esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 3.1.1 Influence of the Parameters
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Solvents**} & \multicolumn{3}{c}{**F-Value**} & \multicolumn{3}{c}{**Solid/liquid Ratio (g/ml)**} & \multicolumn{3}{c}{**F-Value**} & \multicolumn{3}{c}{**Temperature (°C)**} & \multicolumn{3}{c}{**F-Value**} & \multicolumn{3}{c}{**Time (@)**} & \multicolumn{3}{c}{**F-Value**} \\ \cline{2-13}  & **\(\bullet\)** & **\(\bullet\)** & **\(\blacksquare\)** & & **0.25** & **0.25** & **0.325** & & **30** & **40** & **50** & **2** & **6** & **10** & \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{l} lycopene \\ 25/100 g \\ \end{tabular} } & 3.36\({}^{\circ}\) & 3.27\({}^{\circ}\)b & 33.19\({}^{\circ}\)b & 4.19\({}^{\circ}\)b & 4.19\({}^{\circ}\)b & 3.36\({}^{\circ}\)b & 3.35\({}^{\circ}\)b & 3.35\({}^{\circ}\)b & 3.32\({}^{\circ}\)b & 3.36\({}^{\circ}\)b & 4.01\({}^{\circ}\)b & 5003.1\({}^{\circ}\)b & 501.6\({}^{\circ}\)b & 501.6\({}^{\circ}\)b & 501.7\({}^{\circ}\)b & 4.01\({}^{\circ}\)b \\  & DPPH & 7.32\({}^{\circ}\)b & 82.93\({}^{\circ}\)b & 7.56\({}^{\circ}\)b & 40.81\({}^{\circ}\)b & 7.19\({}^{\circ}\)b & 7.28\({}^{\circ}\)b & 80.4\({}^{\circ}\)b & 31.35\({}^{\circ}\)b & 77.56\({}^{\circ}\)b & 0.32\({}^{\circ}\)b & 75.94\({}^{\circ}\)b & 70.53\({}^{\circ}\)b & 4.62\({}^{\circ}\)b \\  & Polyphant & 31.15\({}^{\circ}\)b & 28.55\({}^{\circ}\)b & 25.53\({}^{\circ}\)b & 3.68\({}^{\circ}\)b & 40.51\({}^{\circ}\)b & 24.19\({}^{\circ}\)b & 17.53\({}^{\circ}\)b & 20.14\({}^{\circ}\)b & 28.54\({}^{\circ}\)b & 27.51\({}^{\circ}\)b & 29.39\({}^{\circ}\)b & 13.13\({}^{\circ}\)b & 27.73\({}^{\circ}\)b & 25.06\({}^{\circ}\)b & 29.44\({}^{\circ}\)b & 4.62\({}^{\circ}\)b \\  & Firemonia & 66.80\({}^{\circ}\)b & 57.88\({}^{\circ}\)b & 16.99\({}^{\circ}\)b & 141.14\({}^{\circ}\)b & 44.68\({}^{\circ}\)b & 44.84\({}^{\circ}\)b & 42.30\({}^{\circ}\)b & 4.07\({}^{\circ}\)b & 49.77\({}^{\circ}\)b & 46.68\({}^{\circ}\)b & 43.34\({}^{\circ}\)b & 44.37\({}^{\circ}\)b \\  & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of variance (ANOVA) of tomato pomace, influence of solvent, sample/solvent ratio, temperature, and time on parameters. Regarding the influence of solvent/liquid ratio on various parameters, it was observed that the optimal extractions occurred at a ratio of 0.1 g sample/25 mL solvent. Specifically, lycopene extraction yielded 3.56 mg/100 g, polyphenols reached 43.51 mg/L, and flavones reached 54.63 mg/L. Conversely, DPPH activity was the highest at a ratio of 0.3 g sample/25 mL solvent, resulting in an antioxidant activity of 80.43%. Furthermore, the solvent/liquid ratio significantly impacted (\(p<0.001\)) the polyphenol content, particularly observed at 261.49 mg/L.
