esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For DOA estimation in MIMO radar with mutual coupling, to the best of our knowledge, up to now, there are no references about SR-based methods that exploit the signal noncircularity to extend the virtual array. Consequently, utilizing noncircular sources and the sparse representation framework, we aim to eliminate the effect of mutual coupling and then achieve a better DOA estimation. In this paper, by using the noncircular signals, we propose a sparse representation-based DOA estimation algorithm in MIMO radar with mutual coupling. The new method firstly eliminates the effect of mutual coupling by extracting the information of mutual coupling coefficients into a diagonal matrix. Secondly, based on the reduced dimensional transformation and the characteristics of signal noncircularity, the new extended received data matrix is formulated to enlarge the array aperture. Finally, a reweighted matrix is constructed, based on which a joint reweighted \(l_{1}\)-norm minimization sparse representation framework is designed to obtain the DOAs. In the presence of mutual coupling, the proposed algorithm performs better than the conventional algorithms on account of the error elimination and the application of noncircular signals. The rest of this paper is organized as follows. In Section 2, the MIMO radar system model with mutual coupling and noncircular signals is described. In Section 3, the implementation process of the proposed method is described in detail, including mutual coupling elimination, noncircular signal-based extended matrix construction and the joint reweighted sparse representation-based DOA estimation scheme. In Section 4, we give some related remarks and discussions regarding the parameter setting and the computational complexity of the proposed method. Then, in Section 5, simulations are implemented to verify the efficiency and the advantages of the proposed method. Finally, our conclusions are summarized in Section 6. Notation: \((\cdot)^{\rm H}\), \((\cdot)^{\rm T}\), \((\cdot)^{*}\), \((\cdot)^{-1}\), \(\rm E(\cdot)\) and \(\rm det(\cdot)\) denote conjugate-transpose, transpose, conjugate, inverse, expectation and determinant operators, respectively. \(\otimes\) and \(\odot\) denote the Kronecker product and the Khatri-Rao product, respectively. \(\mathbf{I}_{K}\) denotes a \(K\times K\) dimensional unit matrix. \(\|\cdot\|_{1}\), \(\|\cdot\|_{2}\) and \(\|\cdot\|_{F}\) denote the \(l_{1}\)-norm, the \(l_{2}\)-norm and the Frobenius-norm, respectively. \(\rm diag(\cdot)\) denotes the diagonalization operation.
## Results  
### Polymethoxflavones and \(D\)-Limonene Contents in Orange Peel Oil  
Cold-pressed citrus peel oil mainly consists of volatile terpenoids and non-volatile components. The terpenoids generally do not absorb UV light; therefore, they cannot be determined by HPLC-UV, but they are suitable for GC-MS analysis. In contrast, the non-volatile compounds, like polymethoxyflavones, absorb more UV light, but they have a higher boiling point; thus, they are typically separated and analyzed using HPLC with a UV detector.  
The HPLC chromatogram of the non-volatile components in long-leaf orange peel oil (OPO) is shown in Figure 2. By comparing the retention time (Rt) and UV spectra of the oil sample (Figure 2B) and those of the mixed standards (Figure 2A), eight polymethoxyflavones (Compounds 2-9) were identified in the OPO. The small peak at 3.2 min (Figure 2B) was confirmed to not be isosinensetin (1) because its Rt was slightly later than the corresponding standard peak (3.0 min), and their 210-400-nm UV spectra were different, as shown above each chromatogram. The total ion current (TIC) obtained from GC-MS analysis of the volatile components in OPO is shown in Figure 3. The most abundant peak was identified as \(D\)-limonene.  
Figure 2: HPLC chromatograms of long-leaf orange peel oil (OPO) showing polymethoxyflavones (PMFs). Notes: (**A**) The mixed standard chromatogram with the UV spectrum of Compound 1; (**B**) separation and identification of the non-volatile compounds in OPO and the UV spectrum of the peak with Rt = 3.2 min. Numbers 1-9: 1, isosinensetin; 2, sinensisetin; 3, hexamethoxyflavone; 4, tetramethyl-O-isoscutellarein; 5, nobitetin; 6, tetramethyl-O-scutellarein; 7, heptamethoxyflavone; 8, 5-demethylnobitein; and 9, tangeretin.  
