esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Integrated Localization and Communication
### Localization for RISeUAVs
#### 7.3.2 RISeUAV Localization  
RISeUAV is energy-efficient and cost-effective for various applications as we have discussed in this paper. Also, the RISeUAV can help ground vehicles with accurate localization and communication [202]. However, for RISeUAV to accomplish its tasks, particularly for aerial RIS phase shift design, the UAV's accurate position must be known, which is a non-trivial task. As stated, GPS signals are not accurate for RISeUAVs for beam-steering for autonomous crash-avoidance flights in dense environments. To this end, we have proposed the SLAPS technique [46] for simultaneous localization and phase shift of RISeUAV. In the SLAPS, the localization estimation and RIS phase shift are iteratively modified and optimized so both converge to the optimal solution, which is accurate localization-based communication. The SLAPS mechanism has been illustrated in Figure 8.  
The SLAPS principle is as follows: Suppose that the actual RISeUAV pose (i.e., position and heading) is denoted with \(\hat{x}\). The mMIMO BS beamforming and RIS-PhSh are implemented based on the RISeUAV's estimated pose (denoted as \(x\)). Then, the estimated pose and consequence BS-BF and RIS-PhSh are iteratively updated and optimized so that the received signal at the receiver (e.g., an anchor point) is maximized, indicating that \(x\) converges to \(\hat{x}\), where the maximum channel gain is achieved.
# Compact Design and Impact Ionization: Utilizing Small-Sized Thyristors in a 4-Stage Marx Generator
### Marx Generator  
The Marx generator, patented by [PERSON] in 1923, is a circuit designed to deliver high-voltage pulses by leveraging the principle of charging capacitors in parallel and subsequently discharging them in series to provide energy to a load [26]. A typical [PERSON] generator configuration is illustrated in Figure 1. In this setup, each stage contains a capacitor (\(C_{i}\)) that is charged through resistors (\(R_{i1}\) and \(R_{i3}\)). When the spark gaps (SGs) are triggered, the capacitors discharge through \(R_{i2}\), resulting in an output voltage that is the cumulative sum of all \(n\) individual stages [27]. During the early development of the Marx generator, spark gaps were the only available switching devices, achieving rise times on the order of 100 ns, which was considered extremely fast for that era. However, advancements in semiconductor technology have since led to the development of significantly faster switching devices.  
Various Marx generators based on solid-state semiconductors have been developed. Using new arrangements with semiconductor switches (MOSFETs, thyristors), their performance (voltage and current outputs, and very fast rise times) can match that of spark gaps and gas switches such as thyratrons [28].  
In the context of this paper, the Marx generator serves as the voltage-boosting step for the pulse generator being developed. By using semiconductor devices--specifically D2 PAK  
Figure 1: Schematic of a Marx generator.  
thyristors instead of conventional spark gaps--we developed a topology as explained in Section 2, to enable off-the-shelf components such as SiC MOSFETs and thyristors. This innovative approach enables the delivery of high-voltage and current pulses within sub-nanosecond switching times.
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis
### The Requirements of SNR for Successful Target Detections Based on Second Order Statistical Detector  
Research at Dstl has indicated that a peak SNR of 75 in the imagery is sufficient enough for the detection of full-pixel, spectrally distinctive targets by an integrated visible-SWIR imaging system. In the case of sub-pixel targets with 12% fill-factor (i.e., abundancy), they could only be detected by the visible-SWIR imager unless the signal of the imagery have peak SNRs of 250 and above. However, the detection of sub-pixel targets with 6% of fill-factor will require peak SNRs of 400 and above in the imagery for them to be detected successfully. The figures that have been presented here are based on the imageries which have been pre-processed by atmospheric corrections such as the QUAC [20], and a second order statistical distance measure algorithm such as the ACE [21] has been utilised for target detection.
