esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Algorithm  
This section presents the proposed algorithm for mining all frequent approximate subgraphs with structural and label variations in nodes and edges in a graph collection, named AGCM-SLV (Approximate Graph Collection Miner with Structural and Label Variations).  
AGCM-SLV works as follows: First, the algorithm mines all frequent nodes to obtain a set of frequent subgraphs containing a single node. After that, a subgraph expansion process begins, where frequent subgraphs are expanded into larger candidate subgraphs by connecting neighboring nodes and adjacent edges according to a depth-first traversal strategy. Each expansion (candidate subgraph) is verified to determine if it satisfies both the frequency and dissimilarity thresholds. Otherwise, the candidate subgraph cannot be expanded further and must be pruned (discarded) due to the anti-monotonicity property. The expansion process continues until there are no more frequent subgraphs to expand.  
Each frequent subgraph mined \(P\) is associated with its occurrences in the graph collection, which determines the subgraph's frequency. These occurrences can have structural and label variations in both nodes and edges. Label variation results from substituting node or edge labels, while structural variations result from representing additional or missing nodes and edges in the frequent subgraph. Because of these variations in labels and structure, each occurrence has a dissimilarity value with the frequent approximate subgraph it represents. This dissimilarity cannot exceed a predefined dissimilarity threshold (\(\beta\)). The pseudocode for the proposed algorithm is shown in Algorithm 1.  
```
0:\(D\): graph collection, \(\alpha\): support threshold, \(\beta\): dissimilarity threshold, \(\gamma\): node dictionary (optional), \(\delta\): edge dictionary (optional)
0:FS: all unique FREQUENT_SUBGRAPHS
1:\(P\leftarrow\) Obtain all FREQUENT_NODES in the graph collection \(D\)
2:for every \(subgraph\)\(G^{\prime}\) in \(P\)do
3: candidate_subgraphs list \(\leftarrow\)EXPAND_FREQUENT_SUBGRAPHS(\(G^{\prime}\))
4:for every candidate_subgraphs in candidate_subgraphs_list do
5:for every occurrence \(o\) of \(G^{\prime}\)do
6: expanded_occurrence \(\leftarrow\)EXPAND_OCCURRENCE(\(o\))
7:if\(dissimilarity(expanded\_occurrence\), candidate_subgraphs) \(\leq\beta\)then
8: expanded_occurrence is an occurrence of candidate_subgraphs
9:ifSupport(candidate_subgraphs,\(D\)) \(\geq\alpha\)then
10:if candidate_subgraphs has not been mined then
11:insert candidate_subgraphs into \(P\) and into FS
```  
**Algorithm 1** Proposed algorithm: AGCM-SLV
## 4 Some Illustrations of the Effects of Parameters \(p\) and \(\lambda\)  
The process for calculating the p(x)-Laplacian on an image is that of the implementation in Algorithm 1. The effect parameters \(p\) and \(\lambda\) of the model on an image are illustrated in Figure 2.  
1. In the case \(0<p<1\), the pictures are partitioned into several homogeneous areas;
2. In the case \(1\leq p\leq 2\), when \(p\) approaches 2, there is a smoothing effect, whereas when \(p\) converges on 1 there is no smoothing effect;
3. The increase in parameter \(\lambda\) limits the effects of \(p\).  
Overall, when acting on the value of these parameters, several smoothing levels are reached which make the pictures striking or blurred.  
Figure 2: Effects of parameters \(p\) and \(\lambda\) on the Lena image. From left to right, parameter \(p\) increases to values \(\{0.01;1.00;2.00\}\). From top to bottom, the lambda increases by taking values \(\{0.01;0.10;1.00\}\).
## 3 Results
### CPR Performance with No-Feedback and with Real-Time Feedback System
#### 3.1.3 Compression Rate  
The medical participants performed infant CPR using the TT compression technique, see Figure 5c. The mean value of the achieved chest compression rate using the TT compression mechanism during no performance feedback was 83.96 \(\pm\) 8.87 cycle/min compared to the compression rate parameter mean value 106.68 \(\pm\) 8.43 cycle/min with feedback. The median value of the compression rate parameter with no feedback and feedback performance were 85 cycle/min and 110 cycle/min, respectively. The difference and percentage of difference between the no feedback and feedback group were 25% and 29%. During CPR, there was an improvement in the compression rate parameter with the feedback of approximately 27% compared to the CPR performance without feedback. There was a significant difference in the compression rate parameter with feedback-assisted performance compared to unassisted performance (\(p<0.05\)).  
