esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dynamic Changes in Volatile Metabolites during Shaken Black Tea Processing  
Headspace solid-phase microextraction (HS-SPME) combined with GC \(\times\) GC-TOFMS was used to detect black tea processing samples, and the total number of volatile compounds identified in FL, S1, S2, S3, F, and D were 212, 213, 218, 219, 236, and 241, respectively (Tables S1 and S2). The 1D, 2D, and 3D contour plots of total ion chromatography (TIC) of shaken black tea are shown in Figure 1B. A total of 241 volatile substances was divided into 9 categories, including 49 aldehydes (5 terpene aldehydes), 40 esters, 29 alcohols (10 terpene alcohols), 34 ketones (9 terpene ketones), 30 aromatics, 24 alkenes (14 terpene alkenes), 17 alkanes, 13 furans, and 5 other compounds.  
Among them, aldehydes had the highest content of volatile substances, accounting for 37.53-41.76% of total volatile organic compounds (TVOCs). Alcohols were the second most abundant volatiles, accounting for 16.21% to 25.59% of TVOCs, and the proportion decreased significantly during black tea processing, especially in the drying stage (Figure 1C). After drying, the proportion of ketones in TVOCs significantly increased by 76.42% (\(p<0.01\)) compared with FL. However, the proportion of alcohol and ester contents in finished tea significantly reduced by 35.98% (\(p<0.01\)) and 50.93% (\(p<0.01\)), respectively, compared with FL. The proportion of aldehyde, alkene, and aromatic contents were relatively stable during processing. According to the literature, the main aroma components of black tea are alcohols and aldehydes [22], which is consistent with the results of this experiment.  
Among tea secondary metabolites, terpenoids are one of the main abundant and diverse classes of natural compounds [23]. The aroma-related volatile terpenoids in teas are mainly terpene alcohols, terpene ketones, terpene alkenes, and terpene aldehydes, which are important components of VOCs in all kinds of finished oolong tea and black tea [24]. A total of 38 terpenoids, terpene alcohols (103.23-137.62 ug/L), and terpene alkenes (24.61-55.47 ug/L) were present in high concentrations. The total amount of terpenoids tended to increase during processing. After drying, terpenoids accounted for 15.63% of TVOCs, an increase of 67.17% over the FL. Terpene alcohols and terpene ketones were identified as the key volatile contributors to the flower and sweet attributes of tea due to an extremely low odor threshold. The thresholds of (E)-\(\beta\)-ionone, \(\beta\)-damascenone, and linalool were 0.007, 0.002, and 6 ug/L, respectively [25], with floral, sweet, and other aromas [26].
Deep learning, as a leading machine learning approach, has gained widespread use in automatic modulation classification due to its ability to learn features directly from raw data without manual feature engineering. This approach has proven especially advantageous for handling complex signal patterns in AMC applications [6; 7; 8]. One landmark study by [PERSON] et al. [9]. demonstrated that convolutional neural networks (CNNs) can outperform traditional approaches on benchmark datasets [10]. Other advanced models, such as the convolutional LSTM dense neural network (CLDNN) and ResNet, have achieved high accuracy by leveraging temporal and spatial dependencies within signal data [11].  
Given that modulated signal samples form a time-series, a two-layer long short-term memory (LSTM) model has been introduced, utilizing amplitude and phase as inputs to capture temporal dependencies effectively. This approach enhances classification performance by capturing the time-series nature of signals to improve modulation recognition accuracy, especially under dynamic signal conditions [12]. In [13], the authors introduced DLRNet, which employs depthwise convolution combined with large kernels to improve feature extraction capabilities, demonstrating significant gains in recognition accuracy. In [14], the authors utilized the short-time Fourier transform (STFT) to transform time-series data into spectral images, which were then used as input for a CNN [15]. This approach resulted in high classification accuracy, demonstrating the effectiveness of converting temporal data into a frequency domain representation for improved signal recognition. In [16], the authors introduced a lightweight network that achieves high recognition accuracy while maintaining low computational requirements. This model relies on the estimation and conversion of phase parameters, effectively balancing performance and efficiency in modulation recognition tasks. In [17], [PERSON] et al. developed a method called DPM-SCNN that enhances sequence feature representation by implementing cyclic shift processing on time-series data. This approach has demonstrated high recognition accuracy on benchmark datasets.  