## 1 Introduction
The ones-counter encoder can work at 350 MHz while maintaining 170 M samples/s, owing to the pipelined adder tree structure. Furthermore, due to the inconsistency of delay elements caused by the process, the bin-by-bin calibration is used to increase their linearity. There are two calibration tables (histogram/lookup table) in this design, which are used to calibrate automatically and online in the hardware. As the delay elements vary with voltage and temperature, a frequency counter is used to extrapolate their effects on the delay line and compensate them. The contribution of this work makes the TDL-TDC accessible to time measurement with high precision and speed related applications easily. For example, if the bandwidth of a silicon photomultiplier (sipm) detector is 7 M, it is expected that such a TDC can be utilized for a PET. This paper is organized as follows: Section 2 describes the architecture of the TDC design, which employs a ones-counter encoder and online calibration. Section 3 contains the assessment platform, characterization of the TDC performance, and comparison to state-of-the-art studies. Finally, Section 4 comprises the summary and conclusion.
## 5 Results and Discussions
### Temperature Susceptibility of THFS Modified Asphalt Binder
#### 5.5.5 Frequency susceptibility based on CAM model
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Asphal Binders** & \(G_{g}{}^{*}\)**/kPa** & \(f_{c}\)**/\(H\)z** & \(m_{e}\) & \(k\) & \(R_{G}\) & \(R^{2}\) \\ \hline SK-90 & 2.63 \(\times\) 10\({}^{5}\) & 81.680 & 0.843 & 3.808 & 0.067 & 0.9996 \\
4\% THFS + SK-90 & 3.31 \(\times\) 10\({}^{5}\) & 67.836 & 0.833 & 3.328 & 0.075 & 0.9996 \\
6\% THFS + SK-90 & 3.57 \(\times\) 10\({}^{5}\) & 66.571 & 0.813 & 2.998 & 0.082 & 0.9999 \\
8\% THFS + SK-90 & 3.88 \(\times\) 10\({}^{5}\) & 64.537 & 0.809 & 2.549 & 0.096 & 0.9990 \\
10\% THFS + SK-90 & 3.91 \(\times\) 10\({}^{5}\) & 58.783 & 0.806 & 2.481 & 0.098 & 0.9994 \\ \hline \end{tabular}
\end{table} 
Table 9: The fitting parameters based on the CAM model. Furthermore, asphalt binder with a higher \(R_{G}\) value represented that the ratio of elastic to viscous was high and less sensitive to frequency. According to the test results in Table 9, it could be observed that 10% THFS modified asphalt binder had the highest \(R_{G}\) and lowest \(m_{e}\), and \(k\), which indicated that 10% THFS modified asphalt binder had a wider relaxation spectrum and less sensitivity to frequency. It also could be predicted that THFS modified asphalt binder had a better temperature and frequency susceptibility compared to SK-90. In other words, the addition of THFS could decrease the temperature and frequency susceptibility of asphalt binder.
# Decoding Visual Motions from EEG Using Attention-Based RNN
## 3 Materials
### Experimental Protocol  
Similar to the stimuli used in previous research [8], four types of motion were adopted in this experiment: translation (T), rotation (R), expansion (E), and contraction (C), all displayed on a gray (RGB: 96, 96, 96) background. The visual stimuli were made up of light gray (RGB: 136, 136, 136) and dark gray (RGB: 96, 96, 96) bands. They were presented on a 24-inch LCD monitor with a refresh rate of 60 Hz and a resolution of \(1920\times 1080\) pixels. The sizes of bright and dark bands of all stimuli were the same. Figure 1 shows the four stimuli displayed to the subjects.  
