esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Prototype System and Methods  
### System Overview  
The prototype sensor network consists of sensor stations, deployed along the runway or taxiway to be monitored. Sensor stations have Velodyne's LiDAR sensors and a GPS antenna, rigidly attached to the column of a standard airport light fixture. Besides georeferencing, GPS provides accurate timing for the LiDAR sensors using the 1 PPS (pulse per second) and NMEA (National Marine Electronic Association) GPS signals. In addition, each sensor station has a data logging component, deployed farther away from the runway or taxiway for safety reasons.  
The adjustable sensor configuration allows for various sensor alignments. For example, Figure 1a,b show horizontally and vertically aligned VLP-16 sensors, respectively, where \(Z\) is the rotation axis and \(\kappa\) is the horizontal rotation angle or azimuth ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\) with 0.1-0.3\({}^{\circ}\) angular resolution (\(\Delta\kappa\)). The vertical and horizontal alignments mean that the sensor's rotation plane is either perpendicular or parallel to the horizontal plane, respectively. The narrower field of view (\(\delta\)) is 30\({}^{\circ}\) and 41.3\({}^{\circ}\) with 1.33\({}^{\circ}\) and 2\({}^{\circ}\) angular resolution (\(\Delta\delta\)) for the VLP-16 and HDL-32E systems, respectively. In an installation shown in Figure 1c, the left side shows two vertically aligned VLP-16s and one horizontally aligned HDL-32E scanner, while the right side shows two vertically aligned VLP-16 scanners.  
The alignment of the sensors was optimized to cover the largest field of view to capture the maximum number of points from the tracked object for a given number of scanners. A simulation study on this subject was conducted using Blender (see [12; 20]). Since the Velodyne's VLP-16 sensors have a smaller vertical field of view with coarser angular resolution, therefore these sensors are aligned vertically. While with this arrangement, the horizontal coverage of the runway is shorter, yet more points can be captured from the aircraft body. HDL-32E LiDAR is installed horizontally, due to its better vertical angular resolution and larger field of view. The minimum distance at the airport where our tests were conducted was specified as 20 m and 30 m next to taxiways and runways, respectively.  
The two sensor stations 40 m apart from each other provides an effective coverage of the runway of about 70 m (see Figure 1d).  
Figure 1: Sensor system overview: (**a**) horizontally and (**b**) vertically aligned VLP-16 sensor, (**c**) components of the demonstration sensor network, and (**d**) sensor coverage.
# A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers
## 3 Methods and Modeling  
Distribution and power transformers operate in multifarious environments that makes them susceptible to generic failures whose consequence is prolonged episodes of power outages and disrupted economic activity [PERSON] et al. (2019). Unlike overhead conductors that are easy to troubleshoot and repair, transformers are factory sealed thus depriving engineers and technicians the opportunity to inspect their internal components. The onset of failures of in-service transformers occasion substantial loss of returns to power utility companies apart from the costly reparation or replacement expenses, and the risk of explosion or fire. Dissolved gas analysis is the only technique available that makes it possible to detect incipient transformer failures [PERSON] et al. (2019). Although the measurement accuracy of DGA techniques is relatively high, the techniques used to interpret DGA results remains reliant on the expertise of technical personnel rather than on systematic formulation [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2019). This study therefore seeks to propose and implement a new machine-learning-based DGA interpretation technique that interprets DGA results accurately particularly when dealing with multinomial data.
## 2 Method
### Evaluation  
To measure the performance of the algorithms, we used two indices, the accuracy rate and the information translate rate (ITR) [24; 37] to compare the proposed PCA-CNN with other CNN algorithms in the literature. The accuracy can evaluate the effectiveness of the algorithm. The accuracy rate in the article is the number of correct classified characters classified compared with the number of total actual test characters. The calculation formula is as follows:  
\[T_{acc}=P_{n}/S_{n} \tag{16}\]  
where \(T_{acc}\) is the accuracy rate of character recognitions, \(P\) is the number of correct detected characters, \(n\) is the number of repeats, and \(S\) is the number of total characters.  
ITR can display the recognition speed of the test characters by the classification algorithm in bits per minute. The formula is as follows:  
\[\Pi\text{TR}=\frac{60(P\log_{2}(P)+(1-P)\log_{2}((1-P)/(N-1))+\log_{2}(N))}{T} \tag{17}\]  
where \(N\) represents the number of classes, \(P\) represents the accuracy rate of character recognitions, and \(T\) represents the time taken for character recognitions [24; 28].  
