esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Silicon Photonics
### System Testing  
In order to assess the L3 MATRIX system's capabilities in more detail, we developed a specialized testbed to allow for characterization from a single component up to a system-level demonstration under realistic conditions. The testbed, depicted in Figure 12a, comprised FPGA boards with multiple high-speed RF and optical interfaces (SFP28/QSFP28) and servers with different network interfaces spanning from 10 Gb/s SFP+ up to 100 Gb/s QSFP28. In addition, commercial L2/L3 switches employing front-panel pluggable modules, along with miscellaneous testing equipment to interface with the L3 MATRIX technology. Figure 12b-e depicts indicative experimental results from L3 MATRIX final demonstrator devices. As already described, the development of our interconnection scheme requires all-optical multiplexers and demultiplexers, such as Echelle gratings [22]. To this end, 2 \(\times\) 6 and 2 \(\times\) 4 Echelle grating structures have been included in the fabricated chip of Figure 4, and both have been measured in our testing and characterization testbed, revealing the corresponding channel response of Figure 12b-e, respectively. The channels of the two Echelle gratings are following a Gaussian response, with the channel spacing being 4 nm and 20 nm for the 2 \(\times\) 6 and 2 \(\times\) 4 configurations, respectively. Further details for our Echelle structures are shown in Figure 12f-h, including the layout and two optical micrographs. The inset of Figure 12g shows an SEM image of the region of the Echelle grating that is indicated by the respective green rectangle.  
Figure 11: Grating coupler fabrication by a two-step silicon etching. (**a**) The first Si etching. Inset: the hard mask (SiO2) on top of the 70 nm Si patterns. (**b**) Lithography of the second Si etching. Inset: the photoresist mask for second Si etching. (**c**) The second Si etching. Inset: two-steps Si structure.  
Figure 10: (**a**,**b**) Typical optical and SEM micrographs of our directional couplers. (**c**–**e**) Low sidewall roughness has been achieved in both straight waveguides (**d**) and bends (**c**,**e**).
During recent years, the full missions of multiple low Earth orbiting (LEO) satellite IR radiometers have been reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system [18; 19; 20], and multi-year data sets of satellite SSTs (ACSPO RANs) and the corresponding matchup data sets (MDS) have been accumulated for a period from September 1981-present. In this study, we explore their potential use for adjusting the DRC thresholds, validation of the QC results, and retrospective evaluation of the \(i\)Quam data.
## 2 Theory: Rank-1 Polarimetric Contrast Enhancement for Ship Detection  
The general formulation employed in this work is detailed in [31], in which the polarimetric coherency matrix formalism was used. In this study, we aim at assessing the performance for quad-pol and dual-pol data, so we have adapted the expressions to the polarimetric covariance matrix, since the formulation in the dual-pol case is easier with the covariance matrix than with the coherency matrix. It is important to note that the final results for the quad-pol case are numerically equivalent, regardless of the matrix formalism, as the two matrices can be converted into each other by using an unitary transformation.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 3 Simulation Validation