The motion parameters of the stimuli were set as the same as that in [8], where the speed of T, E, and C was all set to 7.4 \({}^{\circ}\)/s and R stimulus was rotated at 40 rotations per minute. All stimuli had a viewing angle of \(11.7^{\circ}\) at a distance of 80 cm from the monitor. All motion animations were played at a frame rate of 45 fps. There were four blocks in the experiment, each with 8 runs. One run comprised 25 trials (i.e., 25 motion animations randomly chosen from the four types). The total duration of displaying a stimulus was 1000 ms, and the standard onset asynchrony (SOA) was 2000 ms. Thus, the inter-stimulus interval (ISI) was 1000 ms, in which the monitor displayed a gray blank. To maintain mental stability, the subjects were asked to rest for 60 s between each run and 20 min between each block. Figure 2 illustrates the timing structure of one block.  
Therefore, a total of 800 trials was obtained from each subject, and the entire dataset contained 6400 trials (\(8\times 800\)). A perceptual discrimination task was added to the experiment: a target cue was randomly presented within approximately 10% of the stimulus intervals, and the next stimulus may or may not be consistent with the cue (probabilities for both were 0.5). A participant was required to respond as soon as possible when the stimulus was presented by pressing a button, with the left button implying consistency and the right button implying inconsistency. The time of pressing the button and the correct response rates were recorded. Before the experiment began, the subjects were informed of the detailed procedure of the experiment and trained how to respond using the button.  
Figure 1: Four types of motion stimuli. The arrows on the figures indicate the direction of movement.
# A Novel Multi-Cell Interference-Aware Cooperative QoS-Based NOMA Group D2D System
### Related Work
The power allocation in the NOMA signal was based on the assumption that only a single user is close to the source, which is not true as D2D receivers always tend to cluster around their respective transmitter. This aspect makes the channel condition of the users very similar to each other. Furthermore, the proposed D2D network is modelled by a poisson point process (PPP), which is not sufficient to fully model the behaviour of a D2D network.
# Analysis of Key Issues on GNSS-R Soil Moisture Retrieval Based on Different Antenna Patterns
## 2 Theoretical Analysis of Key Issues on Soil Moisture Retrieval  
To promote the research and business application processes of soil moisture remote sensing via GNSS-R, the soil detection range of the reflected GNSS signal must be clarified from the perspective of three-dimensional space. A specular reflection point can help determine the geographic location, while the spatial resolution and detection depth help determine the volume of the detected soil cube. Because the recording of soil moisture information occurs when GNSS signals are reflected on the Earth's surface, the following three problems are present in both single-antenna and multi-antenna GNSS-R remote sensing patterns.
# Parallel Image Completion with Edge and Color Map
## 4 Experimental Results and Analysis
### Edge, Color Information and Image Masks  
In order to train the edge model, the ground truth edge maps \(E_{gt}\) should be generated as the training labels. Canny algorithm is chosen to obtain rough but solid binary edge maps due to its speed, robustness, and ease of use, instead of dense sketches extracted by HED (Holistically-nested Edge Detection) [48]. Edge maps produced by Canny algorithm are binary (1 for edge, 0 for background) and one-pixel wide, which enhance the generalization capability of the model. However, edge maps produced by HED are of varying thickness and pixels can have intensities at the interval of [0, 1]. There are no significant improvements compared to Canny algorithm in the completion results [23]. The performance of Canny algorithm is controlled by the standard deviation of the Gaussian smoothing filter \(\sigma\), [PERSON] [23] suggests that the best results will be obtained when \(\sigma\approx 2\); an edge map example is shown in Figure 2b.  
Color maps corresponding to the style features that can provide additional information for the missing region during completion. To train \(G_{2}\), the ground truth color maps \(C_{gt}\) are created in an explicit way; they should be as simple as possible and include enough style information. First, a median filter with kernel size 21 is applied to the original images. In order to filter out the high frequency information, a large kernel size is chosen in the first median filtering. Next, the K-means algorithm with \(k=3\) is used to yield the average color maps. We choose a small \(k\) because the style information should be held as much as possible. After that, a median filter with kernel size 3 is applied again to blur the sharpness of the boundary lines. A small kernel size is adopted in this median filtering because the boundary lines are thin. This results in color maps with largely constant colors in low-frequency regions; a color map example is shown in Figure 2c.  
