esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Convolutional Neural Networks: Methodology and Advances (Invited)
## 2 Development of Optical Convolution Neural Network
### Optical CNN Based on Tunable Optical Attenuation of Coherent Light  
The last category of optical CNNs is generally based on the tunable attenuation of coherent light. In this architecture, the data matrix and the weight matrix are represented by the intensities of light waves, and two cascaded intensity modulations are used to load the input data and weights to accomplish the dot-product operation in the optical domain. In addition, optical-electrical conversion is achieved on each optical path, and the sum operation is conducted in the electrical domain.  
As shown in Figure 8a [112], two acousto-optical modulator (AOM) arrays were utilized, with one for data loading and the other for kernel weighing. A single laser was employed as the coherent light and divided into several equal power beams. Then, the light waves passed through two acousto-optical modulator arrays and converted the optical power into electrical voltage at the PD array. Subsequently, a switching array decided whether the voltages were accumulated positively or negatively to realize real-valued computing. The proof-of-concept experiment was verified with a recognition accuracy of 98.9% on the MNIST dataset and 91.5% on the Fashion-MNIST dataset. P-doped-intrinsic-N-doped (PIN) current-controlled attenuators have also been reported to adjust the optical power for the weight matrix (Figure 8b) [113]. The PIN junction works in the forward bias state, and the optical power is tuned by changing the injected carriers. The input data are also represented by the optical power, and the linear dot-product process is realized with tunable optical attenuation. The addition operation is conducted in the electrical domain on the PD, and nonlinear activation is accomplished with an on-chip microring modulator. An integrated end-to-end photonic deep neural network (PDNN) with one convolutional layer and two fully connected layers was experimentally demonstrated. In this architecture, through the design of the physical connection mode of the photoelectric link, convolution and full connection were realized by the combination of photoelectricity, and nonlinearity was realized by using a microring modulator. The classification results can be directly generated at the output port of the integrated chip. The printed letters were demonstratedwith a two-class classification accuracy of 93.8% and a four-class classification accuracy of 89.8%. In addition, serial dot-product coupled with integrated balanced homodyne detection was also proposed [114]. The VVM was completed in each optical channel and the parallel multiple channels enabled the VMM operation. In addition, other elements, such as MRR and MZI, have also been reported as tunable optical attenuators to realize optical CNNs [88,101].
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Antarctica Project
#### 3.5.4 Measurement Techniques: Waveforms Used  
The Antarctic project performed the characterisation of the ionospheric channel with narrowband sounding to measure the SNR and the availability throughout a campaign and wideband sounding to measure the Doppler spread and the multipath delay spread through the scattering function. The soundings were performed during 24 h a day for a frequency carrier ranging from 2 to 30 MHz with steps of 500 kHz which corresponds to 57 sub-sessions. The frequency sampling of the band base signal is 100 ksps.  
The narrowband sounding frame (see Figure 11a) starts with a synchronization word, three PN m-sequence of 255 chips, then 2 s of silence followed by a transmission of a 10 s narrowband carrier and followed by 2 s of an idle slot again, as shown in Figure 11a. The idle periods are used in the receiver to measure the SNR and availability factor [26].  
The wideband sounding frame (see Figure 11b) starts with a synchronization word, three PN m-sequences of 255 chips [32]. 5100 ms of wideband data is transmitted in this use case, using a PN m-sequence of 255 chips, obtaining a total of 200 consecutive sequences with a bandwidth of 10 kHz, from which the system derives the channel response matrix (see Figure 12a) and afterwards the scattering function (see Figure 12b). From these two functions the multipath delay spread (see Figure 12c) and the Doppler spread (see Figure 12d) of the channel will be calculated. Other characteristics, such as the SNR and even the number of paths could also be computed. The characteristics of the wideband sounding are summarized in Table 4.  
Figure 11: (**a**) Narrowband frame definition and (**b**) wideband frame definition.  