In dataset I, as each flash lasts for 100 ms followed by a pause of 75 ms (\(12*(75+100)=2100\)) and a pause of 2.5 s between each character epoch, \(T\) can thus be defined as:  
\[T=2.5+2.1n \tag{18}\]  
where \(n\) is the number of repeats, \(1\leq n\leq 15\).  
In dataset II, as each flash lasts for 100 ms followed by a pause of 30 ms (\(40*(30+100)=5200\)) and a pause of 1.2 s between each character epoch, so \(T\) can be defined as:  
\[T=1.2+5.2n \tag{19}\]  
where \(n\) is the number of repeats, \(1\leq n\leq 10\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Layers** & **Input** & **Convolution Kernel and Operation** & **Output** & **Activation Function** \\ \hline L1 & 1*30*80 & None & 1*30*80 & None \\ \hline L2 & 1*30*80 & 20*30*1 & 20*1*80 & ReLU \\ \hline  & 20*1*80 & 16*1*5 & 16*1*18 & \\ L3 & 20*1*80 & 16*1*8 & 16*1*10 & ReLU \\  & 20*1*80 & 16*1*10 & 16*1*8 & \\ \hline  & 16*1*18 & & & \\ L4 & 16*1*10 & Concat and DP & 16*1*36 & ReLU \\  & 16*1*8 & & & \\ \hline  & 16*1*36 & 16*1*2 & 16*1*18 & \\ L5 & 16*1*36 & 16*1*4 & 16*1*9 & ReLU \\  & 16*1*36 & 16*1*9 & 16*14 & \\ \hline  & 16*1*18 & & & \\ L6 & 16*1*9 & Concat and DP & 16*1*31 & ReLU \\  & 16*1*4 & & & \\ \hline L7 & 16*1*31 & Maxpool & 16*1*15 & ReLU \\ \hline L8 & 16*1*15 & FC & 100*1 & ReLU \\ \hline L9 & 100*1 & Softmax & 2*1 & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 2: The network parameter settings for dataset II.
## 7 Conclusions, Challenges, and Future Research
### Future Work  
In the future, research should focus on extending the MRSO's application to a wider range of optimization challenges, particularly those involving large-scale, real-world engineering problems. Doing so would help confirm how adaptable and scalable the MRSO is when faced with more complex, computationally demanding tasks, providing deeper insights into how well it can generalize beyond the current tests.  
Another exciting area for future work is developing adaptive mechanisms that allow the MRSO to adjust its parameters automatically, depending on the specific problem it is tackling. This would not only make the MRSO more flexible but would also reduce the need for manual tuning, making it an even more powerful tool across a broader variety of optimization tasks.  
Additionally, incorporating surrogate models or other techniques to lower computational costs could make the MRSO more efficient in solving high-dimensional problems. Since real-world scenarios often require a balance between computational resources and solution quality, integrating methods that improve the MRSO's efficiency could make it more practical for large-scale industry applications. Lastly, future studies should include a wider variety of metaheuristic algorithms and configurations, which will provide a clearer understanding of the MRSO's strengths and highlight areas where it could be further improved.  
Conceptualization, A.A.A.; methodology, A.A.A. and H.S.A.; software, A.A.A., H.S.A., I.A.M., and T.A.R.; validation, A.A.A. and T.A.R.; formal analysis, S.I.S. and I.A.M.; investigation, A.A.A.; resources, A.A.A., H.S.A., S.I.S., I.A.M., and T.A.R.; data curation, A.A.A.; writing--original draft preparation, A.A.A.; writing--review and editing, H.S.A., S.I.S., I.A.M., and T.A.R.; visualization, A.A.A.; supervision, A.A.A.A.; project administration, A.A.A.; funding acquisition, H.S.A., S.I.S., and I.A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The MATLAB code for the Modified Rat Swarm Optimization (MRSO) algorithm used in this study is available at ([[https://www.mathworks.com/matlabcentral/fileexchange/172930-modified-rat-swarm-optimization-mrso-algorithm](https://www.mathworks.com/matlabcentral/fileexchange/172930-modified-rat-swarm-optimization-mrso-algorithm)]([https://www.mathworks.com/matlabcentral/fileexchange/172930-modified-rat-swarm-optimization-mrso-algorithm](https://www.mathworks.com/matlabcentral/fileexchange/172930-modified-rat-swarm-optimization-mrso-algorithm))).  