Building on these, [PERSON] et al. improved AMC by introducing a bidirectional gated cycle unit in the CLDNN, which balances accuracy and speed, making it suitable for real-time applications [18]. [PERSON] et al. further refined this by integrating Bi-LSTM layers with CNNs, leading to a model that excels in environments with noise interference [19]. Innovative methods continue to emerge. Meta-learning frameworks, like the meta-transformer, have also made strides by enabling AMC models to learn generalizable patterns from limited samples, promising improved performance on previously unseen modulation types [20]. Newer strategies address noise resilience. [PERSON]'s Waveform Spectrum Multimode Fusion combines residual networks with multimodal fusion, which is highly effective in environments with high-order digital modulations [21].
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation
### Model Performance  
According to the above metrics, the performance of our model on the validation set is shown in Figure 7. Figure 7a shows the corresponding accuracies when we set different values of \(K\). The accuracy rapidly improves between epochs 0 and 20 and is essentially maximum at 40 epochs, fluctuating around the maximum thereafter. Obviously, the smaller the \(K\) value is, the higher the accuracy. This shows that our scores are meaningful: the higher the score is, the higher the confidence in the prediction and the greater the probability of vulnerabilities. In addition, the curves for \(K\) = 100\(\sim\)1000 are all above 90% in the end, while those for \(K\) = 100\(\sim\)600 reach above 95%. Since there is no open source of V-Fuzz, we implement the prediction model of V-Fuzz (according to the parameters in the paper) and conduct experiments on the same dataset used for _SAViP_. Figure 7b shows the Top-K accuracies for V-Fuzz. The accuracies for \(K\) = 100\(\sim\)600 surpass 82%, indicating that SAViP is 13% better than V-Fuzz. Figure 7c shows the comparison of the recall of the two models; V-Fuzz reaches 0.6775, which is slightly better than its performance in the original paper; this is because our standards for dataset labeling are not totally same with it. Figure 7c clearly shows that the recall of our model and V-Fuzz increase in similar manners, and both reach the highest value around epoch 40. Our recall increases from 50% to 78%, which is 11% higher than V-Fuzz. These comparisons indicate that our model makes improvements over the state-of-the-art V-Fuzz.  
In addition, we distinguish the performance of each CWE. To assess the confidence for the highest-scoring data, we take the \(K\)-10 accuracy for each CWE. Figure 8 shows the \(K\)-10 accuracy and recall of SAViP and the recall of V-Fuzz for each CWE. The two dashed lines in the figure are the 95% accuracy baseline and the 78% recall baseline. We can see that, for the \(K\)-10 accuracy, only CWE134 and CWE401 reach 95% and 90% accuracy, respectively, while the rest reach 100%. This shows that our model exhibits high confidence in the vulnerability scores, which means that a high-scoring function has a high probability of possessing vulnerability. In terms of the recall rate, SAViP performs better than V-Fuzz for all of the CWEs. More specifically, for SAViP, 6 CWEs possess a recall higher than the baseline (i.e., the recall overall), while the recall of the other 6 CWEs is lower. This shows that the performance of our model is balanced; that is, the overall performance improvement over V-Fuzz does not originate from the extreme performances of individual CWEs.  
**Summary**: SAViP outperforms the state-of-the-art approach V-Fuzz in vulnerability prediction with a large margin. It can automatically predict vulnerabilities without prior knowledge predefined by security experts, which reduces the burden on researchers.
