esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR
## 2 Basic Principles of the Scanning System  
The mechanical rotation angle of the MEMS mirror is generally within \(\pm\)5\({}^{\circ}\). According to the principle of plane mirror reflection, when the direction of the incident light remains unchanged and the plane mirror rotates by \(\omega\) degrees, the reflection angle of the light will change by 2\(\omega\) degrees, so after passing through the MEMS mirror, the scanning angle of the beam is usually within \(\pm\)10\({}^{\circ}\), as shown in Figure 1. For LIDAR, especially car-level imaging LIDAR for autonomous driving technology, the scanning field of view requires at least 40\({}^{\circ}\), so special optical design is needed to extend the FOV of MEMS mirror. At present, the commonly used method to extend FOV is adding an optical system with telescope structure, which is to sequentially pass the laser source through a positive lens, a MEMS mirror, and a negative lens. This method has a certain aberration and a large divergence angle. The optical scanning system design in this paper adopts an \(f\)-\(\theta\) lens that often used in laser scanning and laser marking for flat field focusing and uses a rear positive lens group to collimate the laser beam and extend the scanning angle.  
The schematic diagram is shown in Figure 2. H\({}_{\text{i}}\) and H\({}_{\text{i}^{\prime}}\) are object principal plane and image principal plane of the lens groups, respectively. The laser beam is focused by the cemented lens L\({}_{0}\) and completely irradiate into the effective area of the MEMS mirror. After being reflected by the MEMS mirror, the \(f\)-\(\theta\) lens L\({}_{1}\) is used to correct the image surface into a plane and focus the beam. Then the laser passes through the symmetrical cemented  
Figure 1: The relationship between MEMS mechanical angle and the field of view.  
positive lens group L\({}_{2}\), whose object focal plane coincides with the image focal plane of the \(f\)-\(\theta\) lens. So L\({}_{2}\) collimates the scanning beam.  
Assume the focal length of the \(f\)-\(\theta\) lens L\({}_{1}\) is \(f_{1}\)\({}^{\prime}\), and that of the collimating lens L\({}_{2}\) is \(f_{2}\)\({}^{\prime}\), then according to the geometric optics theory, the image height \(h\) of the beam after passing through the \(f\)-\(\theta\) lens can be obtained:  
\[h=f_{1}\)\({}^{\prime}\cdot\tan\gamma \tag{1}\]  
where \(\gamma\) is the scanning field angle of the MEMS mirror. For the collimating positive lens group L\({}_{2}\), its object height \(h\) satisfies:  
\[h=f_{2}\)\({}^{\prime}\cdot\tan\theta \tag{2}\]  
So, the field of view of the scanning system \(\theta\) is:  
\[\theta=\arctan\frac{f_{1}\)\({}^{\prime}\cdot\tan\gamma}{f_{2}\)\({}^{\prime}} \tag{3}\]  
From Equation (3) we can see that if \(f_{1}\)\({}^{\prime}>f_{2}\)\({}^{\prime}\), the system scanning angle will be extended.
## 2 Design Concept of Self-Fitting  
Because the hearing ability, cognitive level and personal habit of each patient are different, each hearing aid is individually fitted and unique. Although the fitting of a hearing aid is similar to that of glasses, the process is more complex and the practitioner requires higher professional skills. Figure 1 shows a comparison of the fitting processes between a traditional hearing aid and a self-fitting hearing aid. From the figure, during the traditional fitting, the audiologist is highly important and should have rich professional knowledge of hearing aids and audiology. Firstly, the audiologist performs an overall assessment for the patient and selects the appropriate equipment and scheme for aural rehabilitation. Secondly, the audiologist receives feedback from the patient and subsequently determines which parameters need to be adjusted. Finally, the audiologist decides on the extent of the adjustment according to experience. Therefore, in a traditional fitting, the audiologist's skills and experiences are the key to the performance of the hearing aid, which is also one of the important factors affecting the popularity of hearing aids in developing countries.  
As shown in Figure 1b, during the fitting process, the self-fitting hearing aid can interact directly with hearing-impaired persons without the participation of audiologists. The optional expert system can partially act as an expert based on the artificial intelligence algorithm. The realized functions include parameter initialization, parameter update and hearing measurement, etc. However, related research is relatively scarce. In addition, for portability considerations, the human-computer interaction tends to be wireless, which requires greater power consumption.  