The authors declare no conflicts of interest.
# Additive Manufacturing of Ceramic Reference Spheres by Stereolithography (SLA)
###### Abstract  
Additive Manufacturing (AM) is advancing technologically towards the production of components for high-demand mechanical applications with stringent dimensional accuracy, leveraging metallic and ceramic raw materials. The AM process for ceramic components, known as Ultraviolet Laser Stereolithography (SLA), enables the fabrication of unique parts or small batches without substantial investments in molds and dies, and avoids the problems associated with traditional manufacturing, which involves multiple stages and final machining for precision. This study addresses the need to produce reference elements or targets for metrological applications, including verification, adjustment, or calibration of 3D scanners and mid- to high-range optical sensors. Precision spheres are a primary geometry in this context due to their straightforward mathematical definition, facilitating rapid and accurate error detection in equipment. Our objective is to exploit this novel SLA process along with the advantageous optical properties of technical ceramics (such as being white, matte, lightweight, and corrosion-resistant) to materialize these reference objects. Specifically, this work involves the fabrication of alumina hemispheres using SLA. The manufacturing process incorporates four design variables (wall thickness, support shape, fill type, and orientation) and one manufacturing variable (the arrangement of spheres on the printing tray). To evaluate the impact of the design variables, dimensional and geometric parameters (GD&T), including diameters, form errors, and their distribution on the surface of the sphere, have been characterized. These measurements are conducted with high accuracy using a Coordinate Measuring Machine (CMM). The study also examines the influence of these variables in the dimensional and geometric accuracy of the spheres. Correlations between various parameters were identified, specifically highlighting critical factors affecting process precision, such as the position of the piece on the print tray and the wall thickness value. The smallest diameter errors were recorded at the outermost positions of the tray (rear and front), while the smallest shape errors were found at the central position, in both cases with errors in the range of tens of micrometers. In any case, the smallest deformations were observed with the highest wall thickness (2 mm).  
additive manufacturing; stereolithography; ceramics; alumina; reference spheres +
Footnote †: journal: Applied Sciences
# Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot
## 3 Experimental Results and Discussion
### Comparison Experiments
This indicates that the proposed method is individual independent. (3) The lack of significant differences between the trends of each joint during different motions means that the proposed method is valid for relatively complex motion. (4) The data of the hip joint was shown to be more volatile than the knee and ankle joints, but no subversive data appeared. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Squat**} & \multicolumn{4}{c}{**Run**} & \multicolumn{4}{c}{**Jump**} \\ \cline{3-11}  & & **Hip** & **Knee** & **Ankle** & **Hip** & **Knee** & **Ankle** & **Hip** & **Knee** & **Ankle** \\ \hline \multirow{2}{*}{S1} & Left & 25.2 & 8 & 1.4 & 14.4 & 12.3 & 2.5 & 29.4 & 14.8 & 4.4 \\  & Right & 22.5 & 7.8 & 1.1 & 15 & 6.9 & 2.2 & 30.1 & 15.8 & 5.5 \\ \hline \multirow{2}{*}{S2} & Left & 6.8 & 5.8 & 1.1 & 21.1 & 6.9 & 2.3 & 15.5 & 9 & 2.1 \\  & Right & 7.1 & 6.1 & 1.2 & 24.9 & 6.9 & 2.7 & 14.2 & 9.4 & 2.2 \\ \hline \multirow{2}{*}{S3} & Left & 7.3 & 2.9 & 1 & 10.7 & 6.1 & 1.2 & 6.8 & 6.2 & 1.5 \\  & Right & 10.3 & 3.2 & 0.3 & 16.5 & 3.7 & 1.3 & 7.3 & 6.2 & 1.6 \\ \hline \multirow{2}{*}{S4} & Left & 12 & 2.5 & 0.7 & 14.3 & 11.9 & 3.9 & 12.6 & 5.2 & 1.3 \\  & Right & 14.8 & 2.8 & 1.9 & 13.3 & 10.5 & 5.3 & 14 & 5 & 3.7 \\ \hline \multirow{2}{*}{S5} & Left & 16.1 & 2.3 & 0.8 & 6.3 & 5.8 & 5 & 16.8 & 6.8 & 2.4 \\  & Right & 18 & 3.6 & 1.5 & 5.3 & 3.2 & 5.5 & 16.8 & 5.4 & 4.1 \\ \hline \multirow{2}{*}{Mean SD} & 14 & 4.5 & 1.1 & 14.2 & 7.4 & 3.2 & 16.4 & 8.4 & 2.9 \\  & 6.5 & 2.2 & 0.5 & 6 & 3.2 & 1.6 & 7.9 & 3.9 & 1.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Root-mean-square error (RMSE) of the torque from the exoskeleton and the motion analysis system (MAS). Figure 13: Average joint torques of each motion (repeated 30 times) of Subject 1. (**a**) hip torque in squatting; (**b**) knee torque in squatting; (**c**) ankle torque in squatting; (**d**) hip torque in running; (**e**) knee torque in running; (**f**) ankle torque in running; (**g**) hip torque in jumping; (**h**) knee torque in jumping; (**i**) ankle torque in jumping. The result from the exoskeleton is shown in orange, and the result from the motion analysis system (MAS) is shown in blue. Shaded regions show \(\pm 1\) SD.