## 2 Methods
### Phase-Based Effective Connectivity Estimation Approaches Considered in This Study
#### 2.3.2 Phase Slope Index  
The phase slope index (PSI) is an effective brain connectivity measure that assesses the direction of coupling between two oscillatory signals of similar frequencies [13]. Given two time series \(\mathbf{x}=\{x_{i}\}_{t=1}^{l}\) and \(\mathbf{y}=\{y_{i}\}_{t=1}^{l}\), the PSI is defined as the slope of the phase of the cross-spectra between \(\mathbf{x}\) and \(\mathbf{y}\):  
\[PSI(\mathbf{x}\rightarrow\mathbf{y})=\Im\Bigg{(}\sum_{f\in F}C_{\mathbf{xy}}^ {*}(f)C_{\mathbf{xy}}(f+df)\Bigg{)}, \tag{11}\]  
where \(C_{\mathbf{xy}}(f)=S_{\mathbf{xy}}/\sqrt{S_{\mathbf{xy}},S_{\mathbf{xy}}}\) is the complex coherence, \(S_{\mathbf{xy}}\in\mathbb{C}\) is the cross-spectrum between \(\mathbf{x}\) and \(\mathbf{y}\), \(S_{\mathbf{xx}},S_{\mathbf{yy}}\in\mathbb{C}\) are the auto-spectrums of \(\mathbf{x}\) and \(\mathbf{y}\), \(df\in\mathbb{R}^{+}\) is the frequency resolution, \(F\) stands for the set of frequencies over which the slope is summed, and \(\Im\) indicates selecting only the imaginary part of the sum [12]. If the PSI, as defined in Equation (11), is positive, then there is directed interaction from \(\mathbf{x}\) to \(\mathbf{y}\) in \(F\). Conversely, if the PSI is negative, the directed interaction goes from \(\mathbf{y}\) to \(\mathbf{x}\). Note that by definition the PSI is an antisymmetric measure: \(PSI(\mathbf{x}\rightarrow\mathbf{y})=-PSI(\mathbf{y}\rightarrow\mathbf{x})\).
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 2 Related Work
### Object Tracking  
A second component of our research involves fast and accurate object tracking. As the term suggests, object tracking aims to follow one or more objects across different frames in a video. This longstanding research area has seen the development of various approaches, leveraging either traditional or modern deep learning techniques.  
Traditional techniques, despite their age, remain relevant due to their simplicity and speed. Two well-known categories rely on either features or correlation. Feature-based techniques use characteristics like color, textures, and edges to track objects from one frame to the next [22; 23], while correlation-based techniques learn a filter to discriminate the tracked object from the background [24; 25]. While these traditional methods are effective for basic tracking tasks, they face limitations in complex scenarios involving occlusions and illumination changes.  
To address these challenges, many deep learning-based trackers have been developed, leveraging their ability to learn more efficient and robust feature embeddings. These have been popularized with the advent of Siamese CNN-based trackers, introduced by [PERSON] et al. with SiamFC [26]. This approach consists of two branches that extract features from the template and the search region, which are then used to find the most similar object to the template in the search region using similarity matching. Researchers have continued to improve upon this method to enhance tracking performance and robustness [27; 28]. However, Siamese CNN-based trackers still struggle with global context, similar objects, and occlusion [29].  
The success of transformers in natural language processing has led to their application in computer vision tasks, including object tracking. Some approaches employ a hybrid CNN-transformer architecture since CNNs excel at representing local features, while transformers capture global features more effectively. By combining them, these hybrid approaches aim to achieve the best of both worlds [30; 31]. Other approaches opt for a completely transformer-based solution, relying solely on the attention mechanism to achieve state-of-the-art tracking performance [32; 33].  
Next to video object tracking, related work has extended the focus to video object segmentation, where a segmentation of the object is tracked across a video instead of only the bounding box [34; 35]. This segmentation information enables more fine-grained analysis but comes at the cost of increased computational complexity. Several softwaresolutions [36; 37] have been developed using video object segmentation trackers in combination with Segment Anything [21]. These tools allow user input to extract a mask, which is then tracked across the video to facilitate data labeling, keying, and inpainting. While these solutions share similarities with parts of our proposed solution, they do not focus on real-time tracking for live broadcasts.
