esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis on Time Consumption  
Time consumption is essential for the online engineering applications. Figure 8 illustrates the simulation results of time consumption of interpretation. For the local interpreta  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Iteration**} & \multicolumn{8}{c}{**Active Power Generation (p.u.)**} & \multirow{2}{*}{**Control Generators**} & \multirow{2}{*}{**Elapsed Time (s)**} \\ \cline{2-2} \cline{4-9}  & **G1*** & & & & & & & & & \\ \hline
1 & 5.78 & 5.30 & 3.23 & 1.84 & 4.59 & 0.00 & 2.67 & 3.16 & G4(+), G7(\(-\)) & 1.053 \\
2 & 5.84 & 5.30 & 3.23 & 2.02 & 4.59 & 0.00 & 2.46 & 3.16 & G4(+), G7(\(-\)) & 1.050 \\
3 & 5.85 & 5.30 & 3.23 & 2.22 & 4.59 & 0.00 & 2.23 & 3.16 & G4(+), G8(\(-\)) & 1.044 \\
4 & 5.86 & 5.30 & 3.23 & 2.44 & 4.59 & 0.00 & 2.23 & 2.91 & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The iteration process of adjustment.  
Figure 7: Angle trajectories to a disturbance at 0.2 s before and after implementing SC.  
tion shown in Figure 7(a), the elapsed time mainly consists of two parts: the clustering time by k-means and the calculation time of SHAP. For example, when the number of background data points is 20, the time cost of clustering is 0.529 s, and the elapsed time of SHAP is 0.698 s for each interpreted OC/instance. Besides, the elapsed time is nearly proportional to \(K\). To reduce the interpretation time, \(K\) should be reduced by aggregating instances.  
The global interpretation shown in Figure 7(b) is based on the interpreter trained by 20 background data points. For example, when the number of interpreted instances is 20, the global interpretation takes 13.639 s, nearly 20 times the consuming time of SHAP for one interpreted OC/instance. The elapsed time is nearly in proportion to the number of interpreted instances. Thus, the number of interpreted instances for the global interpretation should not be too large and is recommended below 20. Twenty instances can well describe the characteristics of the value change of the features (e.g., Figure 6).  
Overall, the time consumption of the proposed interpretation method for DSA instances is low, satisfying the requirements of the online rolling applications of critical unit detection and power system preventive security control.
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 3 Feature Extraction and Utilization
### Digital Features
#### 3.1.4 Log Sequence Features  
A log sequence refers to a set of consecutive log records in a log file. The log event features encoded by a log event cannot record enough information, and, in particular, the relationship feature before the log event cannot be recorded. On the other hand, in order to meet different anomaly detection methods, it is also necessary to construct log sequence features. For example, the time series information of log event features cannot meet the needs of random forest classification. Just as sentence vectors or log event vectors are obtained from word vectors, log sequence features are generated in a similar manner. For example, by using TF-IDF to transform log sequence features into a weighted average representation of log event features, the weight of rare log events is higher than that of common log events.  
[PERSON] et al. [47] considered that some words in the log are invalid words (such as \"to\"), and the word embedding technology word2 vec cannot filter these pairs of meaningless words. Furthermore, because word2 vec uses words as the coding unit, these frequent invalid words will increase the vector space. In order to reduce the waste of computing resources, a method called log2 vec was proposed. Log2 vec performs part-of-speech tagging on words, and vocabularies tagged as invalid words are set to zero vectors by the encoder. Then, the log event vector is generated by weighting the corresponding log template words, and, similarly, the log sequence vector is generated by averaging the log event vector. [PERSON] et al. [48] explored the method of constructing log sequence features by modeling the word-word interaction information in a log sequence. This encoding method using word-by-word matching can deal well with new logs in the evolution of the system. In LogEvent2 Vec [45], the log event is used as the input of the encoder to obtain the correlation between log events, and, on this basis, the log sequence vector is obtained by averaging the log event vector. It should be noted that, although this log sequence feature is aggregated from log event features, it does not mean that it is more suitable for anomaly detection. Since it is limited by machine learning classifiers, the accuracy is often lower than that of deep models, and the method of using log sequence features is not as effective as the latter. One possible way to improve accuracy is to reduce the size of the log event window [49].
## 3 Results And Discussion
### Calculation Results of Flow Noise
#### 3.2.2 Calculation Results for Lighthill Volume Excition  
The calculation results for the Lighthill volume as an acoustic excitation are shown in Figure 8, and a comparison of the calculation results regarding Lighthill and pulsating pressure is shown in Figure 9. Except for monitoring point 13 in the tail vortex area, the comparisons of the rest (monitoring points 1-12 and 14) are not very different, and they all show a greater sound pressure level of the calculation results from the pulsating pressure as the excitation. Therefore, only the cases of monitoring points 1 and 13 are shown in Figure 9. When the difference in sound pressure level is 20 dB, the difference in sound pressure is at least one order of magnitude (as Equation (8)), so it is reasonable for researchers to ignore quadrupole sources in fluid-radiated noise calculations and consider pulsating pressure alone as a dipole source, slightly affecting the calculation results.  
\[L=20\lg\frac{p}{p_{ref}} \tag{8}\]  
where \(L\) is the sound pressure level; \(p\) is the sound pressure; \(p_{ref}\) is the reference sound pressure, taken as \(1\times 10^{-6}\) Pa.  
