esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Speed Prediction
#### 3.1.3 GCN-GRU  
In order to simultaneously capture the spatial and temporal correlations of traffic data, this research uses a combined GCN-GRU model to implement the prediction task. The principle of the model is shown in Figure 3 and is as follows: (1) Use a two-layer graph convolutional network to aggregate the spatial information of first- and second-order neighbors to capture the spatial correlation of traffic flow. (2) Incorporate a gated recurrent unit model to capture the temporal correlation through information transfer between units. (3) Obtain the prediction results through the fully connected layer.  
Figure 3: The structure of GCN-GRU.  
Figure 2: The internal structure of GRU.  
The internal structure of GCN-GRU is shown in Figure 4, where \(Y_{t}\) is the final predicted speed value. The graph convolution operation is calculated as follows:  
\[f(X,A)=\sigma(\hat{A}ReLU(\hat{A}XW_{0})W_{1}), \tag{4}\]  
where \(W_{0}\) and \(W_{1}\) denote the weight matrix in the first and second layers, and \(ReLU\) represents the activation function.  
Then, combined with a gated recurrent unit model, the temporal correlations are captured through information transfer between memory units, and the specific process is as follows:  
\[r_{t} =\sigma(W_{r}[f(A,X_{t}),h_{t-1}]+b_{r}), \tag{5a}\] \[z_{t} =\sigma(W_{z}[f(A,X_{t}),h_{t-1}]+b_{z}),\] (5b) \[c_{t} =\tanh(W_{h}[f(A,X_{t}),(h_{t-1}\odot r_{t})]+b_{h}),\] (5c) \[h_{t} =(1-z_{t})\odot c_{t}+z_{t}\odot h_{t-1}, \tag{5d}\]  
where \(f(A,X_{t})\) denotes the graph convolution process defined in Equation (4), and \(b\) represents the deviation of the model in the training process.  
Overall, through the effective combination of GCN and GRU, the spatio-temporal dependence of traffic data is well modeled to predict traffic speed.
# Fusing Context Features and Spatial Attention to Improve Object Detection
## 5 Proposed Methods  
### Context Features  
We introduced a multi-scale dilated convolution module to extract context features in the original network model, increased the receptive field while maintaining the resolution to make full use of context information, and applied a spatial attention mechanism after fusing the extracted context features and object features.  
\[\begin{split} AM&=\sum_{i=1}^{M}\omega_{i}px_{i}+ \sum_{j=1}^{N}\omega_{j}px_{j}(px_{i}\in CF,px_{j}\in FM)\\ &=\sum_{k=1}^{N}\omega_{k}px_{k},\end{split} \tag{1}\]  
where \(AM\in R^{H\times W\times C}\) represents the extracted attention map, \(CF\in R^{H\times W\times C}\) the context features, \(FM\in R^{H\times W\times C}\) the feature map, \(px_{i}\) the value of the \(i\)-th pixel, and \(N\) represents the number of pixels in the image. Each attention map we extracted was concatenated from the context features and the original feature map with the addition of a weight \(\omega_{k}\).  
First, we used the \(k\)-means method to determine the \(k\) average sizes of the objects and then divided each of the \(k\) average sizes into \(n\) average sizes. Here, we took \(n\) and \(k\), respectively, to be 3 and 4. That is to say, we divided the sizes of the objects into 4 large average sizes and then divided each large average size into 3 small average sizes. We used 4 feature layers for feature extraction, and small objects were extracted in the shallow layer, while large objects were extracted in the deep layer; anchor boxes of 3 sizes were used to locate the objects on each grid point of each feature layer. The adoption of the feature-enhanced extraction FPN (feature pyramid network) could help avoid the problem of insufficient semantic information in shallow networks and their inability to detect small objects. At the same time, it could avoid the decrease in detection speed caused by fusing too many feature layers together. FPN could better integrate deeper semantic information and shallow information to increase the amount of feature extraction information and thereby improve detection accuracy. We extracted the feature information of the objects through ordinary convolution and then extracted the context feature information through multi-scale dilated convolution. The structure of the multi-scale dilated convolution process is shown in Figure 1. Multi-scale features were extracted using dilated convolutions with a convolution kernel size of 3 \(\times\) 3 and expansion rates of 1, 3, and 5, respectively, and multi-scale features were fused to obtain features containing context information. We sent the context feature information and the object feature information into the enhanced feature extraction network for further feature extraction. In Figure 2, the blue part represents the object information features, and the red parts represents the context features. The two sets of features were spliced according to the channel, and the spliced features were sent to the enhanced feature extraction network to obtain information-rich features.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 2 Signal Model and Performance Metrics  
In this section, we consider a general waveform framework, as well as the metrics which can evaluate the performance of a given waveform.