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 3 Results  
In order to verify the effectiveness of the proposed method for deghosting, we first apply the pseudo-3D algorithm to the synthetic data obtained from the forward modeling of the saltdome model (Figure 5). The saltdome model is a classic acoustic model for testing multiple attenuation methods for marine data [34], and it has a width of 5400 m, a depth of 1250 m, and a grid spacing of 5 m. The model contains high-speed layers and faults, and the corresponding records are complex. As for the observation system, in order to highlight the influence of ghost, the sinking depth of the sources is 14 m and the sinking depth of the receivers is 25 m. The maximum offset is 2550 m and the trace interval is 10 m. To further verify the effectiveness of the algorithm, we also adopt the algorithm to the field streamer data. For data preparation, we choose the open-source software Madagascar, which is applicable to digital image and data processing in geophysics and related fields, as a tool for forward modeling and data preprocessing.
## 5 Experiments and Results
### Quantitative Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**ResUNet**} \\ \cline{2-5}  & **DSC** & **mIoU** & **Recall** & **Precision** \\ \hline CS & 0.6404 \(\pm\) 0.0324 & 0.4688 \(\pm\) 0.0212 & 0.6876 \(\pm\) 0.0537 & 0.7088 \(\pm\) 0.0213 \\ DA & 0.8942 \(\pm\) 0.0452 & 0.8814 \(\pm\) 0.0422 & 0.8922 \(\pm\) 0.0421 & 0.9206 \(\pm\) 0.0354 \\ IVC & 0.8388 \(\pm\) 0.0372 & 0.8502 \(\pm\) 0.0318 & 0.8586 \(\pm\) 0.0344 & 0.8918 \(\pm\) 0.0421 \\ LAA & 0.8225 \(\pm\) 0.0312 & 0.6822 \(\pm\) 0.0386 & 0.6845 \(\pm\) 0.0244 & 0.7254 \(\pm\) 0.0441 \\ LAW & 0.8132 \(\pm\) 0.0404 & 0.6645 \(\pm\) 0.0354 & 0.6782 \(\pm\) 0.0243 & 0.6822 \(\pm\) 0.0312 \\ PM & 0.7288 \(\pm\) 0.0591 & 0.7388 \(\pm\) 0.0466 & 0.7816 \(\pm\) 0.0381 & 0.7985 \(\pm\) 0.0477 \\ PML & 0.5184 \(\pm\) 0.0266 & 0.4266 \(\pm\) 0.0342 & 0.5708 \(\pm\) 0.0371 & 0.6024 \(\pm\) 0.0672 \\ PAA & 0.9178 \(\pm\) 0.0388 & 0.8598 \(\pm\) 0.0322 & 0.8776 \(\pm\) 0.0372 & 0.9345 \(\pm\) 0.0511 \\ PA & 0.8922 \(\pm\) 0.0420 & 0.8812 \(\pm\) 0.0460 & 0.8954 \(\pm\) 0.0434 & 0.9288 \(\pm\) 0.0437 \\ RVW & 0.8914 \(\pm\) 0.0421 & 0.6288 \(\pm\) 0.0344 & 0.7212 \(\pm\) 0.0502 & 0.7366 \(\pm\) 0.0332 \\ SVC & 0.8978 \(\pm\) 0.0382 & 0.8194 \(\pm\) 0.0322 & 0.8368 \(\pm\) 0.0442 & 0.8624 \(\pm\) 0.0461 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Obtained segmentation results of different cardiovascular structures for ResUNet network architecture. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{4}{c}{**UNet**} \\ \cline{2-5}  & **DSC** & **mIoU** & **Recall** & **Precision** \\ \hline CS & 0.5893 \(\pm\) 0.0545 & 0.4231 \(\pm\) 0.0611 & 0.6721 \(\pm\) 0.0466 & 0.6842 \(\pm\) 0.0325 \\ DA & 0.8834 \(\pm\) 0.0375 & 0.8392 \(\pm\) 0.0445 & 0.8621 \(\pm\) 0.0488 & 0.8892 \(\pm\) 0.0465 \\ IVC & 0.8175 \(\pm\) 0.0485 & 0.7910 \(\pm\) 0.0531 & 0.8114 \(\pm\) 0.0518 & 0.8310 \(\pm\) 0.0707 \\ LAA & 0.7845 \(\pm\) 0.0440 & 0.6788 \(\pm\) 0.0412 & 0.6654 \(\pm\) 0.0610 & 0.7094 \(\pm\) 0.0659 \\ LAW & 0.7671 \(\pm\) 0.0591 & 0.6384 \(\pm\) 0.0484 & 0.6610 \(\pm\) 0.0522 & 0.6912 \(\pm\) 0.0588 \\ PM & 0.7398 \(\pm\) 0.0461 & 0.6970 \(\pm\) 0.0520 & 0.7588 \(\pm\) 0.0488 & 0.8096 \(\pm\) 0.0677 \\ PML & 0.5299 \(\pm\) 