Masks play a vital role in the training. There are two types of masks that are used in the experiments: regular mask and irregular mask. Regular masks are square with a size of \(128\times 128\) (25% of total image pixels) at a random location within the image. We use QD-IMD (Quick Draw Irregular Mask Dataset) as the irregular masks, as shown in Figure 3. The irregular masks are augmented by introducing four rotations (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), 270\({}^{\circ}\)) and a horizontal reflection for each mask.  
Figure 2: An example of the ground truth image and corresponding edge and color maps. (**a**) ground truth \(I_{gt}\); (**b**) edge map \(E_{gt}\); (**c**) color map \(C_{gt}\).
# Feature Learning via Correlation Analysis for Effective Duplicate Detection
## 4 Our Approach
### Feature Selection Algorithm  
The feature selection algorithm [6] used in this study is designed to identify the features most effective for improving model performance and classification accuracy. Figure 4 provides an overview of the feature selection process, which extracts the top-K value features that contribute significantly to duplicate bug detection. The algorithm aims to determine the optimal subset of features that maximizes classification accuracy while minimizing redundant or irrelevant features. By removing such variables, the algorithm reduces the risk of overfitting, accelerates training, and enhances overall model efficiency. In this study, the algorithm was applied to classify bug reports into duplicate and nonduplicate categories and to extract the most relevant linguistic features indicative of each class.  
The feature selection process began with an incremental evaluation of K values, where K represents the number of top-ranked features selected for the model. The K values were set from 1 to 10 to analyze the effect of increasing feature count on model performance. A K value of 1 corresponds to selecting only the single most significant feature word, while a K value of 2 includes the two highest-ranked features. Similarly, a K value of 10 incorporates the top 10 features, which represents the largest subset tested in this study. For each K value, the model was trained and validated across datasets to measure its classification accuracy, precision, recall, and F1 score. The systematic evaluation of these metrics helped determine the optimal K value that consistently delivered the best results.  
The results showed that a K value of 10 yielded the highest classification performance across all tested datasets. For instance, in the Apache dataset, the accuracy increased by 6.55% compared to lower K values, while the JDT and Platform datasets showed improvements of 4.51% and 3.14%, respectively. These results showed that selecting the top 10 features provided a balance between capturing sufficient linguistic patterns and avoiding noise or redundancy in the feature set. The extracted features were ranked by importance and subsequently integrated into the BERT model [7], which used these features to predict whether a newly submitted bug report was a duplicate.  
By combining the feature selection algorithm with the BERT model, this study effectively identified key linguistic patterns and improved the accuracy of duplicate bug report detection. The feature selection algorithm ensured that only the most relevant fea  
Figure 4: Feature selection for top-K value extraction.  
tures contributed to the training process, reducing computational overhead and enhancing model efficiency. The approach highlights the importance of systematically evaluating feature selection parameters to ensure optimal performance, thereby showing the practical application of feature engineering in real-world software maintenance tasks.
# Ripple Suppression in Broadband Microwave Photonic Phase Shifter Frequency Response
## 4 Conclusions  
The presence of ripples in the frequency response of a MPPS implemented using a 90\({}^{\circ}\) hybrid coupler has been investigated. The ripples are due to the coupler amplitude and phase imbalance, which are large at high input RF signal frequencies. This generates an unwanted RF signal modulation sideband at the MPPS output, which causes amplitude variation and phase deviation. A technique for suppressing the ripples, by inserting a low-cost optical filter in the MPPS structure, has been presented. The experimental results demonstrated a large improvement in the MPPS phase deviation from \(\pm 12^{\circ}\) to \(\pm 3.8^{\circ}\) for a \(-180^{\circ}\) to \(180^{\circ}\) phase shift over a 4 to 40 GHz frequency range.  