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 5 Software Simulation Platform  
Efficient algorithms and a variety of network models are important cornerstones to support the continued development of photonic chips. At present, photonic computing networks are mainly simulated and trained through electronic computers. Then, the trained model parameters are loaded onto the photonic chip. By far, there are two main software simulation platforms in use: one is IPKISS [45], and the other is INTERCONNECT. In addition to the above two commonly used simulation tools, the Institute of Microelectronics of the Chinese Academy of Sciences has designed a system-level simulation and verification tool, named PDA.  
PDA designers use Python to package various models of optical devices. Users can modify the parameters and interface of the devices, and connect the device by a simple function statement. It can realize the extremely complex network structure at the link level, or the simulation task of the framework. After the completion of the system, each optical path can be monitored and the simulation diagram in the time domain can be output. This greatly facilitates the observation and analysis of the experimental results. Because of the flexibility of Python, PDA can also interact with MATLAB to complete the simulation according to the operators' different needs. PDA also has the advantage that it can be used in conjunction with the open-source layout tool Klayout to realize the complete chip design process of the layout driver and schematic driver. This provides great convenience for the overall development and design of the chip. It is worth mentioning that it is easy to operate.
# Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
###### Abstract  
For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning.  
maritime traffic; automatic identification system data; spatial-temporal density; national maritime traffic route; logarithmic scale; stretch symbolization 20221211246  
2022
## 3 Comparison of Mutually Exclusive Alternatives
### Economic Efficiency Indicators
#### 3.3.1 Benefit-Cost Ratio (BC Ratio)  
The benefit-cost ratio can be considered as the most common indicator that compares the investments and the benefits. The decision-making process can have high confidence in this indicator due to its ability to provide the gain in terms of investment percentage [48]. An example of the popularity of this indicator is that the flood defense programs in the United Kingdom must have a BC ratio of more than 8 to receive public funding [25].
# Graphene-Enhanced CuW Composites for High-Voltage Circuit Breaker Electrical Contacts
## 2 Materials and Methods
### Microstructure Characterization Methods
#### 2.4.2 In Situ Scanning Kelvin Probe Force Microscopy (SKPFM) Experiment  
An atomic force microscope (AFM) is a scanning probe microscope used to image and manipulate surfaces on an atomic scale. It works by scanning the surface of a sample using a sharp probe tip and measuring the force between the tip and the surface. These measurements yield high-resolution images of the sample's surface, along with quantifications of surface roughness and other pertinent physical properties. Among the various modes of AFM, scanning Kelvin probe force microscopy (SKPFM) stands out as a technique capable of detecting the Volta potential of materials [32].  
The AFM and SKPFM experiments were conducted using the TI-900 Triboindenter (Hysitron, Minneapolis, MN, USA) atomic force in situ test system and the Nanoscope V (Veeco, Plainview, NY, USA) equipped with the NT-MDT Kelvin probe, operating in the tapping mode. The surface height and Volta potential difference (hereinafter referred to  
Figure 1: The preparation process of graphene-enhanced CuW composites and electrical contacts.  
as \"potential\" in the main text) of the materials were obtained in situ before and after the addition of graphene. By observing and analyzing the distribution and changing rules of surface height and potential, along with considering the disparities in the electronic work function among different elements, we were able to determine the distribution of graphene within the composites. The cantilever used in our study has a spring constant of 2.8 N\(\cdot\)m\({}^{-1}\), a resonant frequency of 60-100 kHz, and a standard PtIr-coated silicon tip with a <25 nm curvature. Lift height was set to 60 nm. The SKPFM experiments were carried out in air at room temperature and relative humidity of 38 \(\pm\) 1%. The system was calibrated by using a high-quality highly oriented pyrolytic graphite prior to the experiment to ensure the accuracy of the potential measurements.
