esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results, Analysis, and Evaluation
### Computation Time and Analysis  
We tested the proposed algorithm with a computer Intel Core i7-CPU 3.5 GHz; RAM-8 GigaBytes; Operating System: Windows 7 Microsoft-64 bits; Visual Studio 2013-C++. The computation time of the proposed algorithm was dependent on the number of facets in each model. With the test models in Table 1, computation time ranged from 17.5 to 17,486 ms with \(|M|\in[544,79162]\). Based on Table 1, we conclude that, if the number of facets is small, the computation time will be short.  
[PERSON] did not provide computation time. He only explained that his method will loop through all of the indices of the set of elements and will permute and swap at each iteration. As a result, the encryption time is expected to scale linearly with the complexity of the object. In addition, the decryption process requires swap operations to be performed in the reverse order than the one used for encryption. As a result, the decryption process is more \"expensive\" than encryption. We found that the encryption process of [PERSON]'s method was twice as long as our method and that the decryption process of [PERSON]'s method was three times as long. In our method, the encryption time and decryption time are similar. Moreover, the average computation time (including encryption and decryption times) of [PERSON]'s method is 1.25 times greater than the average computation time of the proposed method. In [PERSON]'s method, he only explained and analyzed factors affecting computation time. The computation time of [PERSON]'s method depends on how long it takes for the valid CAD model to check the time of feature encryption and the time of edge and facet encryption. He concluded that his method will meet users' requirements. We estimate that, with a computer has the same parameters, [PERSON]'s method must compute three parameters: valid model checking, feature encryption, and edges & facet encryption. We were able to estimate that the computation time of [PERSON]'s method is at least twice as long as our method. Therefore, we concluded that our method is faster than [PERSON]'s method as well. Figure 8 shows the computation time of the proposed algorithm and [PERSON]'s method with the test model in Table 1, according to the number of facets.
## 3 Experimental Results
### Training and Test  
During training, the learning rate was set to 0.001 and the batch size to 64. These hyperparameters were selected based on preliminary experiments to ensure stable convergence across both input types. Figure 10 shows the model accuracy on both the training and test sets. The solid line represents the model trained with Mel-spectrograms, while the dashed line represents the model trained with raw seismic data. The model trained with Mel-spectrograms consistently outperforms the one trained with raw data across all epochs. This performance gap suggests that Mel-spectrograms provide a more structured  
Figure 9: Samples from the training set: (**a**) weak noise traces, (**b**) strong noise traces. Purple clusters represent the signal’s energy distribution.  
and informative representation, allowing the model to extract discriminative features more effectively. Training with raw signals is also less stable, as indicated by the noticeable fluctuations in the red dashed line. These instabilities may stem from the complexity of raw seismic waveforms, which increase the difficulty of model optimization. In contrast, the Mel-spectrogram-based model achieves higher and more consistent accuracy on the test set, demonstrating superior generalization capability and robustness. This highlights the advantage of the model under a limited training set, where it is impossible to fully cover all possible noise conditions.  
Table 1 shows the accuracy of both models on the test dataset. The model trained with Mel-spectrograms performs better overall, especially in detecting strong-noise traces. Single-trace normalization was applied during training to stabilize the learning process, as the wide amplitude range in seismic data can otherwise cause the model to diverge. However, normalization also reduces the difference in amplitude between normal and abnormal traces, making them harder to separate. Mel-spectrograms provide more information, including frequency and attenuation, not just amplitude. These extra features help the model detect anomalies more effectively. As a result, the Mel-spectrogram model shows higher accuracy, especially for strong-noise traces. Figure 11 shows the identification results of the test data. As illustrated in Figure 11, the Mel-spectrogram-based model produces fewer errors in these weak-noise traces, as indicated by the greater number of green traces. Both models successfully identify most noise traces, but the model trained with Mel-spectrograms achieves higher overall accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Type** & **Accuracy Rate** & **Overall Accuracy Rate** \\ \hline Mel-spectrogram & Weak noise & 90.2\% & \\  & Strong noise & 82.5\% & \\ Raw seismic trace & Weak noise & 94.7\% & \\  & Strong noise & 93.1\% & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model accuracy on test data.  
Figure 10: Accuracy curves during model training.
# A Fast Recognition Method for Dynamic Blasting Fragmentation Based on YOLOv8 and Binocular Vision
## 4 Discussion  
### Comparison of Fragmentation Recognition Results  
As shown in Figure 16, by performing mask segmentation on the same set of rock fragment images using both commercial software and the proposed model, the proposed model accurately identifies rock fragment boundaries in the original images and achieves high-coverage segmentation. Leveraging an advanced image segmentation algorithm, the system covers nearly all rock particles, exhibiting a low miss rate and clear edge detection, thereby enhancing analytical completeness and accuracy. In contrast to the grayscale-based edge detection used in commercial software, the proposed model uses colored masks to partition different rock fragments, with each fragment labeled in a unique color. This makes the output more visually intuitive and aids rapid assessment of fragment size and spatial distribution.  