# Design and Implementation of Health and Risk Level Assessment for Socially Disadvantaged Patients
## 4 Performance Analysis
### ROC Analysis  
We conducted ROC analysis for the data correlation analysis of the measured data. An ROC curve is a graph showing the performance of a classification model at all classification thresholds. Before going into detail, we set the cut-off value, above which the participants were predicted as positive (they have the disease) and below which were predicted as negative (they do not have the disease). We set the thresholds at 5 and 15, respectively, as shown in Figures 10 and 11.  
Figures 10 and 11 are diagrams showing the AUROC. ROC is one of the most important evaluation metrics for checking any classification model's performance. The area under the curve has a meaningful interpretation for the classification of monitoring data, determining the optimal cut-off value. It illustrates how much the model is capable of distinguishing between classes. Our model predicts whether a patient has a disease or not and is reasonably effective in distinguishing between patients with disease and without.
## 2 Methodology
### Adsorption Isotherm Models
#### 2.4.2 Freundlich isotherm  
Freundlich [41] derived an empirical formula based on the equilibrium result of the isothermal experiment, which is the Freundlich isotherm adsorption equation. The basic assumption of this model is that the solid surface has different adsorption positions and requires different adsorption energies. The equation is as follows:  
\[\mathrm{q_{e}}=\mathrm{K_{F}}\mathrm{C_{e}}^{\frac{1}{n}} \tag{10}\]  
where \(\mathrm{K_{F}}\) is the Freund isotherm adsorption constant representing the adsorption capacity of the adsorbent, and \(\mathrm{n}\) is the Freund isotherm adsorption constant representing the linear deviation. After taking the log on both sides of Equation (10), it can be converted into a linear form:  
\[\mathrm{logq_{e}}=\mathrm{logK_{F}}+\frac{1}{\mathrm{n}}\mathrm{logC_{e}} \tag{11}\]  
By plotting the log on both sides of Equation (10), a straight line with a slope of 1/n and an intercept of \(\mathrm{K_{F}}\) is obtained. From this, two constants, \(\mathrm{n}\) and \(\mathrm{K_{F}}\), can be determined, where the value of \(\mathrm{n}\) will affect the type of adsorption curve.  
The Freundlich adsorption isotherm can be re-expressed with mass balance [39; 40]; that is, Equation (1) can be replaced by Equation (10):  
\[\frac{\mathrm{K_{F}}\times\mathrm{M}}{\mathrm{V}}\mathrm{C_{e}^{\frac{1}{n}} +\mathrm{C_{e}}-\mathrm{C_{0}}}=0 \tag{12}\]  
Then, the initial solute concentration can be used to obtain the relationship between \(\mathrm{R}\) and \(\mathrm{M}\):  
\[\mathrm{M}=\frac{\mathrm{V}}{\mathrm{K_{F}}}\frac{\mathrm{R}}{\left(1-\mathrm{ R}\right)^{\frac{1}{n}}}\mathrm{C_{0}^{\left(1-\frac{1}{n}\right)}} \tag{13}\]  
Once the Langmuir and Freundlich adsorption isotherm parameters are derived from the data of the adsorption experiment, the equilibrium solute concentration in the solution and the removal efficiency of the adsorbent to the adsorbate or the removal efficiency of the research target can be calculated, and the required adsorbent quality can be determined.