In one case at monitoring point 13, the calculated sound pressure level for Lighthill volume excitation within 200 Hz is substantially higher than the calculation results for pulsating pressure excitation, verifying our assumption that the quadrupole source has non-negligibility in some regions due to very intense vortex activity at a higher velocity flow. Of course, for most applications, appropriate neglect can simplify the calculation process and provide acceptable results.  
Figure 7: Sound pressure level distribution for pressure pulsation excitation.
## 2 Materials and Methods
### Electrode Placement  
A Delsys Trigno wireless EMG system (16-bit resolution, EMGWorks, version 4.7.3.0, Delsys Inc., Boston, MA, USA) was utilized to capture both the EMG signals and the angular velocity of the thumb at the anatomical reference point during the scissors opening and closing task. The reliability of the EMG system was validated (ICC = 0.92) in previous studies [28]. Avanti sensors, used as EMG electrodes, were aligned parallel to the muscle fibers with a 10 mm separation between electrodes. The skin was prepared to ensure that the impedance between the electrodes remained below 20 k\(\Omega\). To investigate how the motion of the scissors affects the thumb, an sEMG (sEMG) sensor was positioned at the anatomical reference point. In this experiment, the sensor was also used to assess the maximal contraction involved in the key pinch and the scissors' opening and closing activities.
# Trajectory Planning for Intelligent Robotic and
## 1 Trajectory Planning for Intelligent Robotic and Mechatronic Systems  
Trajectory planning is a crucial and challenging problem for research on intelligent robotic and mechatronic systems, which play a pivotal role in modern manufacturing processes, and especially within the framework of Industry 4.0 [1]. Indeed, in every robotic application, it is required to define not only a path, but also a motion law that can guarantee a feasible and safe operation of the system according to the requirements of the task and the limits of the robot [2]. Many approaches to the problem of trajectory planning have been developed and investigated in the literature, with applications that span from industrial, collaborative and, more in general, autonomous and intelligent robotic and mechatronic systems [3, 4].  
The motion law of a robotic system can be planned by considering different goals. The design of a proper motion law can be evaluated, for instance, in relation to the energy consumption of a robotic or mechatronic system, and, therefore, optimal trajectories can be determined based on the best performance of the robot in terms of time-energy consumption [5, 6, 7]. Another interesting field of application is that of vibration reduction. Indeed, many automatic machines and mechatronic applications require smooth and jerk-limited trajectories during the prescribed operation [8, 9, 10]. Moreover, emerging scenarios of industrial robotics, such as collaborative robotics and human-robot interaction, demand advanced strategies for the planning of robot trajectories to ensure smoothness, safety, and fluency during the execution of a task for a robot working alongside a human operator [11, 12, 13]. Finally, the trajectory planning for robotic and mechatronic systems is also tightly coupled with the motion control problem of such systems to guarantee high performance in the execution of the demanded motion law [14, 15].  
In this Special Issue, we invited researchers to contribute with original works and review articles related to trajectory planning for intelligent mechatronic systems, autonomous machines, industrial and collaborative manipulators, as well as mobile and reconfigurable robots. Original research papers on these themes have been sought, with a focus on both theoretical investigations and real-world applications on these topics.  
Suitable topics included, but were not restricted to, the following: path and trajectory planning, dynamic modelling, energy efficiency, vibration suppression, smooth trajectories, motion profile optimization, motion control, intelligent robotic and mechatronic systems, collaborative robotics, and motion planning for human-robot interaction.
## 3 System Architecture  
This section explains the fundamentals of continuous phase modulation and different components of the CPM-OFDM transceiver.
# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
## 3 Model Construction and Training Setup
### TabNet Model Architecture
The Feature transformer layer is similar to the role of the previous FC layer, which related to feature calculation and is more complex. The structure is shown in Figure 4. The Feature transformer layer consists of two parts. The parameters of the first half of the layer are shared; that is, they are trained together on all steps, while the second half is not shared and is trained separately on each step. This is carried out considering that for each step, the input is the same features (the mask layer only masks some features and does not change others), so we can use the same layers for the common part of feature computation first and then see to different parts using different layers later. In addition, we can see that the residual connection is used in the layer, multiplied by \(\sqrt{0.5}\) to ensure the network's stability. The split layer cuts the vector output from the Feature transformer layer into two parts, one of which is used to compute the final output of the model, while the other is used to compute the mask layer for the next step. The attentive transformer layer computes the mask layer of the current step based on the result of the previous step, as Figure 5 shows. The sparsemax layer can be understood as a sparse version of the softmax layer. The feature attribute output portrays the global importance of the feature. The model first sums the output vectors of a step of the model to obtain a scalar, reflecting this step's importance for the final result. Then, it is multiplied by the mask matrix of this step to reflect the importance of each feature, and the global importance of the feature is obtained by adding up the results of all steps. Figure 4: Feature transformer architecture. Figure 5: Attentive transformer structure. The federated averaging algorithm (FedAvg) enables decentralized training of the TabNet model across participants while preserving data privacy. Each participant trains a local TabNet model, computes updates, and sends them to a central server. The server aggregates these updates using a weighted average based on the dataset size, creating a global TabNet model, which is then shared back with participants. This cycle repeats until model convergence, combining TabNet's strengths with privacy-preserving federated learning.
## 4 Discussion
### Evolution of U-Mo Matrix
#### 4.1.1 M11 (Intermediate Burnup, 52%)  
At the intermediate burnup level, it was shown in previous work that the fission gas bubbles are present on the cell boundaries and in the cells of U-Mo fuel [23; 33]. The fracture surfaces at these burnups clearly show formation of small sub-grains near the cell boundaries. The microstructural changes taking place in U-Mo at the intermediate burnup  
\begin{table}
\begin{tabular}{c c c c c} \hline  & & **M11** & & **M14** \\ \hline
**Elements** & **at.\%** & **2 Sigma** & **at.\%** & **2 Sigma** \\ \hline U & 74.22 & 0.59 & 65.6 & 0.64 \\ \hline Mo & 19.80 & 0.53 & 26.8 & 0.36 \\ \hline Fission Products & 5.66 & 0.30 & 7.1 & 0.40 \\ \hline Impurities & 0.30 & 0.05 & 0.30 & 0.05 \\ \hline \end{tabular}
\end{table}
Table 2: Measured composition from M11 and M14 samples using APT (impurities: C, N, Si, and O).  
Figure 7: 18 \(\times\) 18 \(\times\) 2 nm\({}^{3}\) slice showing distribution of fission products observed from APT data in (**a**) M11 and (**b**) M14 samples, respectively. Dotted circles are used to highlight enriched segregation of fission products.  
level are comparable to the low-dose sample [23; 33]. The presence of nano-sized ordered bubbles was reported based on TEM findings. However, APT allows to chemically identify the fission products, which is extremely challenging from EDS-TEM analysis due to the small size of the bubbles in comparison to the thickness of the lamella. Previous attempts to study the nature of fission products in the nanometric bubble superlattice could identify only Sr, Y, and Ba [23; 33]. APT analysis on the other hand revealed the presence of Zr, Y, Nb, Mo, Tc, Ru, Pd, La, Ce, Nd, Sr, Ag, Pr, Sm, Gd, Xe, Cs, and Ba for the intermediate burnup sample. Note that the possibility exists that overlap of peaks in the mass spectrum could mask the presence of other fission products. The mass spectrum also revealed a trace amount of Xe in the specimen prepared from the matrix region. It is well-known that the gaseous fission products are located on the cell boundaries and at the interface of the interaction layer and matrix due to its poor solubility in the U-Mo alloy [23]. The relative contribution of fission products in the measured composition of the fuel at this burnup was close to 5.6 at.%, however, the local magnification effect can affect the absolute quantification of fission products due to the difference in the evaporation field of the U-Mo matrix and the bubble superlattice structures.
###### Abstract  
The application of autoencoders in combination with Dynamic Mode Decomposition for control (DMDc) and reduced order observer design as well as Kalman Filter design is discussed for low order state reconstruction of a class of scalar linear diffusion-convection-reaction systems. The general idea and conceptual approaches are developed following recent results on machine-learning based identification of the Koopman operator using autoencoders and DMDc for finite-dimensional discrete-time system identification. The resulting linear reduced order model is combined with a classical Kalman Filter for state reconstruction with minimum error covariance as well as a reduced order observer with very low computational and memory demands. The performance of the two schemes is evaluated and compared in terms of the approximated \(L^{2}\) error norm in a numerical simulation study. It turns out, that for the evaluated case study the reduced-order scheme achieves comparable performance with significantly less computational load.  
reduced order observers; PDE models; diffusion-convection-reaction systems; dynamic mode decomposition; autoencoders; machine learning; Kalman Filter
# Structural Features Guiding the Design of Liquid-Crystalline Elastomeric Fluorescent Force Sensors
## 2 Discussion
### Structural Factors That Impact the Mechanofluorescent Behavior  
#### 2.2.1 Fluorophore Content in the Elastomeric Network  
One of the parameters that influences the mechanoluminescence exhibited by carbazole-based LSCEs is the concentration of the organic fluorophore incorporated into the elastomeric network [19].  