Figure 12: Wideband channel response for February 1 st 2014 at 07 UTC with 13 MHz of carrier frequency: (**a**) normalized channel response \(h[n,\tau]\), (**b**) normalized scattering function \(R_{s}[\tau,v]\), (**c**) multipath power profile (in ms), (**d**) Doppler power profile (in Hz).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Waveform** & **Chip-rate** & **Delay** & **Multipath** & **Doppler** & **No. of** & **Doppler** & **Measure** & **Processing** \\
**BPSK** & **(kchip/s)** & **(ms)** & **Res. (us)** & **(Hz)** & **CIRs** & **Res. (Hz)** & **Time (s)** & **Gain (dB)** \\ \hline
255 m-sequence & 10 & 25.5 & \(\sim\)100 & \(\pm\)19.61 & 200 & 0.196 & 5.1 & 47 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of the wideband sounding waveform of the Antarctica project
## 3 Results and Discussion
### Nutritional Composition of Soybean Seeds  
The proximate composition of raw soybean seeds harvested from both agronomic systems during 2021-2024 is presented in Table 4.  
Crude protein (CP) levels remained relatively stable, ranging from 32.00% to 34.12%, with no statistically significant differences observed between the P1 (semi-organic) and P2 (conventional) treatments (\(p>0.05\)). These results are consistent with prior multi-site evaluations of non-GMO cultivars under Central Polish conditions, where the protein content for 'Abaca' ranged from 39.6% to 42.8% depending on seasonal hydrothermal conditions [23]. In the present study, lower protein levels may reflect partial drought exposure during key vegetative phases, especially in 2022. Oil content (CO) exhibited minor inter-annual variation, ranging from 18.65% to 20.11%. While oil levels tended to be slightly higher under P1 management in certain years (e.g., 2022 and 2024), differences were generally non-significant. These oil concentrations confirm the suitability of 'Abaca' for both full-fat soy processing and oil extraction, as also supported by prior cultivar screenings [23]. Interestingly, our earlier research highlighted that oil content in soybean can be sensitive to hydrothermal variability, with drier seasons sometimes yielding elevated oil concentrations due to altered seed filling dynamics. Moisture content at harvest ranged from 11.78% to 13.37%, reflecting proper harvest timing and consistent post-harvest drying. A small but statistically significant moisture reduction was observed in P2 during 2024 (\(p<0.05\)), potentially linked to field microclimate variability and plant maturity differences. Bulk density values ranged between 65.52 and 71.13 kg hL\({}^{-1}\), with slightly higher values under P1 conditions, suggesting improved kernel development in mechanically weeded plots. Bulk density is an important indicator for storage stability and processing efficiency, with values above 68 kg hL\({}^{-1}\) generally meeting industrial processing standards [24; 25]. Seed admixture content ranged from 0.66% to 1.15%. The slightly elevated admixture levels observed under P1 conditions may reflect minor mechanical seed coat damage induced by rotary hoeing and the absence of chemical desiccation prior to harvest [10]. Despite these differences, all admixture levels remained well within commercial acceptance limits for food-grade soybeans [26]. Taken together, these findings confirm that both cultivation systems maintained high compositional stability of soybean seeds. Importantly, the semi-organic P1 approach preserved seed quality while offering potential agroecological benefits, in line with EU
## 3 Numerical Results
### Deformation Characteristics under Different Compression Rates
#### 3.3.3 Deformation Characteristics of Node \(\mathrm{D_{e}}\)  
This section mainly displays the radial displacement and equivalent stress of the \(\mathrm{D_{e}}\) node under different compression rates in the experimental results. Figure 8 shows the equivalent stress and radial displacement curves of node \(\mathrm{D_{e}}\) under different compression ratios (10-15%) at a linear velocity of 320 mm/s.  