## 3 Results
### Methodological Quality
[37] conducted a network meta-analysis of eight different therapies (CS injections, ESWT, ultrasound therapy (US), botulinum-toxin A (BTX-A), dry needing (DN), autologous whole blood (AWB), nonsteroidal anti-inflammatory drugs (NSAIDs), and PRP), analyzed changes in the VAS at 1, 2, 3, 6 months, and ranked the results of the therapy by utilizing the surface under cumulative ranking curve (SUCRA). They found that ESWT was more efficacious than the other seven therapies. In particular, it was superior to the placebo at the 1-, 2- and 3-month VAS change analyses and it was superior to the placebo and CSs at the 6-month analysis. [PERSON], [PERSON] et al. [28] in their meta-analysis compared ESWT without anesthesia treatment and placebo in patients diagnosed with chronic plantar fascitits. In total, 13 studies and 1185 patients were included in the meta-analysis; success or improvement rates, RM score, pain reduction, return to work time and complications were evaluated as outcomes (not specifying the follow up timing). ESWT had better results than other therapy in all the outcomes on study. The authors concluded that ESWT is more effective than placebo, which seems to have negative effects on the outcomes. [PERSON], [PERSON] et al. [38] in their meta-analysis analyzed pain relief (at VAS score) and success rate of ESWT treatment compared with placebo at different follow-up points (1, 3, 6 and 12 months). They compared placebo with different types of SW, and made a distinction based on energy levels and the type of SW administered (FSW or RSW). They concluded that medium-energy ESWT without local anesthesia was more effective on the outcomes on study, regardless of the type of generator. [PERSON], [PERSON] et al. [39] in their systematic review with a network meta-analysis compared the effectiveness of the pain and functional disability of five commonly used therapies on plantar heel pain (PHP), a term comprehensive of PF. Between the five therapies (orthoses, exercise therapy, corticosteroid injections, NSAIDs and ESWT), no statistically significant differences were found at short- (<6 weeks), medium- (6-12 weeks) and long-term (>12 weeks) follow-ups.
# Siamese Tracking with Adaptive Template-Updating Strategy
## 2 Related Work
### Shape-Adaptive Template  
Visual-target detection and tracking are two basic and challenging problems in computer vision. They are highly correlated in most tasks, and the input provided by detection can guide tracking and enhance its performance. At the same time, the precise solution of a detection problem provides reliable observation results for tracking.  
There are a variety of target-detection methods with a bounding-box output. Some recent algorithms are introduced in the work of [PERSON] et al. and [PERSON] et al. [34; 35]. These methods are inspired by the requirements of target detection or tracking, and have been widely used in many applications. However, compared with object-contour detection, the disadvantage of these bounding boxes is that results are inaccurate, and targets cannot be precisely located. In contrast, the results of object-contour detection clearly show the shape and precise position of the target. Using target contour instead of a bounding box to detect or track not only helps improve accuracy, it also tells the system what the target might be according to its shape.  
Figure 2 shows an example of the results of the bounding-box and the object-contour recommendation methods: the results of the bounding-box recommendation method are a rough window, while the results of the object-contour recommendation method are fine pixels. Obviously, the results of the object-contour method contain most target information and least background information compared with the results of the bounding-box method. In the process of feature extraction, background information in the bounding box is tracked as target information.  
In our system, cameras are on aircrafts or Unmanned Aerial Vehicles (UAVs) that are far away from the target, so the initial target is very small in most cases. In addition, most applications  
Figure 2: Results of (**a**) bounding-box and (**b**) object-contour recommendation methods.  