Writing--original draft preparation, formal analysis, and investigation, W.X.; software, B.C. and R.Z.; writing--review and editing, E.H.W.C. and X.W.; project administration and supervision: X.W., X.F., and B.-O.G.  
This research was funded by the National Natural Science Foundation of China (NSFC, Beijing, China), (No. 61501205, 61771221).  
The authors gratefully acknowledge [PERSON]. [PERSON] for valuable discussions.  
The authors declare no conflict of interest.
## 1 Introduction  
Digital radiology is gaining importance in routine radiological procedures, mainly due to its ability to generate images that can be processed, archived, and transmitted electronically [1]. Indirect conversion and scintillator-based flat detectors are the primary types of flat detectors in use, replacing image intensifiers in cardiac and angiographic procedures [2, 3]. In flat panel detectors (FPDs), X-ray detection and the readout are done simultaneously. Compared to image intensifier-based fluoroscopy, direct conversion of the X-ray photons into digital information results in lower dispersion of X-ray photons, which reduces spatial resolution. In other words, flat-panel digital fluoroscopy is expected to show a better spatial resolution limit compared to image intensifier-based fluoroscopy. Another advantage of the FPD is its wide dynamic range that improves contrast throughout the image and allows better visualization of the low-contrast regions [2, 3, 4]. Despite this advantage, fluoroscopically guided procedures are considered high dose modalities with doses that often exceed the deterministic effect threshold [5]. Reported diagnostic reference levels (DRLs) vary from 42 to 75 mGy.cm\({}^{2}\) in diagnostic coronary angiography (CA) and from 84 to 213 mGy.cm\({}^{2}\) in percutaneous coronary interventions [5]. Furthermore, the maximum entrance surface air kerma (ESAK) rate limits from 25 to 100 mGy min\({}^{-1}\) are used to limit the radiation dose from X-ray fluoroscopic equipment used during international procedures [6]. These dose limits are verified during quality assurance (QA) measurements and are used as quality control parameters to monitor equipment performance.  
International radiation protection advisory bodies have suggested implementing QA--a vital optimization tool in medical imaging [7; 8; 9; 10]. QA measurement can help identify any equipment malfunction well in advance; this can contribute to minimizing imaging costs and ensuring radiation protection in patients and staff. Several studies have been performed to evaluate the FP fluoroscopy X-ray system's dose and image quality performance [11; 12].  
The present study reports the measurement results of the acceptance and commissioning of two biplane digital fluoroscopy systems based on FPDs at a University Hospital in Oman. The study aimed to determine the norms of acceptable practice for fluoroscopy systems using FPDs and will provide baseline data for future performance monitoring and QA.
# Assessing Database Contribution via Distributed Tracing for Microservice Systems
## 5 Discussion  
In our experiment, we deployed two microservice systems on a single machine with only one instance per database (service). In industrial microservice systems, a service can have several to thousands of instances running on different containers and can be dynamically created or destroyed according to the scaling requirements at runtime [7; 12], but this does not affect the effectiveness of our method because these databases (services) can achieve data consistency through related mechanisms [53], which has nothing to do with the database contributions.  
By comparing the experimental processes and results of the two systems, we can see that the more business scenarios there are, the lower the efficiency of the AHP process (it is strenuous to construct pairwise comparison matrices). At the same time, the increase of databases in the system may reduce the differentiation between database contributions. An industrial microservice system is usually a large-scale distributed system containing more business scenarios and databases. In this case, more appropriate weighting methods (e.g., G1 method [54], combined weight method [55]) can be considered and try to classify databases and calculate contributions by category.  
Since this method aggregates the number of records operated in the business scenario through trace information, the code logic will affect the contribution of the databases. For example, making a large number of redundant calls to a database while implementing a business function may increase the contribution of the database to a certain extent, and because our approach is oriented to business scenarios, different partitions of business scenarios also impact the database contribution.