### Damage Detection with Decision-Level Fusion Model  
As described in Section 2.4, the results obtained from the single PNN models can provide subset information for the decision-level data-fusion model. After the PDFs for each pattern class at the test sample point were obtained, four fusion models were chosen to fuse the identification results of classifiers 1, 2 and 3 from the single PNN classifier models by Equation (5), and then the fused class-conditional PDFs for each pattern were attained, the final damage decision results of test samples were made by Equation (6) finally. As all training samples were set as the training patterns (link weights) for single-PNN classifiers, they were all correctly identified for all three classifiers in the training phase. Thus \(w_{q,j}\) was set to 0.5 for models 1, 2 and 3, and 0.333 for fusion model 4. Table 4 showed the results of damage detection with three decision-level fusion models.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Noise** \\ **Level \(\varepsilon\)** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Eigen Fusion** \\ **Method** \\ \end{tabular} } & \multicolumn{3}{c}{**IA of Testing Samples for Each Pattern (\%)**} & \multicolumn{1}{c}{**Total IA**} & \multicolumn{1}{c}{
\begin{tabular}{c} **Average** \\ **of Total IA (\%)** \\ \end{tabular} } \\ \cline{3-8}  & & & **Pattern 1** & **Pattern 2** & **Pattern 3** & **Pattern 4** & **(\%)** & **88.7** \\ \hline \multirow{4}{*}{1} & 1 (NC1 + NC2) & 82 & 94 & 96 & 71 & 85.75 & \multirow{4}{*}{86.9} \\  & 2 (NC2 + NC3) & 88 & 98 & 96 & 78 & 90 & 88.7 \\ \cline{1-1}  & 3 (NC1 + NC3) & 87 & 97 & 93 & 84 & 90.25 & \multirow{4}{*}{86.9} \\ \cline{1-1}  & 1 (NC1 + NC2) & 85 & 88 & 86 & 83 & 85.5 & \\ \cline{1-1} \cline{2-8}
2 & 2 (NC2 + NC3) & 81 & 86 & 90 & 81 & 84.5 & \\ \cline{1-1}  & 3 (NC1 + NC3) & 86 & 93 & 96 & 88 & 90.75 & \multirow{4}{*}{73.8} \\ \cline{1-1}  & 1 (NC1 + NC2) & 26 & 89 & 82 & 75 & 68 & \\ \cline{1-1} \cline{2-8}
3 & 2 (NC2 + NC3) & 57 & 90 & 87 & 82 & 79 & 73.8 \\ \cline{1-1}  & 3 (NC1 + NC3) & 33 & 92 & 91 & 81 & 74.25 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identification results of eigen-level fusion model.  
It is observed that all four decision-level multi-classifier data fusion models perform very well in detecting damage, and the average IA for all four fusion models exceeds 82% when \(\varepsilon\leq 0.2\%\). The decision-level data-fusion model is superior to the single PNN classifier model in the damage detection of a structure owing to the data-fusion processing of redundant and complementary information. Just as expected, the IAs for all four fusion models decrease with the increase of noise level. In addition, there is no apparent change in total IAs for four different fusion models, namely, fusion models 1, 2, 3 and 4, as shown in Table 4. The fact shows that all four decision-level data-fusion models are feasible and effective in damage detection of a structure under noise conditions.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 5 Experimental Validation
### Experiments on UR5e Robot  
#### 5.2.1 Experiment 1: Setup  
In the first experiment, tests were conducted on a UR5e robot from Universal Robots. A flexible part was grasped by the end-effector of the robot, which was required to perform a linear transfer with a moving distance of 0.55 m, as shown in Figure 7. The part is made of polypropylene, and the technical drawing in Figure 8 shows its principal dimensions. These physical properties affect the dominant mode of the part. The part with the sensor weighs approximately 0.4 kg and is mounted perpendicular to the direction of motion. The starting and ending positions of the end-effector were (80, 600, 0) mm and (80, 600, 550) mm with the orientation fixed to (\(\pi\), \(-\pi/2\), 0) rad using RPY representation. The velocity and acceleration constraints are defined as: \(V_{\mathrm{max}}\) = 0.65 m/s, \(A_{\mathrm{max}}\) = 2.4 m/s\({}^{2}\). The high-speed motion of the robot can excite significant vibrations in the flexible part along the \(z\)-axis direction. To measure the vibration response of the part, an ICM-20602 accelerometer from TDK InvenSense was attached to the tip of the part. The acceleration signal was recordedby the accelerometer with a sampling rate of 500 Hz. The settling time is defined as the duration from when the movement starts until the part's residual vibration remains consistently within \(\pm\)2 m/s\({}^{2}\). Using the impact hammer test, the flexible part is approximately characterized by a natural frequency of 5.58 Hz and a damping ratio of 0.01.