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
## 3 Models Results and Measurements  
### Simulink Thermal Models  
A one-dimensional thermal model was implemented by the thermal library of Simulink-Matlab. By properly setting the boundary conditions, it was possible to calculate the temperature between layers and surfaces, with particular reference to PV cells and batteries. The schemes of the thermal models under consideration are depicted in Figure 5, with reference to the commercial PV module (Figure 5a) and the modified PV module with battery energy storage (Figure 5b). This last model makes reference to the area corresponding to the batteries.  
Temperatures \(T_{a}\), \(T_{s}\) and \(T_{g}\) were set by thermal blocks as ideal temperature sources. Thermal fluxes through the different layers (\(G_{1}\), \(G_{2}\), \(G_{3}\) and \(G_{4}\)) were modeled by ideal heat flux sources. All the heat exchanges were modeled according to the previous sections, properly setting the parameters. Temperatures of the different thermal nodes were detected and displayed by ideal temperature sensors.  
Figure 5: Simulink thermal model without (**a**) and with (**b**) battery energy storage.
## 2 Principle of Standard and Differential Modulation Modes

Table 1: The SOAs parameter values used in the simulation setups. The operating point has been chosen to maximize the contrast ratio (CR), which is given by the ratio between high and low transmissions of the optical power at the SOA-MZI output. The data signal possessed a mean optical power of \(-12\) dBm at 1545 nm (i.e., 194.17 THz) is injected into the SOA-MZI UP. The sampling signal with various optical powers ranging from \(-40\) to 6 dBm was injected at the MP. The wavelength of the sampling signal was 1550 nm (i.e., 193.54 THz). The Figure 3 shows the SOA-MZI static characteristic with a maximum CR = 37 dB for the co-direction and CR = 35 dB for the counter-one. Hence, the maximum transmitted powers of the data signal at the SOA-MZI output were achieved at the peak powers of \(-1\) and \(-3\) dBm for co- and counter-directions, respectively. The dynamic behavior of the SOA-MZI depends on the carrier lifetime and the stimulated carrier recombination time [37]. Both SOAs are biased at the bias current of 350 mA. An optical power of \(-12\) dBm at the wavelength of 1545 nm is injected at the UP and an intensity-modulated optical power of \(-1\) dBm for the co-direction schema and \(-3\) dBm for the counter-direction one at 1550 nm was injected at the MP. At the SOA-MZI output, the modulated optical data signal was photo-detected by a photodiode (PD) and amplified by a low noise amplifier (LNA). The frequency response of the used SOA-MZI in Figure 4 shows a low-pass behavior with an8.5 GHz cutoff frequency for the co-direction schema and 8.3 GHz for the counter-direction one. Figure 4: Dynamic characteristic of the SOA-MZI with the bandwidth of 8.5 GHz for the co-direction and 8.3 GHz for the counter-direction. Figure 3: Static characteristic of the SOA-MZI that represents the transmitted power at the SOA-MZI out as a function of the input optical power at SOA-MZI MP. The contrast ratio (CR) equal to 37 dB is achieved for the co-direction schema and CR equal to 35 dB is achieved for the counter-direction one.
## 3 Dataset Creation Process
### Finding Android Manifest Files  
The initial step in creating the dataset is data collection. This begins with opening a project file in the Google BigQuery console, specifically targeting the github_repos dataset within BigQuery. Within this dataset are the commits, contents, and files tables. The first task is to extract data from the files table. Here, we check for the presence of the AndroidManifest.xml file across all repositories, logging the results in a separate table. If a repository contains this file, it falls within the scope of our study and becomes a source for constructing our dataset. Phases 1-3, illustrated in Table 1, are done in this part. The reasoning for the decision in Phase 3 has some concerns. Firstly, suppose a developer specifies in the commit message that they are addressing a security vulnerability, and only one file is modified. In that case, it is certain that the fix occurred within that file. Secondly, because the goal is to classify the type of security vulnerability addressed in each commit, analyzing fixes that involve multiple files can be complex and may result in misclassification.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Phase** & **Description** & **Condition/Criteria** \\ \hline Phase 1: Codebase Identification & Identify if the repository contains an & The repository must include an \\  & Android application. & AndroidManifest.xml file. \\ \hline Phase 2: Commit Validation & Validate whether the repository includes & The repository must have at least one \\  & code commits. & commit. \\ \hline Phase 3: Android-Specific Fix Check & Ensure the commit modifies & The commit must include changes to at \\  & Android-specific source code. & least one java file. \\ \hline Phase 4: Vulnerability Pattern Match & Analyze commit messages for predefined & Search for keywords indicating \\  & vulnerability-related patterns or & vulnerabilities (e.g., “CVE”, “fix”, \\  & keywords. & “patch”). \\ \hline Phase 5: Deleted Code Analysis & Examine the commit for code deletions & The deleted_lines field in the commit \\  & related to vulnerability fixes. & must not be empty. \\ \hline Phase 6: Dataset Integration & Add valid vulnerable code fixes to the & If conditions are met, integrate the code \\  & dataset. & fix; otherwise, discard. \\ \hline \hline \end{tabular}
\end{table}
Table 1: General conditional workflow of the activities before we do preprocessing.  