The quantitative results of the PMFs and limonene are shown in Table 2. In general, \(D\)-limonene accounts for more than 95% of the total volatiles, and it is the major component of orange peel oil; the non-volatiles, which are dissolved in limonene, consist primarily of PMFs, as these compounds account for more than 91% of the total peak area. Specifically, nobiletin and heptamethoxyflavone were the most abundant PMFs in long-leaf OPO, followed by tangeretin, tetramethyl-\(O\)-scutellarein and hexamethoxyflavone. 5-Demethylnobiletin, the 5-hydroxyl derivative of nobiletin, was the least concentrated PMF in long-leaf OPO.
# 3D Face Model Super-Resolution Based on Radial Curve Estimation
## 3 Method
### The Radial Curves' Extraction  
As shown in Figure 1, radial curves on a face model are a set of curves passing the nose tip, and they can be defined by the intersections between the face model and a set of planes obtained by rotating the facial symmetrical plane around the normal direction of the nose tip. Thus, we have to locate the nose tip point and the symmetrical plane of a 3D face model for radial curve extraction. Assuming 3D face models are triangle mesh models, we first perform principal component analysis for the vertices of the face model to establish an initial coordinate system, i.e., three principal orientations as the coordinate axes with \(Y\) axis throughout the front and back of the face, and then fit a cylinder to the 3D face model [13] and adjust the \(Z\) axis to be parallel to the cylinder's axis. The nose tip is the most protruding point on the face's surface, so the point of the maximal value in \(Y\) direction is chosen as the nose tip. In order to establish a uniform coordinate system, we adjust the \(Y\) axis to be parallel to the normal vector of the nose tip with the nose tip being the coordinate center (see Figure 2). In order to get rid of the influence caused by the head size on the following radial curve estimation, we normalize all high resolution face models in size by a scale transform determined by several landmarks, such as nose tip and the corner points of the eyes and mouth, which can be labeled manually. With the nose tip, we estimate the symmetrical plane of the 3D face model using the method proposed in [14].  
As illustrated in Figure 1, we get the first radial curve by calculating the intersection curve of the symmetrical plane and face mesh, and then rotate the symmetrical plane around the normal direction of the nose tip by a fixed angle \(\beta\) gradually to extract the other radial curves. A radial curve is initially represented in a group of intersection points of the plane and edges of the triangle patches in the face mesh. For the following radial curve registration, we uniformly sam  
Figure 1: (**a**) Symmetrical plane (shown in green). (**b**) Radial curves (shown in red) on 3D face model.  
Figure 2: (**a**) Initial coordinate system. (**b**) Standard face model coordinate system. \(X\), \(Y\) and \(Z\) axes are shown in red, green and blue respectively.  
a fixed sampling internal \(\Delta\) (here, 0.01 by experience). Then each radial curve is represented in a point sequence.
## 4 Experimental Environment and Results
### Field Study Results
#### 4.3.2 Second Phase Implementation Results  
The second phase expanded the implementation scale significantly. As shown in Table 6, the research was extended to nine more diverse sites, encompassing 30 sessions with 1338 participants. Compared to the first phase, participation increased by 102.8%, with the site types diversified to include performance events (n = 452), school settings (n = 285), and early education institutions (n = 215). The system optimization effectiveness analysis demonstrated significant improvements across all evaluation metrics (\(p\) < 0.001). The overall SUS score increased from 77.85 (\(\pm\)2.31) to 82.15 (\(\pm\)2.15), showing a 5.52% improvement with a significant effect size ([PERSON]'s d = 0.92). The learnability scores improved from 89.12 (\(\pm\)1.98) to 94.12 (\(\pm\)1.87), representing a 5.61% increase (d = 0.86). Notably, the usability scores showed the most significant improvement, rising from 75.03 (\(\pm\)2.45) to 82.46 (\(\pm\)2.33), marking a 9.90% increase (d = 1.24).  
The evaluation of the intergenerational interaction effect yielded particularly noteworthy results. As indicated in Table 3, the elderly-children co-learning mode demonstrated significant advantages across all metrics:  
* The overall SUS score reached 92.12 (\(\pm\)2.31), surpassing the elderly-only group's 80.70 (\(\pm\)2.45) by 14.15%;
* The learnability reached 94.12 (\(\pm\)1.98), exceeding the elderly-only group's 89.25 (\(\pm\)2.12) by 5.46%;
* The usability reached 91.62 (\(\pm\)2.15), surpassing the elderly-only group's 78.56 (\(\pm\)2.33) by 16.62%.  