Most initial hearing aid parameters are calculated according to the prescription formula, and the goal is to obtain the average configuration of the hearing aid according to certain specific parameters [20; 21]. However, individual differences such as age, sex or service life of the hearing aid affect the fitting results, and thus the average approach is biased [22]. Therefore, how to effectively adjust the parameters is quite difficult [23; 24]. Because the basic algorithm for the hearing aid is the multichannel loudness compensation algorithm [25], its parameter fitting is discussed in the following subsections.
## 3 Analysis of Simulation Result
### Effect of Dielectric Constant on Discharge  
In order to explore the influence of the dielectric constant of the insulator on the surface discharge of C4F7N/CO2, this paper compares the surface discharge of C4F7N/CO2 when the dielectric constants are 2, 3, 4, and 5.  
Figure 11 shows the two-dimensional electron concentration distribution when the streamer develops near the cathode under different dielectric constants. As the dielectric constant of the insulator increases, the electron concentration in the streamer near the cathode increases from \(4.63\times 10^{21}\) m\({}^{-3}\) to \(1.16\times 10^{22}\) m\({}^{-3}\). At the same time, the development speed of the streamer becomes faster, and the development time from the needle electrode to the plane electrode becomes shorter, which is consistent with the results in [30].  
Figure 10: Variation of luminous flux with applied DC voltage.  
Figure 15 shows the electric field intensity of three stages of surface discharge in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) and SF\({}_{6}\), where (a), (c), and (e) are the surface discharge in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\), and (b), (d), and (f) are the surface discharge in SF\({}_{6}\). By comparison, the discharge rate of C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) is slightly faster than that of SF\({}_{6}\), and the electric field intensity of the streamer head is also greater than that of SF\({}_{6}\). Figure 16 displays the electron concentrations in the three stages of surface discharge in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) and SF\({}_{6}\). It can be seen that in each stage of surface discharge, the electron concentration in the streamer head in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) is greater than that in SF\({}_{6}\).  
Figure 16: Comparison of electron concentrations for surface discharge in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) and SF\({}_{6}\): (a) 0.45 ns; (b) 0.5 ns; (c) 0.7 ns; (d) 0.8 ns; (e) 2.34 ns; (f) 3.42 ns.  
Figure 15: Comparison of the electric field intensity of surface discharge in C\({}_{4}\)F\({}_{7}\)N/CO\({}_{2}\) and SF\({}_{6}\): (a) 0.45 ns; (b) 0.5 ns; (c) 0.7 ns; (d) 0.8 ns; (e) 2.34 ns; (f) 3.42 ns.  
Table 2 summarizes the data in Figures 15 and 16. It can also be seen from Figures 15 and 16 and Table 2 that the streamer channel in C4F7N/CO2 is more obvious than that in SF6. The streamer channel is formed because electrons are constantly moving towards the anode. The electric field generated by the electrons and cations is opposite to that of the needle electrode, so the electric field inside the streamer channel is small. Because the electron concentration in C4F7N/CO2 is greater than that in SF6, the streamer channel in C4F7N/CO2 is more noticeable than that in SF6.  
Figure 17 shows the photon flux of surface discharge in SF6. When comparing Figures 6 and 17, it is clear that the photon flux in C4F7N/CO2 is greater than that in SF6, regardless of stage 1, stage 2, or stage 3. This demonstrates that optical methods can be used to detect surface discharge in C4F7N/CO2 when it replaces SF6 as the insulating gas for high-voltage electrical equipment in the future.
## 3 Methodology
### The ReMAHA Algorithm
#### 3.2.3 Genetic Algorithm for Generating New Data  
The genetic theory of chromosome inheritance posits that during the formation of chromosomes, each parent contributes an equal half of the total genes to their offspring [37]. The new offspring inherits 50% of their genes from each parent, making them similar to their parents while also possessing unique traits. In this study, sample data are treated as chromosomes, and new data are constructed from these samples. This approach ensures that the newly generated data retain common characteristics while also exhibiting the uniqueness of the original two datasets. Using genetic algorithms guarantees the diversity of generated data and effectively addresses the issues of marginalization of data distribution and data overlap that traditional oversampling methods encounter.  