## 3 Results and Discussion  
### Brake Thermal Efficiency  
The term \"brake thermal efficiency\" (BTE) pertains to the ratio of power input supplied to the engine to the power output generated for braking purposes. Figure 3 depicts the brake thermal efficiency (BTE) at various injection timings ranging from 17.5 to 27.5\({}^{\circ}\) before top dead centre (b TDC) under full load engine conditions (10 kg) [19; 20]. The BTE values are shown for multiple fuel compositions, including 100% diesel (D100), 20% waste-plastic oil biofuel (DP20), 5% hydrogen blended with 20% waste-plastic oil biofuel (DP20H10), 10% hydrogen blended with 20% waste-plastic oil biofuel (DP20H10), and 15% hydrogen blended with 20% waste-plastic oil biofuel (DP20H15). Typically, the brake thermal efficiency (BTE) exhibits an increase in correspondence with the augmentation of the engine load. This implies that the engine exhibits improved performance in conditions of increased load [21]. The graphical representation indicates that the brake thermal efficiency (BTE) of the DP20H5, DP20H10, and DP20H15 engines surpasses that of the D100 engine across all tested situations. According to previous research findings, the addition of 15% hydrogen to a mix of waste-plastic oil biofuel and diesel fuel has been shown to result in the greatest brake thermal efficiency (BTE) of 37.08%. In comparison, the maximum BTE achieved by using pure D100 fuel is 30.75%, indicating a significant increase of 17.07% in BTE at a specific crank angle of 23.5\({}^{\circ}\) before top dead centre (TDC). When subjected to maximum operational capacity, the use of a hydrogen blend comprising 5% of the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Limits** & **Uncertainty** \\ \hline BTE & \(\pm\)2.0\% \\ SFC & \(\pm\)2.0\% \\ HRR & \(\pm\)1.0\% \\ CO\({}_{2}\) & \(\pm\)0.2\% \\ CO & \(\pm\)0.2\% \\ O\({}_{2}\) & \(\pm\)0.15\% \\ HC & \(\pm\)0.15\% \\ NO & \(\pm\)0.2\% \\ Smoke meter & \(\pm\)1.0\% \\ Temperature sensor & \(\pm\)0.5\% \\ Pressure sensor & \(\pm\)1.0\% \\ Speed indicator & \(\pm\)0.2\% \\ Load indicator & \(\pm\)0.2\% \\ Encoder & \(\pm\)0.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Uncertainty.  
overall fuel mixture with diesel fuel resulted in the smallest rise in brake thermal efficiency (BTE), measuring around 11.1%. The experimental findings indicated that the hydrogen sample exhibited reduced stiffness and density, leading to improved engine performance, which was attributed to enhanced atomization [22; 23]. Additionally, the introduction of hydrogen into diesel fuel resulted in an enhancement of the mixing ratio, hence facilitating a more thorough combustion process due to its accelerated evaporation rate 19, 20. The present research demonstrates the use of hydrogen in the combustion process. This study determined that the brake thermal efficiency (BTE) values were 30.75% for diesel, 30.9% for DP20, 34.5% for DP20H5, 36.4% for DP20H10, and 37.08% for DP20H15 when measured at 23.5\({}^{\circ}\) b TDC at full engine load conditions.
# Numerical Analysis of EOD Helmet under Blast Load Events Using Human Head Model
## 2 Numerical Model  
The finite element numerical model developed in this study consisted of an EOD helmet on a complete human head model, whereby it is possible to analyze the head response and defensive performance under explosive loads.  
For this work, LS-Dyna finite element software is used. In this section, the different parts that compound the numerical model are described, as mechanical properties of each one, mesh description, and boundary conditions. It is worth mentioning that the human head model used in this work has been previously validated in other works [19; 20].
# Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
## 2 Dimensionality Reduction
### Feature Selection Algorithms
#### 2.3.3 Equilibrium Optimizer  
The Equilibrium Optimizer (EO) is an optimization algorithm, inspired by control volume mass balance models to estimate both dynamic and equilibrium states. It was first proposed by [PERSON] and [PERSON] in 2020 [43]. In EO, all particles (solutions) function as a search agent according to their concentration (position). These search agents adjust their concentration at random relative to the best-performing solutions (also known as equilibrium candidates) towards the equilibrium state (ideal outcome) [43; 44]. EO has demonstrated the ability to randomly update the solution within a good balance between exploration and exploitation [45].  
The key steps involved in EO are the following [43; 44; 45]:  
* Step 1: An initial population of particles is randomly set up within the search space.
* Step 2: Each particle is evaluated using a fitness function, which assesses its performance and determines its suitability as an equilibrium candidate.  