The second phase specifically introduced health monitoring functionalities. The 12-week tracking data demonstrated that this addition not only maintained the system's usability but enhanced its overall effectiveness:  
1. Activity level analysis: * The bilateral tapping analysis showed a 23.5% improvement in coordination;
* The total tapping statistics reflected steadily increasing participation levels;* The exercise intensity classification indicated increased moderate-intensity activity ratios.
* Physiological-indicator changes:
* The pre-post blood oxygen level comparisons showed significant improvements;
* The heart rate variation trends demonstrated good exercise tolerance;
* The activity endurance assessments reflected the users' progressively improving exercise capacities.
## 2 Related Work
### Topology-Based Routing Protocols  
The topology-based routing protocol is one major protocol type for wireless networks, especially for the mobile ad hoc network. Several routing protocols have been defined within the working group of IETF, including Ad hoc On-demand Distance Vector routing (AODV) [7], Dynamic Source Routing (DSR) [8], Temporally Ordered Routing Algorithm (TORA) [9], Destination-Sequenced Distance-Vector routing (DSDV) [10], Topology Broadcast based on Reverse-Path Forwarding (TBRPF) [11], Optimized Link State Routing (OLSR) [12], Zone Routing Protocol (ZRP) [13], Fisheye State Routing (FSR) [14], landmark routing (LAND-MAR) [15], etc. These protocols either use a kind of flooding to detect routes on-demand or proactively maintain routing information at each node. However, these routing protocols need a big amount of signaling information exchange, especially for the proactive strategy, where the routing table should be maintained and updated dynamically. This will not be efficient to be applied in sensor networks, especially for a vehicular sensor network, and some dedicated strategy should be designed as follows.
## 3 Key Aspects for Practical Implementation of DL-Based NOMA
### Transceiver Design  
A recent study [43] focuses on explaining how deep learning aids in overcoming the NOMA as mentioned above difficulties. The multiuser receiver (Rx) is initially improved using deep learning from a model-driven and data-driven perspective. The authors briefly explain how deep learning may enable the optimization of end-to-end NOMA transceivers with practical transmitter (Tx) restrictions or domain knowledge. End-to-end learning is used in NOMA to integrate computation and communication. The authors investigate how deep learning can extract and use upper-layer data for transceiver design. They conclude by outlining some exciting new avenues for deep-learning-enhanced NOMA in mMTC.  
Multiuser Detection Design [98; 99; 100; 101; 102; 103; 104; 105; 106]  
Different users' signals are sent in a non-orthogonal fashion in NOMA. Generally, multiuser detectors (MUDs) are used at the receiving end to differentiate between the overlapping signal streams, thereby minimising inter-user interference (IUI). For several NOMA systems, state-of-the-art MUDs have been created, including parallel interference cancellation (PIC), sequential interference cancellation (SIC), and message-passing algorithms (MPA). Unfortunately, multi-user detection still lacks a unified signal processing framework. By using DNN to improve MUD, we may get a more unified architecture, higher detection accuracy, and shorter processing times. DNN-based concepts may be roughly divided into two distinct camps: data-driven and model-driven. Vanilla DNNs are used in a data-driven strategy, which reduces the time spent on design but increases the amount of data needed for training. Alternatively, a model-driven method uses domain-specific knowledge from NOMA to reduce the need for data and increase learning efficiency. In a recent study [103], the authors propose a DL method that automatically analyzes the CSI of the communication system and detects the original transmit sequences. Figure 6 shows the symbol error rate (SER) and SNR curve of the numerical simulation. The proposed MIMO-NOMA-DL reached 12.6 dB, whereas the traditional scheme reached 16.2 dB--a difference of approximately 3.6 dB. The authors used powerful DL tools to perform accurate signal detection rather than traditional complex signal processing for channel estimation and demodulation.  
Figure 5: Performance of NOMA user pair through DNN-based technique [89].