Figure 14: Normalized confusion matrix.  
Figure 15: Model training and optimization process: (**a**) loss function curves; (**b**) evaluation metric curves.  
Cumulative fragmentation size is a key metric in blasting fragmentation recognition and blast performance evaluation, as it intuitively reflects the overall characteristics of fragment size distribution. To validate the accuracy and stability of the proposed model, images from the same source were processed using both the proposed model and commercial software, and their respective cumulative fragmentation curves were extracted for comparative analysis. The comparison results are shown in Figure 17. The two cumulative curves exhibit similar trends and a high degree of overall fit, indicating strong consistency in particle size recognition between the two methods. The difference in P80 values was reduced to only 3 mm, demonstrating that the proposed model possesses stable and reliable recognition and size prediction capabilities. It is noteworthy that the starting points of the two cumulative size distribution curves show some differences. This difference mainly results from the variation in recognition methods: the commercial software manually delineates fine particles (fines) in the image, while the model proposed in this study uses a fully automated algorithm without manual intervention. This has little impact on the overall particle size analysis and does not affect the model's high accuracy in real-world applications. In summary, the model proposed in this paper achieves recognition accuracy comparable to the mature commercial software, while also performing well in terms of automation and multi-image processing consistency, providing a useful tool for evaluating blasting performance in mining.  
Figure 16: Comparison of fragmentation recognition results: (**a**) original image; (**b**) commercial software; (**c**) proposed model.  
Figure 17: Comparison of cumulative fragmentation curves.
# A Data-Driven Battery Degradation Estimation Method for Low-Earth-Orbit (LEO) Satellites
## 2 Background on LEO Satellite Power System and Methods  
### KOMPSAT-5 Satellite System  
The Korea Multi-Purpose Satellite (KOMPSAT) series is designed for high-resolution Earth observation and a variety of civilian and public services. Among the satellites in this series, KOMPSAT-5, launched in August 2013, is capable of radar observation. It is equipped with a 1 m resolution high-resolution radar, enabling it to perform observations under various weather conditions.  
The power systems of LEO satellites are categorized into photovoltaic (PV) systems, which are responsible for power generation; power converters, which handle power transformation; and battery systems, which meet power demands during eclipse periods or when power generation exceeds consumption [13]. The PV system, which is designed for power generation, produces a variable power output depending on temperature and sunlight intensity. Additionally, changes in temperature affect the short circuit current and open circuit voltage, which, in turn, alter the maximum power point of the system.  
The power generated by the solar panels of the satellite is converted into primary power through a primary power converter. The output of the primary converter serves multiple purposes, including powering loads with built-in power conversion and loads with a wide input voltage range and acting as an input for secondary power conversion. Consequently, the output voltage of primary power conversion is influenced by factors such as the battery's state of charge (SOC), the temperature of the solar panels, the amount of power generated, and fluctuations in load demand.  
Next, a secondary power conversion system is included to supply a suitable voltage to loads with specific input voltage requirements or without built-in power conversion devices. The secondary power converter may include additional low-voltage transformers as needed, with design values varying based on the specific load requirements. Furthermore, to handle loads exceeding the available power during eclipse periods and mission operations, the battery is designed to meet the daily charge-discharge energy balance, maximum discharge limit, and average discharge limit conditions. The maximum and average discharge limits are typically provided by battery manufacturers to ensure good performance throughout a satellite's lifespan. Adhering to these limits is critical for preventing rapid battery degradation.  
Most LEO satellites perform battery charging and discharging based on the power generated and consumed in conjunction with the output of the primary power converter. In the KOMPSAT-5 satellite battery system analyzed in this study, illustrated in Figure 1, the current is not directly controlled by a power converter. Consequently, the battery's charge current and voltage are influenced by variations in the SOC, the temperature of the solar panels, and the solar angle relative to the panels.