The formula for generating data using genetic algorithms, as represented in Equation (11), incorporates an influence factor \(\beta\) to preserve the uniqueness of samples. In this study, we set \(\beta\) to 0.5 to a greater extent:  
\[x=\beta a+(1-\beta)b \tag{11}\]  
The letters \(a\) and \(b\) represent the parent samples used for generating data, while '\(x\)' represents the newly generated sample. This means that, in this experiment, since \(\beta\) is equal to 0.5, both parent samples have the same effect on the newly generated sample.
# Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR
## 3 Architecture Exploration for Backprojection Based Video SAR
### Multi-Stream Based BPA on Multiple GPUs  
The proposed architectures can be extended to be scalable to the available number of GPU devices. For the continuous operation of BP-VISAR for streaming phase history data input, we propose a multi-GPU based BP-VISAR architecture as in Figure 6(a), which shows the case of four GPU devices. In each GPU, BP-VISAR operates in Arch. S3-N, and receives the streaming phase history data as input and generates quarter video frames as in Figure 6(b). Because all the GPUs take the same streaming input and perform the range compression on the same data, this computation can be redundant. However, this architecture can remove the bandwidth overhead for the distribution of the range profile data from a GPU performing the range compression to the others. Moreover, this redundant range compression in each GPU does not diminish the benefit of distributing the image formation load to multiple GPUs because the computation time of the range compression is negligible compared to that of the image formation. In the proposed multi-GPU architecture, the frame merge operation at the end of each quarter-frame generation can be removed  
Figure 6: Multiple stream processing of BP-VISAR.  
just by adjusting the target addresses for D2H transfer of quarter-frames with the stride of the quarter-frame size.
# GNSS Jamming Observed on Sounding Rocket Flights from Northern Scandinavia +
## 2 Jamming Observed on Sounding Rocket Flights  
### SOAR Mission  
The SOAR (Single-stage Operational Assessment of Red Kite) rocket vehicle was launched from ASC on 13 November 2023. It was a single-stage rocket and reached an apogee altitude of 71 km. The spin was not stopped and the motor with the attached aerodynamically stabilizing fins attached was not separated after burnout. The rocket and its payload were not recovered but crashed into the Norwegian Sea. The payload was equipped with a wraparound GNSS antenna. Figure 1 shows the C/N\({}_{0}\) ratios and the number of GPS signals tracked by the DLR Phoenix-HD GPS receiver. At lift-off, the number of tracked satellites dropped briefly from nine to five due to the high initial acceleration, but then quickly rose again to twelve satellites. A rather low average C/N\({}_{0}\) ratio of about 41.5 dB-Hz is typical for GNSS signals received with a wraparound antenna on a spinning sounding rocket. At T + 39.4 s, at an altitude of about 36.5 km, the average C/N\({}_{0}\) ratio suddenly fell by about 6.7 dB. Subsequently, the receiver temporarily tracked only four satellites. Near the apogee, the C/N\({}_{0}\) ratio increased again slightly, and the receiver tracked the maximum possible number of twelve GPS signals again. At T + 208.4 s, at an altitude of about 37.2 km, the average C/N\({}_{0}\) ratio had risen back to the original level. Jamming thus affected the signal tracking in the coasting phase above 36.5 km. However, the power of the jamming signal was not high enough, so the receiver continuously tracked four or more signals, providing a navigation solution for the entire flight.
## 4 Carotenoids Analysis in Plant-Based Foods
### Emerging Plant-Based Protein Sources  
New plant-based protein sources, such as microalgae, duckweed, and protein isolates from peas and soy, have gained interest due to their carotenoid-rich composition and potential nutritional benefits. However, analyzing these compounds is challenging due to the complex protein and pigment composition, which can interfere with chromatographic separation [109; 110]. HPLC is widely used for carotenoid analysis in microalgae, particularly in combination with MS [111].  
SFC has also been explored as an alternative for carotenoid extraction from plant-based protein sources due to its shorter run times and lower solvent usage. Advances in chromatographic techniques have enhanced research on carotenoid bioaccessibility, providing insights into the potential health benefits of including these foods in plant-based diets [112].  
Carotenoids are in smaller amounts in plant-based protein isolates such as peas and soy, requiring highly sensitive detection methods. Techniques such as UPLC-MS and HPLC-MS have been beneficial for quantifying trace carotenoid levels in these matrices, supporting the development of fortified plant-based protein products [113; 114].  