Figure 1: (**a**) Deformation of an LSCE along the direction director, (**b**) evolution of the emission spectrum, and (**c**) variation of the relative emission intensity, \(\Delta I_{\varepsilon}\), of a representative LSCE upon applying a uniaxial deformation \(\varepsilon\) along its anisotropic direction.  
In order to illustrate this point, three different elastomers will be initially considered, which contain increasing amounts of 9-(5-hexenyl)-9\(H\)-carbazole as the fluorogenic component. Specifically, this molecule was introduced in a 5%, 10% and 20% mol, yielding LSCEs EM4_CBZN6_5, EM4_CBZN6_10 and EM4_CBZN6_20 (in Figure 2a), respectively. The XRD pattern (in Figure 2b) for elastomers EM4_CBZN6_5 and EM4_CBZN6_10 at 298 K displays two equatorial reflexes in the wide angle X-ray scattering regime (mesogens spacing \(\approx\) 4.4 A), evidencing a macroscopically aligned sample (order parameter = 0.74) with a nematic liquid-crystalline phase under ambient conditions. DSC thermograms reveal that the nematic mesophase is stable between the glass transition temperature at \(T_{\text{g}}\) = 276 K and the nematic-to-isotropic phase transition temperature, \(T_{\text{N-I}}\), at 327 and 313 K, respectively. The mesogenic and mechanofluorescent behavior of elastomer EM4_CBZN6_20 will be described in detail in Section 2.2.2 due to its isotropic nature.  
Elastomer EM4_CBZN6_5, which contains a 5% mol of carbazole units, exhibits a \(\Delta I_{\text{Max}}\) value of \(-\)27% (Figure 3). On the other hand, LSCEs EM4_CBZN6_10, which contains a two-fold higher concentration of carbazole fluorophores shows an increased efficiency of \(-\)34% [19]. In this way, a higher carbazole content within the elastomeric network leads to more efficient optical force sensors.
# Design of Multi-Mode Antenna Array for Use in Next-Generation Mobile Handsets
## 5 Fabrication and Measurements  
A prototype sample of the proposed 5G handset antenna array was fabricated as illustrated in Figure 10a,b. Due to the similar placements and performances of the modified PIFA pairs, the properties of the handset antenna design for port 1 and 2 are measured and compared below. The feeding mechanism of the adjacent elements is shown in Figure 10c.  
The measured and simulated results of the S-parameters are compared in Figure 11a. As seen, the S\({}_{11}\)/S\({}_{21}\) measurements have good agreement with the simulated results in terms of covering the required multi-operation bands: a quite good impedance bandwidth (S\({}_{11}\)\(<\)\(-10\)) is achieved to cover the operation bands of 2.45-2.65 GHz, 3.5-3.7 GHz and 5.6-6 GHz with resonances at 2.6, 3.6, and 5.8 GHz, respectively. In addition, as shown, the mutual couplings of the adjacent PIFAs are less than \(-15\), \(-10\)  
Figure 8: Radiation patterns of the PIFAs at the middle frequency (3.6 GHz).  
Figure 9: Calculated (**a**) ECC and (**b**) TARC results of the proposed design.  
and \(-13\) dB at the desired frequency bands. One of the vital parameters for the MIMO performance of an antenna array is diversity gain (DG), which can be calculated using the following formula:  
\[DG=10\sqrt{1-\left(ECC\right)^{2}} \tag{5}\]  
The DG characteristics of the antenna are illustrated in Figure 11b. The diversity gain function of the designed antenna over its operation band is more than 9.95 dB over the operating frequency bands [70]. In Figure 12a,b, we plot and compare the calculated TARC and ECC results of the PIFA pairs from simulated and measured results. As shown, the ECC function is very low (less than 0.05) over the different frequency bands of interest. Besides, the obtained TARC results are less than \(-18\) dB.  
Figure 11: Measured and simulated (**a**) S-parameters and (**b**) diversity gain of the two adjacent PIFAs.  
Figure 10: (**a**) Front and (**b**) back views, and (**c**) feeding mechanism of the fabricated sample.  
Measured and simulated radiation patterns (H-plane) of the modified PIFA element at different frequencies are shown in Figure 13. It is worth noting that, during the measurement of the antenna radiation patterns, one port was kept excited while the other one was loaded with a 50 \(\Omega\) load. As can be clearly seen, the sample handset antenna prototype provides good quasi-omnidirectional radiation patterns at different resonance frequencies with peak gains and acceptable agreement between simulations and measurements. It is found that when the antenna frequency increases, the gain level of the antenna is increased [71; 72; 73].