## 3 Methodology
### Evaluation
#### 3.5.1 SHAP Feature Importance  
SHAP assigns each variable an importance value for a particular prediction. Variables with large absolute Shapley values are important. Since we want global importance, we average the absolute Shapley values per variable across the data.  
The following is the formulation for SHAP feature (variable) importance [21]:  
\[\frac{1}{n}\sum_{i=1}^{n}|\phi(i)_{j}|\]  
In this equation, \(\phi(i)_{j}\) represents the contribution of variable \(j\) to the prediction for instance \(i\). The sum is taken over all instances in the dataset, and the absolute values of the contributions are averaged by dividing by the total number of instances, \(n\). The resulting value represents the variable importance, indicating the magnitude of the contribution of a variable to the model prediction outcome.  
By calculating the SHAP feature importance, we can assess the relative importance of different variables in the model's decision-making process. Higher values indicate stronger influences, while lower values suggest a lesser impact on the model's predictions. This information helps to understand the significance of each variable and provide insights into the underlying relationships between the variable and the target variable.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 2 Materials and Methods
### Enhanced YOLOv5 Model
#### 2.2.3 Context Aggregation  
In the task of weed detection, to enhance the detection performance of deep object detection models in complex backgrounds, the Context Aggregation module was introduced into the YOLOv5 network structure. Its core idea lies in the adaptive fusion of context information at different scales using a contextual aggregation approach, aiming to improve the model's robustness and accuracy. The composition of this module is depicted in Figure 4. The broadcast Hadamard product is a type of matrix operation, defined as \(C_{i,j}=A_{i,j}B_{ij}\), denoted as \(A\odot B\).  
Figure 3: PConv, Faster-Block, and C3-Faster Structure Diagrams (**(a)**: PConv; **(b)**: Faster-Block; **(c)**: C3-Faster).  
In this paper, we utilize the context aggregation module to replace the \(1\times 1\) convolution in the head of the neural network. This module operates through three key steps:  
Attention Weight Calculation: The initial step involves computing attention weights. This is achieved by applying a \(1\times 1\) convolution layer followed by a sigmoid activation function. This ensures that the attention weights fall within the range of 0 to 1. The objective here is to automatically adjust the weights based on the importance of input features, facilitating effective feature fusion.  
Context Information Fusion: After obtaining attention weights and feature values, a matrix multiplication operation is performed between the feature values and attention weights. This operation leads to an adaptive weighted fusion of context information. This approach enables the model to better perceive the relationship between objects and backgrounds based on the importance of features in different regions. Notably, this fusion method excels in feature fusion across different scales, contributing to enhanced object detection performance.  
Feature Transformation and Output: The weighted features are subsequently subjected to a \(1\times 1\) convolution layer to enhance their representational capacity. These features, which have undergone context information fusion, constitute the final output features. Importantly, these features possess improved discrimination capabilities between targets and backgrounds in the weed detection task.  
By integrating the context aggregation module into YOLOv5, an adaptive fusion of context information across different scales has been achieved. This enhancement significantly boosts the model's performance. The introduction of an adaptive context information fusion method equips the model to better handle complex backgrounds and variations in target sizes encountered in weed detection tasks.