* Step 3: A selection of the top solutions from the current population is created based on their fitness values. These solutions are considered as equilibrium candidates and are expected to be nearer to the optimal solution.
* Step 4: For each candidate in the population (excluding the equilibrium candidates), their position is updated based on a randomly weighted influence of the equilibrium candidates from the equilibrium pool. This process helps to cover the entire search space and avoid local optima.
* Step 5: Whether any updated candidate solutions have gone out of the predefined bounds of the search space is checked. If so, boundary handling rules are applied to bring them back within valid limits.
* Step 6: Steps 2-5 are repeated until a stopping criterion is fulfilled, such as reaching a maximum number of iterations or attaining a satisfactory solution.
# Dual-Polarization Observations of Slowly Varying Solar Emissions from a Mobile X-Band Radar
## 4 Results  
This section presents the results obtained in 2016 when the X-band radar was deployed at the Payerne airport in Switzerland. The data set consists of 57 daily radar estimates, retrieved from 2 February to 30 September during days without precipitation. Such estimates are compared with the X-band transformed (see Section 2.2) DRAO reference values. As stated in Section 2.1, DRAO acquires accurate measurements of the Sun three times a day. Our \"smoothed\" reference has been derived, for each day presented in this section, by averaging the five closest-in-time DRAO values. It is worth noting that the variability of these five values is so small that the use of a decibel scale with two digits after the decimal point renders the \"log of the mean\" indistinguishable from the \"mean of the log\" for all of the analyzed days. Such a two-digit average of DRAO flux values is expressed in dBsfu: the values are listed in the 4 th column in Table 11 (Appendix A), and are followed by a three-digit standard deviation, calculated using the 5 values on the logarithmic decibel scale. The first columns in Table 11 indicate the Julian day number in 2016, starting from day 33 (2 February) and ending on day 274 (30 September). The other four columns show the values retrieved using the horizontal and vertical channels for the nominal acquired values and the (estimated-)noise subtraction.
## 4 Case Studies
### Corrective Voltage Control
#### 4.1.2 Case Study 2  
In the second case study, the control objectives were extended to regulate both the voltage profile and transmission losses so that the RL agent could mitigate voltage violations and reduce transmission losses simultaneously. The RL agent training and testing performance is shown in Figure 4. Compared to Case Study 1, the second RL agent takes longer to control both voltage profiles and transmission losses. In the testing phase, all voltage  
Figure 3: Performance of RL agent for regulating voltage profiles.  
violations are fixed successfully. More importantly, the average transmission loss reduction is around 1%, demonstrating the proposed method's effectiveness when simultaneously considering multiple objectives. The loss values of Pi, V, Q1, and Q2 within the SAC agent are plotted in Figure 5 during the training phase, where the \(y\)-axis represents loss values and \(x\)-axis represents the training episodes.
## 2 Results and Discussion
### Organoleptic Evaluation  
The acceptability of turbid samples during superchilling storage depends upon the changes in their organoleptic characteristics. Figure 5 displays organoleptic evaluation results for the turbid samples including odor, color, mucus, elasticity and tissue morphology during superchilling storage. At the beginning, all turbid samples had high organoleptic scores and they were of excellent quality, and then a significant quality loss in all turbid samples was observed (\(p<0.05\)) during storage. However, MAP treated samples had significantly higher organoleptic scores than those of the AP and VP treated samples (\(p<0.05\)). The AP, VP, MAP3, and MAP4 treated samples were considered unacceptable by the panelist at day 18, day 21, day 25 and day 23, respectively, due to off-odor and loose elasticity. No significant difference (\(p>0.05\)) was detected between MAP3 and MAP4. The best organoleptic evaluations were reported in MAP1 and MAP2, however the mucus and odor qualities of MAP1 were unacceptable at day 27. The organoleptic evaluation results were also supported by TVC growth, TVB-N and K values, which were also certified in some researches [34].  
Figure 4: Changes in total viable counts (TVC) of samples under different treatments during storage period (AP: air packing; VP: vacuum packing; MAP1:70% CO\({}_{2}\)/30% N\({}_{2}\); MAP2: 60% CO\({}_{2}\)/10% O\({}_{2}\)/30% N\({}_{2}\); MAP3: 50% CO\({}_{2}\)/15% O\({}_{2}\)/35% N\({}_{2}\); MAP4: 55% CO\({}_{2}\)/5% O\({}_{2}\)/40% N\({}_{2}\)).