0.0457 & 0.4815 \(\pm\) 0.0489 & 0.5922 \(\pm\) 0.0464 & 0.6386 \(\pm\) 0.0550 \\ PAA & 0.8891 \(\pm\) 0.0443 & 0.8410 \(\pm\) 0.0662 & 0.8614 \(\pm\) 0.0422 & 0.8812 \(\pm\) 0.0656 \\ PA & 0.8697 \(\pm\) 0.0575 & 0.7824 \(\pm\) 0.0502 & 0.7598 \(\pm\) 0.0349 & 0.7892 \(\pm\) 0.0573 \\ RVW & 0.8702 \(\pm\) 0.0513 & 0.6804 \(\pm\) 0.0645 & 0.7012 \(\pm\) 0.0452 & 0.7214 \(\pm\) 0.0737 \\ SVC & 0.8892 \(\pm\) 0.0462 & 0.7198 \(\pm\) 0.0468 & 0.7394 \(\pm\) 0.0388 & 0.7598 \(\pm\) 0.0560 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Obtained segmentation results of different cardiovascular structures for UNet network architecture.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 3 Si-Based Photodetectors
### Integration of PD with Waveguide  
PDs can be categorized into two types according to coupling schemes: a free-space coupled PD and a waveguide-coupled PD as illustrated in Figure 20. In the case of the free-space coupled PD, a fiber can be directly coupled for a top illumination or a back illumination. In either case, the direction of the incoming light is always parallel to that of the carrier collection. Therefore, in order to enhance the quantum efficiency, the thickness of the absorption layer should be sufficiently large, which results in an increased transit time, and hence degrades the bandwidth. On the other hand, in the waveguide-coupled PD, the tradeoff between the quantum efficiency and the bandwidth can be relaxed. This is because the absorption can be improved by increasing the absorption length along the direction of the light, which does not affect the transit time. However, in this case, the increased junction capacitance may degrade the bandwidth, but not as much as in the case of the free-space coupled PD. Another benefit of the waveguide coupling is that the waveguide PD is more suitable for an on-chip WDM system, thus obviating the use of additional fibers. Evanescent coupling and butt coupling are most commonly used for waveguide coupling. Evanescent coupling is easy to realize whereas it is relatively difficult to design a structure for butt coupling. However, in terms of quantum efficiency, butt coupling with a well-designed waveguide is generally much better than evanescent coupling.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 3 Antenna Design
### Antenna Design Procedure
The gains are 5.0 dBi and 6.0 dBi at 1.7 GHz and 3.4 GHz, respectively. The corresponding efficiencies are \(-1.4\) dB and \(-0.4\) dB. As observed, the radiation pattern at 3.4 GHz now exhibits a peak at \(\theta=0\), \(\phi=0\). The addition of the protrusion has also contributed to providing the antenna with the characteristic of linear polarization along the \(\theta\) component at 1.7 GHz and linear polarization along the \(\phi\) component at 3.4 GHz. This is an important feature because it allows the tag to respond in a cross-polar manner at the second harmonic with respect to the interrogation signal. Figure 12 shows surface currents flowing on the surface of the annular patch at 1.7 GHz (a) and at 3.4 GHz (b). The flow of currents agrees well with the theoretical one. In particular, inside the protrusion, the currents have opposite direction at 1.7 GHz so that they do not contribute to the V-polarized radiation; instead, they have the same direction at 3.4 GHz and contribute to the H-polarized radiated field in the boresight direction. