HVDC for minimizing the system total loss is calculated and changed after 2 s. Accordingly, the total loss is 1.66 and 5.84 MW lower, respectively, compared to the RAS results as expressed in Tables 8 and 10.  
Comparisons of overload changes for each case can be seen in Figures 11 and 15. In terms of the overload criteria, the load of the SCLM control is lower than that of RAS control in Figure 11, whereas the load of RAS control is lower than that of the SCLM control in Figure 15. In the SCLM control, since the operating point is determined to minimize the loss within the operating range for relieving the  
Figure 14: Reactive power operation change in the line overload case at the inverter end.  
Figure 15: Comparison of HVDC control for overload mitigation in the line overload case.  
overload, the line load can be high, but it does not exceed the overload limit, so it is not a problem in terms of reliability.
# Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance
## 2 Materials and Methods: Risk Assessment Algorithm
### Importance Index Estimation  
The importance index (_II_) of a PT indicates the strategic relevance of the asset within the reliability context of the electrical power distribution system. It considers the unit's technical parameters, the operational environment, and the penalties imposed by regulatory entities. For this work, there are a set of 11 criteria to determine the _II_; each criterion can take an integer value between 1 and 3. The final value for the _II_ can be inferred using Equation (2).  
\[II=\frac{\sum\limits_{ii=1}^{p}\left(S_{ii}\times W_{ii}\right)}{\sum\limits_{ ii=1}^{p}\left(S_{\text{max}_{ii}}\times W_{ii}\right)} \tag{2}\]  
where _II_ indicates the importance index of the PT, \(S_{ii}\) is the score assigned to each criterion, \(S_{\text{max}ii}\) represents the maximum possible value, in this case, a value of three, and \(W_{ii}\) is the respective weight of each entry parameter. As stated before, those criteria can only take integer values between 1 and 3. For this reason, a normalizing process was carried out to obtain an _II_ value between zero and one. For that purpose, Equation (3) was used.  
\[II_{N}=\frac{II-II_{\text{min}}}{II_{\text{max}}-II_{\text{min}}} \tag{3}\]  
where _IImax_ and _IImin_ represent the maximum and minimum values that _II_ can take, for this work, those values are one and one-third. Values close to zero indicate the low strategic importance of the PT, whereas values close to one denote a high relevance of the asset. The 11 criteria chosen for this method and their respective weights are presented in Table 1.  
The _N_-1 criterion indicates the ability of the system to handle a sudden disconnection of the PT and supply the demanded load. The critical loads' criterion refers to essential loads such as hospitals, airports and continuous production factories, powered by the evaluated trans-formation unit. Mean load is the estimated average power provided by the transformer in the last 30 days, while the unavailability penalty criterion will depend on the regulations of each country. For this work, the measure is based on Argentina's energy regulator provisions.  
Figure 10: Health index fuzzy model.
## 6 Algorithms and Frameworks in Federated Learning
### Adaptive Learning Rates in Federated Contexts
#### 6.4.3 Federated Adam  
Adaptive Moment Estimation (Adam) is an optimization algorithm that builds upon the foundation of Stochastic Gradient Descent (SGD) by introducing adaptive learning rates for each parameter. This adaptation is achieved by considering the first and second moments of the gradients, which provide estimates of the mean and variance, respectively.  
Adam utilizes these moments to dynamically adjust the learning rate for each parameter, leading to potentially faster convergence compared to standard SGD.  
The federated version of Adam, known as Federated Adam, incorporates this adaptive learning rate methodology into the distributed setting of federated learning. In FL, the training data are split across multiple devices or servers, and models are collaboratively trained without directly sharing the underlying data. Federated Adam enables efficient training in such scenarios by allowing each participating device to estimate the local first and second moments of the gradients for its model parameters. These local estimates are then aggregated across devices to compute global estimates, which are subsequently used to update the learning rates for all participating devices. This approach facilitates adaptive learning in a decentralized manner, promoting faster convergence and improved model performance in FL settings.  
**Challenges in Federated Settings:**  
* Increased communication overhead as clients might need to transmit more information (both moments) to the server.
* Similar to Federated Adagrad, the non-IID nature of the data can cause aggregation discrepancies at the server level [91].