## 6 Thermal Property of the Control and xGNP Modified Asphalt Models  
### Thermal Conductivity Measurement Methods and Results  
The thermal conductivity is a kind of measure of materials to transmit the heat energy in a diffusive manner based on [PERSON]'s law. The materials with a high thermal conductivity are applied to the heat sink, and the materials with a low thermal conductivity are manufactured for thermal insulation. In the laboratory, the xGNP modified asphalt was mixed with ultrasonic stirring during the process of high shear so that the xGNP particles can be homogenously dispersed in the asphalt matrix. The thermal properties' analyzer (KD2 Pro) was employed to measure the thermal conductivity of asphalt based on the transient line heat source method [45]. The asphalt was placed in the glass tube as shown in Figure 9a,b, and the single needle TR-1, with a 2.4 mm diameter and 60 mm length, was used to test the thermal conductivity. During the heating and cooling processes, the temperature-time relationship is monitored by the sensor located in the needle. The thermal conductivity is calculated with the parameters from the fitted curve for temperature-time. The formula for thermal conductivity is shown in Equation (8). The thermal conductivities of asphalt under different temperatures in the laboratory are shown in Figure 9c.  
\[\begin{split} T=m_{0}+m_{2}t+m_{3}Int\ (\text{Heating process})\\ T=m_{1}+m_{2}t+m_{3}ln\frac{t}{(t-h_{i})}\ (\text{Cooling process})\ \text{and}\ k=\frac{q}{4\pi m_{3}}\end{split} \tag{8}\]  
where \(m_{0}\) and \(m_{1}\) are the ambient temperatures in the heating and cooling processes, respectively; \(m_{2}\) is the rate of drift of the background temperature; \(m_{3}\) is the slope of a line relating temperature rise to the logarithm of temperature; \(q\) is the heat input and \(k\) is the thermal conductivity.  
Figure 9: _Cont._Figure 9a,b display the KD2 Pro thermal conductivity apparatus and the temperature control chamber used in the laboratory, respectively. Figure 9c demonstrates the thermal conductivity results tested in the laboratory. The thermal conductivity of the control asphalt binder at room temperature is 0.148 W/m\(\cdot\)K, and it is close to the reference data of asphalt [22] (0.170 W/m\(\cdot\)K). The thermal conductivity of the xGNP modified asphalt binder at room temperature is 0.226 W/m\(\cdot\)K, and it is near to the reference data [22] from 0.396 W/m\(\cdot\)K to 0.934 W/m\(\cdot\)K with different amounts of graphite (different types from the multi-layer graphite used in this study). Through the test results, the addition of xGNP particle increases the thermal conductivity of the asphalt binder. It is likely that high thermal conductivity of xGNP particles (around 3000 W/m\(\cdot\)K) enhances the thermal transfer in the asphalt binder matrix based on the thermal conductivity data of xGNP particles from xgsciences.com [46]. It can also be expected that the light absorption is improved after the addition of xGNP particles in the asphalt binder.
## 5 Conclusions  
The WSA, consisting of a variable number of IMU units and the master unit communicating via UWB, was designed and implemented. A suitable firmware for 100-Hz real-time data transfer from each IMU to the master unit, allowing energy-efficient operation for 6 h or several days in idle mode, was developed and verified. Data loss when sending IMU sensor data from five IMU units in the test network to the master unit and then to the USB and PC was successfully measured and analysed. The conclusion is that the highest packet loss, 5% for the left lower leg IMU unit, was found at the master position near the pelvis at the bottom of the backpack. The less error prone placement for the master unit was on the top of the backpack, with less than 0.02% packet loss for all the IMU units attached to the pelvis, right/left thigh, and right/left lower leg. Most readings were in the range of 0.005% to 0.02%, which is 1 packet loss in 10,000 samples (or 1 packet in 100 s), which is acceptable. As shown in Section 2.2 (Figure 4) and Section 3.1 (Figure 5), synchronization at 100 Hz is strictly guaranteed.  
The packet loss when using WSA while walking on a treadmill, in a public gym, in an urban street environment, and in a laboratory test is less than 0.05%, with one exception. The same version of the WSA was successfully tested in a certified laboratory as a Class-2A biomedical device according to the relevant guidelines. Subsequently, the same WSA, together with algorithms for motion phase detection, was also reliably used in clinical trials with wearable robots and prostheses as part of the CL ++ H2020 project. Some results have already been published in the literature.  
As expected for the presence of Wi-Fi signals, we did not detect any occurrences of WSA data packet loss associated with the propagation of 2.5-GHz and 5-GHz Wi-Fi bands. There were rare cases of packet loss that could not be associated with Wi-Fi signals in a public space.  
Overall, the WSA with IMU units and UWB has proven to be robust and highly reliable. Finally, two potential problems could arise in some cases. The currently unsecured communication might need to be updated by implementing AES256 security in the future. Second, if the currently unused or infrequently used RF bands from 3.5 GHz to 6.5 GHz gain more users, the interference problems might also require more attention.  