Carotenoid quantification in different food matrices requires matrix-specific extraction and analytical strategies to account for variations in lipid content, fiber interactions, and processing effects. While HPLC remains the gold standard, techniques such as UPLC, tandem MS, and SFC are increasingly being used to enhance sensitivity and efficiency. The specific challenges posed by oils, seeds, cereals, and plant-based proteins highlight the importance of adapting chromatographic conditions to improve analytical accuracy. As plant-based diets become more popular, accurate carotenoid measurement and evaluation are increasingly crucial for understanding their functional and nutritional significance. The following sections will discuss the advantages and limitations of these techniques in greater detail, providing a comprehensive understanding of their applications in carotenoid analysis.
## 3 Proposed DVC Framework for Online Correlation Noise Model
### Mathematical Representation of Phase Interpolation for Side Information Generation
#### 3.2.1.2 Phase Difference Calculation Steps  
As stated earlier, in phase interpolation, the motion is represented by a phase shift; therefore, interpolating the phase shift requires the phase difference \(\mathcal{O}_{diff}\) between the phases of two input images, \(\mathcal{O}_{1}\) and \(\mathcal{O}_{2}\) and is computed by Equation (12),  
\[\mathcal{O}_{diff}=\text{atan2}(\sin(\,\mathcal{O}_{1}-\mathcal{O}_{2}),\cos( \,\mathcal{O}_{1}-\mathcal{O}_{2})), \tag{12}\]  
where \(\text{atan2}(\cdot)\) represents the four-quadrant inverse tangent and leads to angular values between \([-\pi,\pi]\), which corresponds to the angular difference between two input image phases. Additionally, it determines the limit of motion, which is bounded by Equation (13),  
\[\left|\mathcal{O}_{shift}\right|=\frac{\left|\mathcal{O}_{diff}\right|}{ \omega}\leq\frac{\pi}{\omega} \tag{13}\]  
where \(\omega=2\pi v\), and \(v\) denotes the spatial frequency of the pyramid level. In a multi-scale pyramid, each level represents a particular band of spatial frequencies \(v\in[v_{min},\,\,\,v_{max}]\). Let \(v_{max}\) correspond to the highest frequency of any level then a phase difference of \(\pi\) represents a shift of one pixel. This appears to be a reasonable shift in coarser pyramid levels for low-frequency content; however, for high-frequency content, it is too limiting to achieve realistic interpolation.
# Probabilistic Ship Detection and Classification Using Deep Learning
## 5 Experimental Results
### Ship Dataset  
The dataset mainly focused on large ships, which were divided into two types navigating in the ocean: warship and merchant ship. Three warship classes exist: aircraft carrier, destroyer, and submarine. Three classes of merchant also exist: container ship, bulk carrier, and cruise ship. Finally, we included a small ship in the dataset, a tugboat that assists large ships in entering and leaving a port, resulting in seven classes.  
To train the Faster R-CNN for ship detection and evaluate single image detection, a total of 7000 still images were collected. Each class included 1000 images, completely balancing the problem. All the still images were manually gathered from Google image search. Most of the collected still images were completely different from and separated from other collected images and none were not consecutive. The ship image dataset was divided into a training dataset and a test dataset, as summarized in Table 2. In detail, 5250 images (75%, 750 images per class) among the 7000 images were used for the training the Faster R-CNN, and 1750 images (25%, 250 images per class) were used to test single image detection.  
To evaluate ship detection performance on videos, seven video clips involving all the aforementioned classes were downloaded from YouTube in MPEG-4 video format. A test video file was decomposed into a sequence of still images that were consecutive in time and each image was processed by Faster R-CNN. The detection result of each image was combined with that of the consecutive images and the combined result was used in video simulation.
## 4 Results and Discussion
### Parametric Analyses of Responses
#### 4.2.1 Analysis of Dimensional Deviations  
Dimensional deviations (Div) are a significant factor in the SLA process because of their vital influence on accuracy. Figure 8 shows the main effect plot for the means for each variable relating to (Div).  