currently used for real-time tracking start with the manual cropping of the bounding box on the target, and it is impossible to generate appropriate boundaries as ground truth in public datasets. In this case, we needed to make our tracker smarter to distinguish between targets and backgrounds. Therefore, we propose a method [36] to extract shape-adaptive templates from targets and track them using a fully convoluted Siamese network. Unlike most startups that use initial bounding boxes in tracking, our requirement was to select the target in real-time video by clicking on the target on the monitor screen. Once someone clicks on any part of the object, tracking information is automatically learned by the system. In particular, our system automatically detects and segments the contours of selected objects at the semantic level. Then, target features are extracted from the contour model without background information. In other words, we let the tracker know which object to track, not the bounding-box area.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 3 Dataset and Methodology
### Spatial-Temporal Self-Attention Graph Convolution Networks (STA-GCN)
#### 3.3.4 Spatial Graph Convolution  
The graph convolutional network (GCN) extends the convolution operation for structured data to graph-structured data. In order to fully utilize the topological relationships between various transportation nodes, spectral graph convolution is used to handle spatial dimension correlations. By analyzing the Laplacian matrix and its eigenvalues, the structural properties of the graph can be obtained. The Laplacian matrix of the graph is represented as \(\mathbf{L}=\mathbf{D}-\mathbf{A}\), and in standard form as \(\mathbf{L}=\mathbf{I}_{N}-\mathbf{D}^{-\frac{1}{2}}\mathbf{A}\mathbf{D}^{- \frac{1}{2}}\), where \(A\) is the adjacency matrix, \(\mathbf{I}_{N}\) is the identity matrix, \(D\) is the degree matrix, \(L=U\mathbf{\Lambda}U^{T}\) is the eigenvalue decomposition of Laplacian matrix, \(\mathbf{\Lambda}=\textit{diag}([\lambda_{0},\dots,\lambda_{N-1}])\in R^{N \times N}\) is a diagonal matrix, and \(U\) is the Fourier basis.  
The traffic parameters at time \(t\) and their information on graph \(G\) are represented as \(x=\mathbf{x}_{t}^{T}\). The Fourier transform of this information is defined as \(\hat{x}=\mathbf{U}^{T}x\). Graph convolution is a convolution operation that can be implemented by replacing the classicconvolution operator with a diagonalizable linear operator \(x\) in the Fourier domain. The formula for the convolution operation of \(g_{\theta}\) on the graph \(G\) is shown in Equation (6).  
\[g_{\theta}*Gx=g_{\theta}\Big{(}\mathbf{L})x=g_{\theta}\Big{(}\mathbf{U}\mathbf{ \Lambda}\mathbf{U}^{T})x=\mathbf{U}g_{\theta}\Big{(}\mathbf{\Lambda})\mathbf{U }^{T}x \tag{6}\]  
Due to the computational complexity caused by all eigenvalues and eigenvectors of the Laplacian matrix in the training process of spectral convolutional networks, this study uses graph convolutional networks based on Chebyshev polynomials to accelerate the solution of the feature matrix. The Chebyshev polynomial, as shown in Equation (7), is used for this purpose.  
\[g_{\theta}(\mathbf{\Lambda})=\sum_{k=0}^{k-1}\theta_{k}\mathbf{T}_{k}( \widetilde{\mathbf{\Lambda}}) \tag{7}\]  
where \(\theta\) is the Chebyshev segment coefficient vector, \(T_{k}(\widetilde{\mathbf{\Lambda}})\). is the \(k\)-th order Chebyshev polynomial of \(\widetilde{\mathbf{\Lambda}}\), and in \(\widetilde{\mathbf{\Lambda}}=2\mathbf{\Lambda}/\lambda N_{max}\lambda_{max}\) is the largest eigenvalue.  
For the \(k\)-th-order Chebyshev polynomial, there is a \(\mathbf{T}_{k}=2x\mathbf{T}_{k-1}(x)-\mathbf{T}_{k-2}(x)\), which has a recurrence relation with the first two terms, \(T_{0}(x)=1\) and \(T_{1}(x)=x\). The convolution operation conducted after approximating the Chebyshev polynomial shown in Equation (8) is depicted in Figure 5.  