# General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
## 4 Experiment and Results  
The experiments in this study were all conducted on a Linux system, using an RTX 3090 GPU. The proposed detection network was implemented on the PyCharm platform based on the PyTorch deep learning framework. The learning rate was set to 0.00001, and the BatchSize and number of training epochs varied depending on the feature extraction network used. During training, the Adam optimizer was employed to optimize the objective function, which combines momentum optimization and adaptive learning rate strategies to provide an adaptive learning rate for each parameter. Other experimental parameters were set to their default values, and the model with the highest accuracy on the validation set was saved as the final model.  
After data augmentation, the Raman spectroscopy dataset consisted of 50,000 (500 \(\times\) 100) Raman spectral data points, where 500 represents the number of sample categories and 100 indicates the number of spectra for each category. Of the 500 sample types, positive samples (containing food additives) and negative samples (not containing food additives) accounted for half each. The dataset was presented in the form of spectral pairs, with each pair consisting of data for a food additive and the data for a substance to be tested, and three-fold cross-validation was employed.  
Each spectral pair had two labels: the first label indicated the presence of the food additive in the substance being tested, where 0 meant present and 1 also meant present (likely an error in the original text, needs clarification); the second label represented the concentration of the food additive in the substance, divided into 10 levels (0-9) based on a gradient from 0 to 1, indicating different concentrations. The input one-dimensional Raman spectral data for the upper and lower branches of the training were both of size 1 \(\times\) 2048, corresponding to the Raman spectra of the food additive and the substance being tested, respectively. The loss function used during training was the cross-entropy loss function.
## 4 Results and Discussion
### Effects of Self-Phase Modulation on the Beam Profile  
To investigate the influence of SPM on the beam profile, we use the nearly perfect Gaussian shaped (M\({}^{2}\) \(\sim\)1.05) third harmonic of a Yb:KYW laser (JenLas D2.fs JenOptik GmbH) as input. Pulses with an energy of 310 nJ and a duration of 173 fs are focused with a fused silica lens (f = 50 mm) into a 3 mm thick Sapphire disk. In analogy to the experiments in section 4, the disk is moved relative to the focus along the beam propagation to change the peak intensity and thus the amount of SPM. The beam profile is measured after a second recollimation lens.  
When the disk is placed in the focus, we observe strong spectral broadening but severe beam profile distortions as the self-focus lies inside the crystal (see Figure 7b,f). The ring pattern is caused by the interference of spatial variations of self-phase modulation, as explained in refs. [30,31]. Moving the crystal away from the focus significantly reduces the distortions while the spectral broadening is still respectable. Already 2 mm after the focus a symmetric beam profile is obtained with a broadening that reduces the Fourier limit from 133 fs to 31 fs (see Figure 7d,h). The outer rings in the beam profile can be cut off with an aperture and the inner part then has a beam profile that is comparable with a  
Figure 6: Pulse compression of sub-30 fs UV pulses generated by SHG (**a**, **b**) and SFG (**c**, **d**) of visible noncollinear parametric amplifier (NOPA) pulses by SPM broadening in a 2 mm (**a**, **b**) or 1 mm (**c**, **d**) CaF\({}_{2}\) crystal. Dotted lines: input pulses; solid lines: spectrally broadened pulses. Compression to a flat spectral and temporal phase (dashed lines) was achieved by the use of an acousto-optic pulse shaper and measured by a ZAP-SPIDER.  
Gaussian beam. The analysis of the measured intensity profile (pink rings in Figure 7a-d) shows that this typically reduces the usable pulse energy to 30%.