# A New Method for Constructing the Static-Voltage-Stability Boundary with a Conversion Strategy
## Nomenclature  
\begin{tabular}{l l} \(f(\cdot,\cdot)\) & Power flow equation \\ \(h(\cdot)\) & Boundary mapping function \\ \(<\cdot,\cdot>\) & Inner product of two vectors \\ \(\mathbf{v},\mathbf{\theta}\) & Bus voltage magnitudes and phase angles \\ \(\Omega\) & Static-voltage-stability region \\ \(\partial\Omega\) & Static-voltage-stability-region boundary \\ \(x\) & Voltage variable vector \\ \(\mathbf{y}\) & Nodal injection power variable \\ \(h\) & Boundary mapping function \\ \(\lambda\) & Load stability margin \\ \(J\) & Jacobi matrix of power flow equation \\ \(L\) & Right eigenvector with an eigenvalue equal to 0 \\ \(\mathbf{\psi}(\cdot)\) & Transfer boundary mathematical model \\ \(\beta_{k}\) & Power-growth-direction angle \\ \(\eta,\alpha\) & Continuous parameter with respect to \(\lambda\) and \(\beta_{k}\) \\ \(\overset{\rightarrow}{b}_{k}(\alpha)\) & Load growth direction with a continuous parameter \\ \(\Delta\mathcal{S}\) & The tangent vector of the model variable \(\mathcal{S}\) \\ \(S_{0}\) & Initial numerical value of \(\mathbf{\psi}(\cdot)\) \\ \(\mathcal{S}^{\text{pre}}\) & Predictor of variable \(\mathcal{S}\) \\ \(S^{\text{cor}}\) & Corrector of variable \(\mathcal{S}\) \\ \(\Theta_{k}\) & The convergence domain at point \(\mathcal{S}_{k}\) \\ \(d\) & Direction indication factor \\ \(\mathbf{e},\mathbf{d}\) & Direction indication vector \\ \(\epsilon\) & An appropriate small parameter \\ \(\mathbf{\mathcal{E}}\) & The set of the continuous parameter \(\alpha\) \\ \(L_{j}(\alpha)\) & The Lagrange coefficient \\ \(\mathbf{\Phi}(S^{\text{cor}})\) & The arc-correction equation \\ \end{tabular}
# Comparison of the Usefulness of CBCT and MRI in TMD Patients According to Clinical Symptoms and Age
## 3 Results  
The age distribution of TMD patients (n = 325) was the highest at 20-29 years (28.9%), followed by 30-39 years (16.9%). The overall age distribution is shown in Figure 3. Female (n = 253) were 3.5 times more frequent than male (n = 72).  
The clinical symptom of 473 joints were frequent in order of pain (77.8%), sound (74.6%) and limited mouth opening (33.6%). In the image analysis, at least one MRI finding was found in 89.0% (n = 421) and at least one CBCT finding was found in 47.1% (n = 223). The proportions of types I-IV, defined based on the simultaneous presence of CBCT and MRI findings, were 7.6% (n = 36), 3.4% (n = 16), 45.2% (n = 214), and 43.8% (n = 207), respectively.  
Table 1 shows the type of CBCT and MRI findings according to clinical symptoms. Type III showed a higher proportion than type II in all the clinical symptoms of pain, sound and limited mouth opening.  
Table 2 shows the distribution of the four types according to age. Type III was at a higher frequency than type II in all age groups and the frequency of type IV increased with age.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Type I** & **Type II** & **Type III** & **Type IV** \\ \hline Sound (n = 353) & 7.9 (28) & 2.8 (10) & 47.9 (169) & 41.4 (146) \\ Pain (n = 368) & 7.1 (26) & 2.4 (9) & 41.0 (151) & 49.5 (182) \\ Limited mouth opening (n = 159) & 3.1 (5) & 5.0 (8) & 37.8 (60) & 54.1 (86) \\ \hline \end{tabular} n; number.  
\end{table}
Table 1: Type of CBCT and MRI findings according to the clinical symptoms % (number).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Age (years)** & **Type I** & **Type II** & **Type III** & **Type IV** \\ \hline
10–19 (n = 75) & 14.7 (11) & 1.3 (1) & 53.3 (40) & 30.7 (23) \\
20–29 (n = 142) & 4.9 (7) & 5.6 (8) & 58.5 (83) & 31.0 (44) \\
30–39 (n = 86) & 8.1 (7) & 1.2 (1) & 46.5 (40) & 44.2 (38) \\
40–49 (n = 65) & _7.7_ (5) & 1.5 (1) & 35.4 (23) & 55.4 (36) \\
50–59 (n = 59) & 3.4 (2) & 3.4 (2) & 32.2 (19) & 61.0 (36) \\
60 \(\geq\) (n = 46) & 8.7 (4) & 6.5 (3) & 19.6 (9) & 65.2 (30) \\ \hline \end{tabular} n; number.  
\end{table}
Table 2: Distribution of the four types according to age % (number).  
Figure 3: Age distribution of temporomandibular joint disorders (TMD) and type III.  
The age distribution of type III is shown in Figure 3. Type III had the highest frequency at 20-29 years, followed by a frequency at 10-19 years, and tended to decrease with age.  
For the age group of 10-29, type III was statistically significantly more frequent than other types (Type I, II, IV) (Table 3).