It is clear from Figure 6 that the normal exposure time (A) has significant effects on the dimensional deviation. As mentioned before, the average exposure time refers to the specific duration for which each photocurable resin layer is exposed to ultraviolet (UV) light during the curing process. This exposure time plays a crucial role in the shrinkage behavior of the resin and can affect the accuracy of the cured product [11].  
The figure depicts that (Div) initially decreases as the exposure time increases, reaching a minimum value at the middle stage, but subsequently starts to increase with further increases in exposure time. This characteristic defines the behavior of the photosensitive material during curing or polymerization. The photosensitive material experiences initial low-volume shrinkage during the curing process, which gradually increases over time.  
Generally, during curing, the photosensitive material undergoes chemical reactions, such as cross-linking or polymerization, where individual molecules join together to form more extensive, interconnected networks. These reactions involve the formation of chemical  
Figure 8: Main effects plot for dimensional deviation. (**A**: Normal exposure time, **B**: Bottom exposure time, and **C**: Bottom layers).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S** & **R-sq** & **R-sq (Adj)** & **R-sq (pred)** \\ \hline
0.512201 & 97.14\% & 94.27\% & 84.54\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Model summary for surface roughness.  
bonds between the molecules. As the bonds are formed, the material's molecular structure undergoes changes, leading to a reduction in volume and resulting in shrinkage [31].  
As is evident from Figure 8, bottom exposure time (B) has little effect on dimensional deviation (Div). This slight increase in (Div) refers to the UV exposure time applied to the first few layers of the 3D prints. It specifically applies to the initial layer fabrication between the platform and the bottom of the resin container. However, the increased UV exposure time for the bottom layers results in more significant shrinkage, leading to increased dimensional deviation.  
Finally, it is understandable that the bottom layer (C) greatly impacts dimensional deviation. More specifically, transforming (C) alone from 1 to 7 can decrease (Div). Increasing the number of (C) provides additional support and prevents warping or deformation during the printing process, leading to a smaller amount of deviation [19].
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 3 Materials and Methods
### Employed Datasets
#### 3.1.2 UCI-HAPT Dataset  
The UCI-HAPT dataset [12] was used as a benchmark for the selected pre-trained model. It was published in 2014 and is an expanded version of the University of California Irvine Human Activity Recognition (UCI-HAR) dataset [57], supplemented with postural transitions. A waist-mounted smartphone with a triaxial accelerometer and gyroscope at the sampling rate of 50 Hz was used to collect the data from 30 volunteers aged 19-48. UCI-HAPT contains data for 12 activities, namely: walking, walking upstairs, walking downstairs, sitting, standing, laying, stand-to-sit, sit-to-stand, sit-to-lie, lie-to-sit, stand-to-lie, and lie-to-stand, 6 of which do not belong to the KU-HAR dataset.  
The dataset contains 10,929 samples of 561-feature vectors with time and frequency domain variables, as well as unsampled 6-channel sensor readings. The median and low-pass Butterworth filters were utilized to perform signal denoising. In this study, we did not use the supplied feature vectors, but manually obtained the time-domain samples from the row sensor readings.  
One of the requirements of TL is that the sample shape of the source and target datasets must be identical. To fulfil this requirement, we preprocessed the UCI-HAPT dataset with the following procedures: first, the sampling rate of the raw sensor readings was increased from 50 to 100 Hz by inserting the average value between two adjacent points. Second, we extracted the time-domain samples using a non-overlapping 3-s windowing technique, yielding 4847 six-channel time-domain samples.  
To evaluate the performance of the selected model on target datasets of varying sizes, we utilized the entire preprocessed UCI-HAPT dataset and its subset, which contained 30% of randomly selected samples, totaling 1652. Figure 1 illustrates the class distributions of the whole preprocessed UCI-HAPT dataset and its subset.  
As can be seen from Figure 1, the preprocessed UCI-HAPT dataset and its subset are imbalanced. Because the sensor readings in the UCI-HAPT dataset were denoised using low-pass Butterworth and median filters and the sampling rate was artificially doubled, it can be stated that the UCI-HAPT target dataset has significant differences in terms of signal representation from the source KU-HAR dataset. This implies that if the selected pre-trained model performs positive knowledge transfer to the UCI-HAPT target dataset, it would be reasonable to use the proposed model for other time-domain data with similar distinctions. It would be beneficial, for example, for cross-position activity recognition problems where the sensor readings are gathered from different body parts.