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 2 Theoretical Background
### Whispering Gallery Modes (WGM)  
In a geometrical approximation, the light circles the interior of the sphere through multiple total internal reflections returning in phase to the origin point. The resonance condition of a given wavelength \(\lambda\) inside of a microsphere of radius \(R\gg\lambda\) is approximately described by the following equation:  
\[2\pi n_{eff}R=l\lambda \tag{2}\]  
where \(l\) is the mode number and \(n_{eff}\) is the effective refractive index of the sphere.  
Equation (2) allows the evaluation of the WGM resonances inside the sphere by taking into account only the sphere parameters. Small perturbations in size or refractive index of the sphere cause a shift in the resonant wavelength. This allows the study of the external conditions of the microsphere causing the difference in its properties. These changes in the parameters of the microsphere can be produced by variations in pressure, chemical composition of the surrounding media, or temperature. In order to know the relationship of the resonant wavelength with temperature, the derivative of Equation (2) with respect to the temperature is calculated:  
\[\frac{d\lambda}{dT}=\lambda\left(\frac{1}{n}\frac{\delta n}{\delta T}+\frac{1} {R}\frac{\delta R}{\delta T}\right)=(\alpha+\beta)\lambda \tag{3}\]  
where \(\alpha\) is the thermo-optic coefficient (\(\alpha=\) 1/\(n\)\(\delta n/\delta T\)) and \(\beta\) the thermal expansion coefficient (\(\beta=\) 1/\(R\)\(\delta R/\delta T\)). The signs of these coefficients are known to be generally positive in glasses [18]. Therefore, it is to be expected that the peaks will shift to the red region of the spectra as the temperature increases, as can be seen in Figure 2.  
Figure 2: Wavelength displacement of a whispering gallery mode (WGM) resonance at different temperatures in the emission spectrum of YAS:Nd\({}^{3+}\).
## 3 Proposed Algorithm
### Basic operation of the algorithm
#### 3.2.1 Local routing table  
As reactive routing protocols typically operate based on a routing table, the proposed protocol also has a local routing table. The local routing table is updated whenever it receives RREQ, RREP, and RERR messages. Table 1 shows the proposed local routing table, which has more fields than the AODV routing table. The function of each field is described below. The DestID field stores the destination node ID using direction values that range from 1 to 4. The distance field stores the distance value between two neighbor nodes, from 1 to 250, as the transmission range of the node. The HopCnt field indicates the hop count information to the destination node. SeqNo is given automatic sequential numbers, which specify a larger sequence number as newer route information. Lifetime is the lifetime  
Figure 2: Basic operational flow chart.  
of the route information; the information with a higher lifetime value remains in the routing table longer. Finally, the state field determines whether or not the route information is valid. If the lifetime field is 0 or if the state field reads INVALID, the routing tab entry is deleted.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 3 Model Design
### Model Structure
The parameters of the model have been determined according to the parameters of the prototype and the similarity relationship, shown in Table 1. The results show that the model needs to have high density and low elastic modulus, which is a challenge because no existing common material can achieve this alone. Therefore, a new type of composite structure is adopted in the scale model of the SFT. The polyethylene pipe and the steel counterweight ring are reasonably arranged, so that the model has the required stiffness and mass and can meet the Froude similarity and Cauchy similarity at the same time. As Figure 3 shows, the model is mainly composed of a PE pipe and counterweight steel rings. The main body of the model is made of a PE pipe, which provides the elastic stiffness of the structure. Compared with other common pipe materials such as polyvinyl chloride and plexiglass, polyethylene has a higher strength and toughness and a lower elastic modulus. The counterweight steel rings are evenly distributed within the PE pipe to provide additional mass. The arrangement of steel rings is discontinuous to reduce the influence on the overall stiffness of the structure. The outer diameter of the counterweight steel ring is slightly smaller than the inner diameter of the PE pipe. They are connected by blind rivets, ensuring their position is relatively fixed. All gaps at the connection are sealed with waterproof glue, a crucial step for maintaining the integrity and water tightness of the model. This model not only fulfills the similarity principles but also facilitates sensor installation within the tube, thus preserving the hydrodynamic profile of the model and improving the accuracy and reliability of the model experiment. Following the fabrication of the scale model, the similarity test is an important step to prove its reliability, and it is the determinant of whether the model can be applied to the hydrodynamic experiment. In this study, the mass similarity, elastic similarity, and natural frequency of underwater motion of the model were tested and compared with the numerical simulation results to verify whether the design of the model and the manufacturing method used reached the predetermined goal, which determined whether it met the Cauchy similarity and the Froude similarity.