Ignoring the influence of instantaneous fluctuations due to the compression of the loading plate, the curve in Figure 8b with a 15% compression rate reached its minimum value at 189.19\({}^{\circ}\). As the compression rate decreased, the time when the corresponding curve reached its minimum value gradually advanced, and the curve with a 10% compression rate reached its minimum value at 180.74\({}^{\circ}\). Affected by the Ae and Be nodes, the \(\mathrm{D_{e}}\) node was displaced away from the roller axis near \(\mathrm{A_{e}}\)-C and \(\mathrm{B_{e}}\)-R. From the partially enlarged image, it can be seen that before the roller rotated to about 142\({}^{\circ}\), as the compression rate increased, the radial displacement corresponding to each rotation angle of the \(\mathrm{D_{e}}\) node also increased. After the curve interleaved near 142\({}^{\circ}\), the situation was opposite, and the radial displacement curve with a higher compression rate was lower than the radial displacement curve with a lower compression. In the locally enlarged image, it can also be seen that there were small oscillations at all times in the curve. The higher the compression rate, the smaller the amplitude of the small oscillations.  
From Figure 8a, it can be seen that before \(\mathrm{B_{e}}\)-R, the greater the compression rate at each rotation angle, the greater the equivalent stress at the \(\mathrm{D_{e}}\) node. After \(\mathrm{B_{e}}\)-R, the situation was the opposite; the higher the compression rate, the smaller the equivalent stress. Then, in the later stage of the same rotation cycle, the equivalent stress approached zero. The equivalent stress of the \(\mathrm{D_{e}}\) node peaked at about 180\({}^{\circ}\) and was greatly affected by compression rate. The peak stress at a 15% compression rate was 230.97 MPa, which was 207.9% of the  
Figure 8: Equivalent stress and radial displacement changes of \(\mathrm{D_{e}}\) nodes under different compression ratios (10%–15%): (**a**) equivalent stress and (**b**) radial displacement.  
peak stress at a 10% compression rate. In addition, as the compression rate decreased, the magnitude of the decrease in the equivalent stress peak of the \(\rm D_{e}\) node also decreased.
## 3 Analysis of Numerical Simulation Results
### Crack Propagation Process
#### 3.2.1 Influence of Different Pore Percentages on Rock Failure Modes
During the crack propagation process, there is almost no obvious main crack direction, showing an all-round expansion trend and forming an extremely complex fragmentation mode. Compared with other schemes, the rock failure in this scheme is the most serious, almost completely losing its original bearing capacity. This shows that the increase in pore percentage greatly changes the failure mode of the rock from a relatively simple fracture to overall fragmentation, and the failure process is more rapid and violent. In summary, as the pore percentage increases, the rock failure mode gradually changes from a relatively simple main crack propagation to a complex multi-crack network propagation and overall fragmentation mode. The interaction between pores and cracks becomes stronger. The influence of pores on the crack propagation path changes from a local small deflection to a comprehensive guidance and obstruction, resulting in frequent branching and coalescence of cracks and, finally, significantly reducing the strength and stability of the rock and changing its failure mechanism.
## 4 Conclusions  
We have demonstrated a novel fiber optic sensor probe with a pair of highly reflected connectors and a vessel of water absorption material, WCS, for water leak detection. The bending loss of the optical fiber was studied to confirm the sensor response due to water absorption. The sensing output is calculated to obtain the response between the reflected light signals of two connectors and the optical fiber bending loss by the WCS expansion due to water absorption. Also, the dynamic range of each sensor probe can be determined by considering the bending loss of each sensor probe due to WCS expansion. In order to show the sensor characteristics of the novel fiber sensor probes, we fabricated three types of probes (A, B, and C type) and investigated the sensitivity of each probe. The large vessel probes (A, B type) provide consistent responses having sensitivity of about 0.233-0.267 dB/mL while the smallest one shows a relatively low sensitivity of 0.067 dB/mL. In conclusion, by managing the sensitivity of the probe by controlling the bending loss mechanism with WCS and the dynamic range of the probe by controlling the connector reflectivity, we can determine the number of sensors and total optical fiber length in order to design an optimal fiber optic sensor system to meet sensing requirements in the leak detection field.
# Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
## 4 Results
### Privacy Findings: Was Sensitive Information Leaked into the Synthetic Records?
#### 4.3.2 User Study
**Identifiers were copied. ** For example, the same name(s) appeared in both documents. Unless contextual information was replicated, participants often disagreed on the severity of a potential privacy leak. 3. **The synthetic document acted as continuation of the real document with linked information**. Counterarguments to the existence of a privacy breach included inconsistencies in synthetic text that made it appear clearly fake (see Question 2) and generic content that made it hard to say whether a description was about the same person or not. There were two examples in which participants agreed on a privacy breach. These contained specific descriptions of a diagnosis or situation that seemed unique enough to lead back to a person (e.g., someone dying soon, if in a non-dying population) and were copied from the original to a large extent. Interestingly, while the incoherence of certain synthetic text often added as protective factor for privacy, the effect may be reversed when a part of text is clearly fake and another part is clearly real, making it possible for a potential attacker to easily pick out copied information. The findings of the privacy evaluation can be summarized as follows:  
* In free text, the removal of PHI may not be sufficient to protect privacy when specific and rare events are described in detail. * The mediocre quality of synthetic text often acted as protective factor by obfuscating what is real and what is fake. * The largest cause of concern for privacy in this synthetic corpus is the existence of larger chunks of text that were copied from the real data, especially when rare events were described.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 5 Discussion  
In this work, both linear models indicated better performance than proximity-based algorithms, as expected. As for the proximity-based algorithms, the CB-LOF attained the best results. It had a relatively good F1-score compared to the rest of the proximity group. The performance differences can be explained by the characteristics of linear models that create a boundary function between classes. In contrast, the proximity-based algorithms use the mean value as a center point for a clustered class. In the dataset, the features within each class prefer a specific boundary function rather than clustered classes. Therefore, our dataset favors linear models.  
The F1-score represents the ability of a specific algorithm to classify data points. The score is influenced by how each model approaches the problem. On the other side of the coin, the inference time is directly associated with the computational effort required by the model. Algorithm improvement (e.g., simplified code) can accelerate the model inference regarding the target system constraint or limitation. This improvement also directly impacts power consumption, as the faster the inference, the less power consumed. Further, the model size correlates with the number of training data used in the ML modelling. It varies between the models, but the proximity-based algorithms require more memory to calculate a specific number of neighboring or surrounding data.  
In the future, we plan to extend the results of this study in two aspects: First is the battery system analysis, another critical system included in the EPS. The scope of this preliminary work was limited to the solar panel system only. Second, this study examined the ML approach algorithm for semi-sequential types of data points. Therefore, it will be essential to investigate another approach: full-time series analysis. Finally, from the results of this study, we have favorable model candidates to be developed for analytical tools of ground station telemetry data and onboard system model development.  
Accordingly, the future system implementation will be developed simultaneously with the CubeSat system. The model has to be adjusted with every satellite sensor calibration process to determine any hyperparameter within the ML models or pre-processing techniques which significantly influences the model's sensitivity to the anomalies. Therefore, the final embedded ML-based anomaly detection has the expected nominal operation in the ground-tested flight model. Ultimately, it could provide critical information immediately after the satellite's deployment and early operation. For example, by having an onboard autonomous anomaly detector, the CubeSat might be able to broadcast its initial condition by CW beacon without the need for the operational team to perform a long traditional analysis of the telemetry data, which potentially takes weeks after the deployment.