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **[mm]** \\ \hline r\({}_{\text{i}}\) & 14.52 \\ r\({}_{\text{o}}\) & 27.07 \\ l\({}_{\text{feed}}\) & 1 \\ w\({}_{\text{feed}}\) & 4.71 \\ w & 1.14 \\ y\({}_{\text{port}}\) & 14.9 \\ h\({}_{\text{h}}\) & 4.91 \\ w\({}_{\text{h}}\) & 11.1 \\ l\({}_{\text{stub}}\) & 12.12 \\ W\({}_{\text{stub}}\) & 15 \\ f\({}_{\text{circ-slot}}\) & 2.81 \\ Y\({}_{\text{circ-slot}}\) & 4.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Final antenna dimensions. Figure 10: Reflection coefficient of the antenna after adding the rectangular protrusion inside the inner perimeter of the ring composing the antenna.
## 1 Introduction
As a result, the frequency ranges analyzed in previous studies on carpal extensors [4; 10; 11; 12; 13; 14; 15] differed, potentially altering the characteristics of the signals, and thus affecting the final outcomes of the equine biomechanics studies. Considering the impact of filtering on human sEMG signals from small abaxial muscle [16] and on equine sEMG signals from large muscles [5; 6], we hypothesize that the characteristics of the myoelectric activity of equine carpal extensors vary depending on the filtering method applied. Furthermore, due to the differences in noise and movement artifact contamination among various muscles, we also hypothesize that filtering-dependent characteristics of the myoelectric activity differed between individual equine carpal extensor muscles. If the results of this preliminary observational study confirm the proposed hypotheses, further systematic investigations into sEMG signal-filtering methods for different equine muscles will be justified. Conversely, if the hypotheses are rejected, it will be reasonable to compare previous studies on the myoelectric activity of carpal extensors without limitations, and future equine sEMG studies may be conducted without specific signal processing restrictions. Since both noise and its reduction introduce modifications to the raw sEMG signal [16], potentially affecting data analysis [5; 6], this observational study demonstrates how the same sEMG signals from the equine carpal extensors are altered by processing with previously published filtering methods [4; 10; 11; 12; 13; 14; 15]. To address the current gap in knowledge regarding equine sEMG signal processing, this preliminary proof-of-concept study aims to compare the sEMG signal features and signal-loss metrics across four equine carpal extensor muscles using different, previously applied in the literature, filtering approaches: raw (unfiltered) signal, low-pass filtering with a 10 Hz cut-off frequency, bandpass filtering with a 40-450 Hz cut-off frequency, and bandpass filtering with a 7-200 Hz cut-off frequency.
# Slotted Circular-Patch MIMO Antenna for 5G Applications at Sub-6 GHz
## 5 Experimental Testing of the Proposed Antennas Within the 5G MIMO Application Framework
So, a 2-port MIMO with two 1 \(\times\) 4 array antennas with high antenna gain and efficiency positively influences how much useful energy is radiated and collected, ultimately determining signal strength and its ability to be distinguished from noise in wireless communication systems, compared with the 2-port MIMO with two 1 \(\times\) 2 arrays and single-element antennas.