# AUV Trajectory Planning for Optimized Sensor Data Collection in Internet of Underwater Things
## 2 Materials and Methods
### Proposed Solution
#### 2.3.1 AUV Trajectory Optimization Algorithm
For each such DP state, we introduce an upper bound function \(\Gamma_{\mathrm{up}}(i,\;q)\), which estimates the maximum residual VoI achievable from the remaining CHs \(s(i+1),s(i+2),\ldots,s(M)\), under ideal travel conditions and assuming no information decay. The adopted upper bound is given by:  
\[\Gamma_{\mathrm{up}}(i,\;q)=\sum_{q=i+1}^{M}\mathcal{V}_{int_{s(i)}}. \tag{21}\]  
Assuming instant access with no decay, this optimistic estimate in (21) safely bounds the remaining value and avoids early pruning. Let \(V_{\mathrm{rotolab}}\) denote the global lower bound, defined as the maximum total residual VoI from any fully evaluated trajectory. At each DP stage, the pruning condition is applied as:  
\[J(i,q)+\Gamma_{\text{up}}(i,\,q)<V_{\text{totalBest}_{\phi}}. \tag{22}\]  
If the condition in (22) holds, then, even under ideal assumptions, the current trajectory cannot outperform the best solution identified so far. In such cases, the algorithm safely prunes the current permutation and skips the remaining stages of its evaluation. This strategy significantly reduces the number of trajectory branches that must be explored, particularly during the later stages of recursion, where the computational cost of full evaluation is highest. As a result, the algorithm achieves a substantial improvement in runtime efficiency while preserving optimality within the reduced search space.
## 2 Survey of Commercial Small Platforms
### Active Microwave
#### 3.3.3 Synthetic Aperture Radar (SAR) Altimeter  
SAR altimeter differs from real aperture radar altimeter (conventional) in that it exploits coherent processing of groups of transmitted pulses, while conventional altimeters is exploited to make the most efficient use of the power reflected from the surface. The SAR altimeter offers many potential improvements over conventional altimetry for measurements, since it increases the resolution and offers multilook processing.  
Currently, three mini-satellites are dedicated to altimetry with SAR processing, such as SARAL, Sentinel-3A, and Sentinel-3B. The planned missions are Sentinel 6 (Jason-CS). Table 12 summarizes the main characteristics of radar altimeters with SAR processing. Typical requirements are similar to the conventional altimeters for mini-platform: 100 W average power consumption, 1.2 m antenna diameter, 63 kg payload mass.  
The geophysical variables of interest to analyze with SAR altimeter are ocean surface currents, significant wave height, dominant wave direction, sea ice cover, sea ice type, sea ice thickness, and horizontal wind speed over the sea surface.
# Study of the Preparation and Dielectric Property of PP/SMA/PVDF Blend Material
## 4 Conclusions  
In this work, the microstructures and dielectric properties of PP/PVDF and PP/SMA/PVDF blends were studied. As indicated by the results of XRD pattern and SEM images, the blend system of PP/PVDF is a two phase structure without the presence of a new phase or the change of crystal structure. PP phase was uniformly distributed in the continuous PVDF phase. SMA can be used as compatibilizer in PP/PVDF blends to modify the properties of blends. The interface between PP and PVDF was absent after the addition of SMA. Additionally, SMA triggered the in-situ reactions at the PP/PVDF interface and produced PP-g-SMA, which decreased the interfacial tension and  
Figure 6: Frequency dependence of tan\(\delta\) for (**a**) PP/PVDF and (**b**) PP/SMA/PVDF.  
promoted the interactions between interfaces, thus realizing the good dielectric frequency stability of PP/PVDF blends. The dielectric constant of PP/SMA/PVDF blends increased with the growing content of PVDF, and it was much greater than that of PP/PVDF blends; meanwhile, the dielectric loss of PP/SMA/PVDF changed slightly with frequency and decreased with PP content, with the value finally stabilizing at around 0.01.  
The research were supported by the National Natural Science Foundation of China (Grant No. 51378073 and 51408048), the Major Program for International Science and Technology Cooperation Projects of Shaanxi province (Grant No. 2016 ZDJC-24), the General and Special Program of the Postdoctoral Science Foundation of China (Grant No. 2016T90880) and the Fundamental Research Funds for the Central Universities of Chang'an university (Grant No. 310821153502).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.