Conceptualization, M.M.; Formal analysis, Z.I.; Funding acquisition, M.M.; Methodology, M.M. and Z.I.; Project administration, M.M.; Software, Z.I.; Supervision, M.M.; Validation, Z.I.; Writing--original draft, M.M. and Z.I.; Writing--review & editing, M.M. and R.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission under the CYBERLEGs project (Grant #287894) within the Seventh Framework Program (FP7-ICT-2011-7), the CYBERLEGs Plus Plus project (Grant #731931), within the H2020 framework (H2020-ICT-25-2016-2017) and the financial support from the Slovenian Research Agency (research core funding No. P2-0228).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Object Detection Application Domains
### Other Domains  
Other fields include smart homes, event detection, rain detection, species detection, and visually-impaired person assistance systems. [PERSON] et al. [232] proposed indoor object detection of specific classes for blind and visually-impaired persons using a Deep CNN RetinaNet framework, [PERSON] et al. [233] proposed DEEP-SEE to detect, track, and recognize in real time, objects during navigation in an outdoor environment for visually-impaired persons. [PERSON] et al. [234] implemented the CNN model to address rain detection. [PERSON] et al. [235] implemented a deep CNN model for shadow detection. [PERSON] et al. [236] presented an \"event detection framework to dispose of multi-domain data\". [PERSON] et al. [237] implemented computer vision-based assistive technology for helping visually-impaired and blind people using Deep CNN. [PERSON] et al. [238] proposed, with Faster RCNN, a vision-based system for monitoring elderly people at home.
## 4 Experimental Validation and Discussion
### Experimental Setup and Results Analysis
The experimental PSD results are shown in Figure 15, where PA\({}_{2}\) with CMUDPD exhibits superior performance due to operating in the identical mode as PA\({}_{1}\). It can be concluded from Figure 15 that PA\({}_{2}\), PA\({}_{3}\), and PA\({}_{4}\) with CMUDPD exhibit satisfactory results considering the manufacturing tolerance of practical PA products.
## 2 Materials and Methods
### Amyloid Beta 1-42 (A\(\beta_{1\text{-}42}\)) Oligomer Preparation and Treatment  
Biotinyl-Amyloid \(\beta\) Protein (A\(\beta_{1\text{-}42}\)) was purchased (Bachem, Bubendorf, Switzerland, 4038795) and stored in desiccated containers at \(-80\)\({}^{\circ}\)C. Before use, the peptide was allowed to equilibrate to room temperature for at least 30 min to avoid condensation upon opening the vial [25]. A 1 mM of Biotinyl-Amyloid \(\beta\) Protein (A\(\beta_{1\text{-}42}\)) treatment solution was prepared in the fume hood by dissolving 0.5 mg of A\(\beta_{1\text{-}42}\) in 105.5 \(\upmu\)L of 100% 1,1,1,3,3,3-Hexafluoro-2-propanol (HFIP) (ThermoFisher, 147541000) [25; 26]. The clear solution containing 1 mM of dissolved A\(\beta_{1\text{-}42}\) was then aliquoted in 0.6 \(\upmu\)L low-retention microcentrifuge tubes (ThermoFisher, 3449). The samples were left in the fume hood overnight to allow HFIP to evaporate and were left 1 h in the desiccator to promote evaporation of any remaining HFIP. Then, the subsequent clear A\(\beta_{1\text{-}42}\) film was resuspended in sterile DMSO (Sigma-Aldrich, 08418) by gently scraping up and down until the peptide film was completely dissolved [26]. The peptide solution was mildly vortexed for 30 s, followed by 5 min of sonication in an ultrasonic bath cleaner (Branson 2510). Twenty-four hours before treatment, A\(\beta_{1\text{-}42}\) solutions were diluted in EGM-2 medium to the final concentrations of 10 \(\upmu\)M, 20 \(\upmu\)M, and 30 \(\upmu\)M, respectively. A 5-day culture of endothelial cells in the microfluidic device was treated with A\(\beta_{1\text{-}42}\) solutions for 48 h. The following design was used throughout the experiments: (1) BBB-No A\(\beta\) (BBB without A\(\beta\)), (2) ECM Only + 20 \(\upmu\)M (A\(\beta\) without BBB), and (3) 10 \(\upmu\)M, 20 \(\upmu\)M, and 30 \(\upmu\)M (A\(\beta\) with BBB). It is important to note that ECM was injected into the gel channel in all conditions.  
Figure 1: The schematic diagram for the construction of the 3D BBB system in the microfluidic device. (**A**) The schematic structure of a microfluidic chip and the representative design depicting the observation window. (**B**) Experimental procedure for the construction of a 3D neurovascular/brain compartment. (**C**) The incubation of a microfluidic device in a dynamic perfusion that allows bidirectional fluid flow. (**D**) Schematic illustration of the expected 3D reconstruction of the endothelial-cell-based vascular barrier (tunable perfusion).