Figure 1: Real-world vulnerable commit-based dataset creation process.  
Note that the activities conducted in this part involve writing appropriate SQL statements within the BigQuery console. The relevant SQL queries will also be shared along with the dataset.  
Figure 2 illustrates the workflow for extracting vulnerable commits related to Android applications. Each phase of the process is visually represented, starting from identifying the repository and concluding with integrating relevant fixes into the dataset. The diagram highlights key decision points and conditions for each step.
## 6 Conclusions  
The improvement of linewidth performance of mutually injection locked semiconductor lasers is investigated, and there have been relatively few reports on this aspect. The locking condition and the influence of system parameters on frequency noise is studied experimentally in both short and long delay regimes, demonstrating mutual injection locking as a practical approach for linewidth reduction. The linewidth performance of mutually injection locked lasers is found to improve with increasing coupling length and coupling strength. For the short-delayed integrated lasers, a linewidth narrowing factor of 13 is demonstrated and 0.74 MHz linewidth is achieved, while the intrinsic linewidth is reduced from megahertz to 62.8 Hz for the long-delayed lasers coupled by fiber link.  
Compared with unidirectional injection, laser linewidth is narrowed without the need of a low frequency noise master laser. In comparison with self-injection or external cavity, the proposed approach can be realized simply by an optical coupling path. In addition, mutual phasing-locking provides a special mechanism for linewidth narrowing of mutually coupled lasers. In combination of advanced integration techniques, the proposed method can be employed for narrow linewidth semiconductor lasers which have important applications in coherent optical communication, atomic sensing and microwave photonics.  
Conceptualization, B.X. and Y.L.; methodology, W.M. and C.S.; validation, J.W., Z.H., L.W. and H.L.; investigation, W.M.; resources, X.K., Y.H. and C.S.; data curation, J.Y.; writing--original draft preparation, W.M.; writing--review and editing, M.W., C.S. and B.X.; supervision, Y.L.  
This research was funded by National Key Research and Development Plan of China (2017 YFA0205800, 2018 YFB0406700); Science Challenge Project (Grant No. TZ2016003); National Basic Research Program of China (Grant No. 2015 CB351900); National Natural Science Foundation of China (Grant No. 51561165012, 51561145005, 61574082, 61621064, 61822404, 61875104); Tsinghua University Initiative Scientific Research Program (Grant No. 20161080068, 20161080062, 2015 THZ02-3); China Postdoctoral Science Foundation (Grant No. 2018M640129) and Collaborative Innovation Centre of Solid-State Lighting and Energy-Saving Electronics.  
The authors declare no conflict of interest.
# Real-Time Identification of Look-Alike Medical Vials Using Mixed Reality-Enabled Deep Learning
## 8 User Study and Real-World Validation
### User Feedback and Future Improvements  
The post-experiment surveys collected feedback from participants on usability and potential improvements. The most common suggestions included integrating a voice-assisted interaction system, allowing users to retrieve vial information through verbal commands rather than relying solely on hand gestures. Medical professionals also suggested incorporating multi-user collaboration features to enable simultaneous interactions within the same MR workspace, which could enhance teamwork efficiency in hospital and pharmacy environments. Another significant concern was the bulkiness of HoloLens 2, with some participants preferring an alternative lightweight MR device to improve comfort during extended use.  