## 5 Conclusions  
Two deep neural network-based ROMs are proposed to predict the heat transfer of nanofluid in absorber tubes with various fin heights, widths, and positions. Both network models contain several repeated convolutional blocks to extract the features of the SDF presented problem geometry. Then, for field prediction, the deconvolutional blocks are connected to expand the highly encoded features to the original size, while for heat flux prediction, the deconvolutional blocks are replaced by two fully connected layers. The temperature field and the heat flux of the outlet cross section are estimated using thetwo neural network-based ROMs, respectively, to test their performance. The results can be drawn as follows:  
* The temperature field of the outlet cross section of the absorber tube is reconstructed with high accuracy, more than 99.9%. Moreover, the computational speed is four orders faster than using the numerical simulation.
* For the estimation of heat flux at the outlet, various sizes of datasets are examined. The 2000-data-point dataset achieves the highest accuracy, more than 99.7%, and the determination coefficient R\({}^{2}\) of 81 samples is higher than 0.9995. Furthermore, each prediction of outlet heat flux takes very little time, only about 0.004 s, which is five orders faster than the CFD solver in OpenFOAM, which is about 400 s for each simulation.
* The strategy of learning rate decreasing is applied to provide a relatively high computational speed at the beginning of the training to reduce the time cost, and at the same time, a relatively small learning rate value at the end of training facilitates the convergence of the solution. Moreover, through the hyperparameter analysis, the initial learning rate of \(5\times 10^{-7}\) is confirmed as the optimum choice.  
In conclusion, the proposed network ROMs in the study enable the accurate and fast prediction of the temperature field and heat flux of nanofluid-filled absorber tube with various fin heights, widths, and positions, which show their huge potential for the fin-shape design and optimization of nanofluid-filled absorber tube.  
Conceptualization, Z.-F.Z.; methodology, Y.H. ([PERSON]) and C.-H.Y.; software, C.-H.Y.; writing--original draft preparation, Y.H. ([PERSON]); visualization, J.-Z.P.; writing--review and editing, W.-T.W., Y.H. ([PERSON]) and Z.-F.Z.; supervision, Y.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Jiangsu Province (BK20201302, BK2020954) and the Fundamental Research Funds for the Central Universities (No. 30919011401).  
The financial support from the Natural Science Foundation of Jiangsu Province (BK20201302, BK2020954) and the Fundamental Research Funds for the Central Universities (No. 30919011401) are gratefully acknowledged by the corresponding authors.  
The authors declare no conflict of interest.
###### Abstract  
This study investigates the mechanical properties of LiCoO\({}_{2}\) (LCO) cathode materials under varying states of charge (SOCs) using both an empirical Buckingham potential model and a machine learning-based Deep Potential (DP) model. The results reveal a substantial decrease in [PERSON]'s modulus with decreasing SOC. Analysis of stress factors identified pairwise interactions, particularly those involving Co\({}^{3+}\) and Co\({}^{4+}\), as key drivers of this mechanical evolution. The DP model demonstrated superior performance by providing consistent and reliable predictions reflected in a smooth and monotonic stiffness decrease with SOC, in contrast to the large fluctuations observed in the classical Buckingham potential results. The study further identifies the increasing dominance of Co\({}^{4+}\) interactions at low SOCs as a contributor to localized stress concentrations, which may accelerate crack initiation and mechanical degradation. These findings underscore the DP model's capability to capture SOC-dependent mechanical behavior accurately, establishing it as a robust tool for modeling battery materials. Moreover, the calculated SOC-dependent mechanical properties can serve as critical input for continuum-scale models, improving their predictive capability for chemo-mechanical behavior and degradation processes. This integrated multiscale modeling approach can offer valuable insights for developing strategies to enhance the durability and performance of lithium-ion battery materials.  
LiCoO\({}_{2}\) (LCO); mechanical properties; state of charge (SOC); deep potential (DP) model; lithium-ion batteries +
Footnote †: journal: Applied Sciences