## 1 Introduction
The background data sources predominantly included various versions of the ecoinvent database, GREET 2021, the GaBi database, and information from the literature. Across the reviewed studies, a consistent functional unit of 1 kg of hydrogen (H\({}_{2}\)) was applied, with some cases specifying additional parameters, such as purity levels and pressure conditions. All studies adopted cradle-to-gate system boundaries, ensuring a comparable system scope. In terms of impact assessment, a range of LCIA methods was applied, including ReCiPe (2016), IPCC 2013 GWP (100a), CML 2001, and EF 3.0 [21], although some studies did not explicitly report the method used. Overall, this methodological review reveals considerable variability in data sources and impact assessment tools, yet it demonstrates consistent application of system boundaries and functional unit definitions in hydrogen-related LCA studies. Although extensive research has examined the environmental impacts of hydrogen production from biomass, there remains a notable gap in integrating socio-economic and techno-economic dimensions. Key cost indicators, such as CAPEX, OPEX, and LCOH, are often excluded. This limits the practical assessment of hydrogen technologies' real-world sustainability. To address this, the present study performs a holistic sustainability assessment that combines environmental LCA with economic evaluation for two biomass-based hydrogen pathways: biogas reforming and agricultural residue gasification. Using a cradle-to-gate boundary and a functional unit of 1 kg H\({}_{2}\), environmental impacts are assessed across 18 categories via SimaPro 9.4 and the ReCiPe Midpoint (H) method, drawing from the literature and Ecoinvent 3.1 data. This study aims to compare the environmental and economic performance of both pathways. It hypothesizes that agricultural residue gasification will show environmental advantages due to its carbon-neutral feedstock, albeit with higher water use and capital costs. This integrated analysis provides insights for guiding sustainable hydrogen deployment across different regional contexts.
## 4 Test Results and Discussion
### Strain Measurement  
A total of 14 strain gauges were attached to the specimen surface to elucidate the overall behavior. Strain gauges labeled 1 to 4 were attached to the upper flange of the center of the beam, of which ST1 and ST2 were attached 10 cm away from the load point in the beam direction, and ST3 and ST4 were attached to the center of the connector where the bearing block was located. Figure 9a shows that the strain rate of ST2 changed rapidly owing to yield at the yield rotation angle and subsequent flange buckling, whereas, in ST1, ST2, ST3 and ST4 were attached to the center of the connector.  
Figure 8: After testing the speedBridge. (**a**) Whole specimen; (**b**) Near pin connection; (**c**) Male connector; (**d**) Female connector; (**e**) Pin and connectors.  
no significant strain was observed during the experiment. This is presumed to be the effect of the torsion of the specimen in the process of manufacturing and loading the specimen. In the case of the upper flange of the beam at the upper part of the bearing block, yield strain was observed. However, as buckling was prevented by the side stiffeners of the block, no strain could exceed the yield strain.  
Strain gauges ST5 to ST12 were attached to both sides of the female connectors among the pin plates, precisely at the bottom of the connectors. Notably, gauges could not be attached to the male connectors because these connectors were located between the female connectors and were not invisible. Figure 9b indicates that the elastic state was maintained such that the strains at none of the measured points reached 0.2%. Specifically, in the cases  
Figure 9: Strain measurements. (**a**) Top flange; (**b**) Pin plate; (**c**) Bottom flange.  
of ST5, ST9, and ST11, which were subjected to tension, the strains did not reach 0.1% and remained in the elastic state. In the cases of ST6 and ST10, which were expected to concentrate bearing stress, the strains did not reach the maximum value of 0.15%. Notably, ST8 and ST12, which were expected to transmit stress through tension, also exhibited negligible levels of strain. However, for ST5 and ST9, which were arranged on opposite sides, although the points were the same, the sizes were slightly different. This is because of the slight asymmetrical behavior exhibited owing to manufacturing errors. In the case of ST7, a sudden drop appeared at approximately three times the yield strain angle. Considering that no noticeable change was observed according to other strain values, the gauge was damaged.  
Figure 9c shows the measurements from the strain gauges attached to the bottom flange, in which the ordinate has a logarithmic scale for readability. Herein, the bottom flange of the beam was deformed, even after yielding. Therefore, the stress was effectively transferred from the pin of the connector. A difference was observed in the post-yield region between the values of the two gauges. As confirmed by the abovementioned data, this difference was caused by the torsion of the specimen owing to manufacturing errors, etc.