\[g_{\theta}*Gx=\mathbf{U}\left(\sum_{i=1}^{K}\theta_{i}\mathbf{T}_{k}( \widetilde{\mathbf{\Lambda}})\right)U^{T}x\approx\sum_{i=1}^{K}\theta_{i} \mathbf{T}_{k}(\widetilde{\mathbf{\Lambda}})x \tag{8}\]
# Cascade or Direct Speech Translation? A Case Study +
###### Abstract  
Speech translation has been traditionally tackled under a cascade approach, chaining speech recognition and machine translation components to translate from an audio source in a given language into text or speech in a target language. Leveraging on deep learning approaches to natural language processing, recent studies have explored the potential of direct end-to-end neural modelling to perform the speech translation task. Though several benefits may come from end-to-end modelling, such as a reduction in latency and error propagation, the comparative merits of each approach still deserve detailed evaluations and analyses. In this work, we compared state-of-the-art cascade and direct approaches on the under-resourced Basque-Spanish language pair, which features challenging phenomena such as marked differences in morphology and word order. This case study thus complements other studies in the field, which mostly revolve around the English language. We describe and analysed in detail the mintzai-ST corpus, prepared from the sessions of the Basque Parliament, and evaluated the strengths and limitations of cascade and direct speech translation models trained on this corpus, with variants exploiting additional data as well. Our results indicated that, despite significant progress with end-to-end models, which may outperform alternatives in some cases in terms of automated metrics, a cascade approach proved optimal overall in our experiments and manual evaluations.  
speech translation; Basque; Spanish; corpus; cascade speech translation; direct speech translation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Methods
### Study Population  
HD Patients. Adults aged 18-70 years at the time of the survey who self-reported a diagnosis of HD were eligible to participate, provided they understood English and agreed to the informed consent. Individuals with juvenile HD and/or those who did not report experiencing at least one of the motor disruptions typical of HD (i.e., balance or gait issues, rigidity, chorea/dystonia, slowed eye movement, difficulty speaking or swallowing) were excluded from participation. These restrictions ensured that study conclusions were isolated to manifest individuals with HD. Consented patients are hereafter referred to as \"individuals with HD\" (IHD, n = 41).  
HD Care Partners. Adults aged 18-70 years at the time of survey who devoted \(\geq\)5 h/week to the care of an adult family member with HD (i.e., reported being involved in their daily activities, emotional support, treatment decisions, and/or accompanied them to medical appointments) were eligible to participate, provided that they understood English and agreed to informed consent. Individuals < 18 years of age, those who were paid professional care takers, and/or did not meet the minimum weekly care requirements were excluded from participation. These restrictions ensured that all care partners were family members as opposed to paid employees; care partners who were reimbursed for their duties by the government or their insurance agency remained eligible to participate. Consented HD care partners are hereafter referred to as \"HD care partners\" (HD-CP, n = 80).  
Comparison Cohorts. In order to contextualize the experiences of IHDs and HD-CPs, relevant cohorts were selected from the NHWS for comparison against the HD cohorts: (a) Individuals who self-reported a diagnosis of Parkinson's Disease (IPD, n = 118) or who reported caring for an adult with Parkinson's Disease (PD-CP, n = 385) enabled a specific comparison of the shared and divergent burdens of HD relative to PD, a separate neurodegenerative motor disorder; (b) Individuals without PD or HD (GP, n = 123) or who reported caring for a patient without PD or HD (GP-CP, n = 240) enabled us to further contrast these burdens relative to the broader General Population.
## 4 Experimental Research
### Comparison of Multi-Feature Extraction Methods
#### 4.6.2 Comparison with Multiple Classical Feature Extraction Methods  
In this section, some experiments were carried out to compare the proposed IMFE with classical feature extraction methods, including AR, WT, CSP, CSSP, and FBCSP. In the experiments, the parameter values of AR and WT were the same as the reference [3; 4], respectively. The parameter values of CSP, CSSP, and FBCSP were the same as the reference [34]. The average classification accuracies with 10-fold CV are shown in Figure 11.  
The classification results of AR, WT, CSP, CSSP, and FBCSP are not as good as those of the IMFE method. This is mainly because these classical feature extraction methods only take into account the information in one domain, including time domain, frequency domain or spatial domain, and they are even completed on the premise that MI-EEG is a linear signal. In fact, MI-EEG is a typical nonlinear signal. IMFE is matched with the nonlinear property of signal, and the parameters' independent optimization of MFE is advantageous for accurately extracting and correctly interpreting the characteristic information of MI-EEG. Furthermore, the minimal standard deviation of IMFE (\(\pm\)2.1) shows the strong stability, and this can better meet the requirements of a real application.  
Figure 10: The average classification accuracy and standard deviation performed by 10-fold CV for IMFE and multiple nonlinear dynamic methods.