Future work will focus on developing occlusion-aware detection models to enhance recognition accuracy for partially hidden vials. Furthermore, further research will explore lightweight augmented reality glasses as an alternative to the HoloLens 2 to provide a more ergonomic and practical solution for prolonged use in real-world medical settings.
###### Abstract  
The modeling of the minimum fluidization velocity (\(U_{\text{0 mf}}\)) and the incipient fluidization pressure drop (\(\Delta P_{\text{mf}}\)) is a valuable research topic in the fluidization field. In this paper, first, a series of experiments are carried out by changing the particle size and material mass to explore their effects on \(U_{\text{0 mf}}\) and \(\Delta P_{\text{mf}}\). Then, an Ergun equation modifying method and the dimensional analysis method are used to obtain the modeling correlations of \(U_{\text{0 mf}}\) and \(\Delta P_{\text{mf}}\) by fitting the experimental data, and the advantages and disadvantages of the two methods are discussed. The experimental results show that \(U_{\text{0 mf}}\) increases significantly with increasing particle size but has little relationship with the material mass; \(\Delta P_{\text{mf}}\) increases significantly with increasing material mass but has little relationship with the particle size. Experiments with small particles show a significant increase at large superficial gas velocity; we propose a conjecture that the particles' collision with the fluidization chamber's top surface causes this phenomenon. The fitting accuracy of the modified Ergun equation is lower than that of the dimensionless model. When using the Ergun equation modifying method, it is deduced that the gas drag force is approximately 0.8995 times the material total weight at the incipient fluidized state.  
Keywords:conical fluidized bed; minimum fluidization velocity; pressure drop; Ergun equation; dimensional analysis +
Footnote †: journal: Applied Physics Letters B
###### Abstract
After observing certain function values, they can be converted into posterior functions through algebraic operations. However, in theory, the Gaussian process will lose its validity in high-dimensional space. [PERSON] et al. estimated the elbow joint torque produced by sEMG through linear and nonlinear dynamic models [17]. However, these methods using traditional machine learning algorithms cannot meet the requirements of current HRC scenarios in terms of accuracy and real-time responses [18]. In recent years, researchers began to focus on continuous motion estimation based on deep learning. These methods are mainly based on advanced time-oriented machine learning methods or deep learning methods. [PERSON] et al. [19] proposed a neural network based approach to predict the mean cutting force in milling progress. [PERSON] et al. [20] developed Takagi Sugeno-Kang (TSK) fuzzy models, which are evolved by an incremental online identification algorithm. [PERSON] et al. [21] investigated the fuzzy Kalman filter (FKF) and improved its implementation by reformulating uncertainty representation. [PERSON] et al. proposed an artificial neural network to estimate the angles of five metacarpophalangeal joints [22]. This work introduced neural networks into the field of continuous motion estimation and verified their feasibility. However, due to the limitations of neural network development at that time, this approach can only estimate simpler gestures. [PERSON] et al. proposed a method based on multilayer perceptrons (MLP) to estimate the motion of multiple joints at the same time [23]. This method divides the sEMG into segments and estimates the joint angle values corresponding to each segment of the sEMG. However, the relevance of the input before and after is not considered, which makes it difficult for the accuracy rate to meet the actual demand. To solve this problem, the recurrent neural network (RNN) has been used for EMG control [24; 25]. The RNN can analyze the time correlation between multiple inputs, which further improves the accuracy of the model. However, the application scenarios of continuous motion estimation are mostly in edge devices, and it is difficult to meet the demand of RNN for computing power, resulting in poor real-time performance. In this paper, we propose a large-scale temporal convolutional network (LS-TCN) to continuously estimate the angles of the 10 main joints of the finger in real time. LS-TCN achieved the estimation accuracy of 71.6%, which is an improvement of accuracy over traditional methods by 6.6%. The rest the paper is organized as follows. We explain the dataset and our methodology in Section 2. Results and discussion are presented in Section 3. Section 4 concludes the paper.