In addition, the medical participants performed infant CPR using the TF compression technique, see Figure 5c. The mean value of the achieved chest compression rate during no feedback was 81.84 \(\pm\) 6.65 cycle/min compared to the compression rate parameter mean value 101.36 \(\pm\) 5.16 cycle/min during feedback. During CPR, there was an improvement in the compression rate parameter with the feedback-performance of approximately 24% compared to CPR performance without feedback. The median value of the compression rate parameter during  
The median value of the compression rate parameter during no feedback and feedback performance were 78 cycle/min and 102 cycle/min, respectively. The difference and percentage of difference between the no feedback and feedback group were 24% and 31%. There was a significant difference in the compression rate parameter with feedback-assisted performance compared to unassisted performance (\(p<0.05\)).
## 4 Experimental Section
### Overall Results on Part I Experiment  
We first calculated the \(P\), \(R\), and \(F1\) values of the complex question decomposition model BTAM with other baseline models on CWQS dataset, and the results are shown in Table 4. It can be seen from Table 4 that the three evaluation metrics of BTAM model we proposed on CWQS dataset outperformed the baseline models with \(P\), \(R\), and \(F1\) values of 89.4%, 78.7%, and 83.7%, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{P}\) & \(\mathbf{R}\) & \(\mathbf{F1}\) \\ \hline SPLITQA & 57.6\% & 43.0\% & 49.2\% \\ DECOMPRC & 56.3\% & 48.0\% & 51.8\% \\ BiLSTM\_CRF & 48.2\% & 38.0\% & 42.5\% \\ BiLSTM\_CNN\_CRF & 55.4\% & 38.2\% & 45.2\% \\ Bert\_BiLSTM\_CRF & 83.5\% & 65.6\% & 73.5\% \\ BTAM & **89.4\%** & **78.7\%** & **83.7\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of different models on ComplexWebQuestions dataset.  
SPLITQA model performs poorly on CWQS dataset test, with its \(F1\) value 34.5% lower than BTAM model. This is due to SPLITQA model's relatively simplistic structure, which makes the model less capable of extracting semantic features from questions when simple questions are given as input.  
DECOMPRC model also performs poorly since it only examines the semantic information of the questions and lacks context when only ten to twenty words are provided as input to the questions.  
The \(F1\) values for the BiLSTM_CRF and BiLSTM_CNN_CRF models are 42.5% and 45.2%, respectively. This is due to the tiny amount of data in the dataset and the lack of other contextual information, which makes learning the true distribution of the problem challenging for the small-scale model. Because of the use of pre-training for encoding, the Bert_BiLSTM_CRF model outperforms the BiLSTM_CRF without pre-training by 31% in \(F1\).  
The BTAM model we proposed employs a dependency encoder for question feature extraction, allowing the model to extract semantic information from the question even when contextual information is unavailable. Furthermore, this model uses transfer training, which improves understanding of the questions.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 3 Method
### Architecture of DGAN  
The overall framework of DGAN is illustrated in Figure 2. For each skeleton action sequence with variable length, the sequence is normalized to a fixed length \(T\), where each frame contains \(M\) persons, each person has \(V\) joints, and each joint has a feature dimension of \(C_{in}\). The skeleton sequence is represented as \(X\in\mathbb{R}^{C_{in}\times T\times V\times M}\). After initial preprocessing, the dimension of \(X\) is transformed to \((M\ast V\ast C,T)\), followed by batch normalization and reshaping to \((M,C,T,V)\). The reshaped sequence is then fed into the DGAN backbone to extract spatio-temporal features. Subsequently, the residual connections and layer-wise downsampling are applied to obtain multi-scale temporal receptive fields. These features are further fed into the classification module after processing through the backbone.  
In the DGAN module, the data are first processed by the dynamic attention graph learning module, which calculates attention weights between joints to dynamically learn spatial correlations. Meanwhile, point-wise attention is computed to enhance the model'sability to focus on key joints, allowing the model to adaptively capture both local and global spatial dependencies within the skeleton structure.  
For the MSTC module, the input features are fed into several temporal convolution branches with different dilation rates and kernel sizes, enabling the model to capture temporal dependencies at different scales. This design enhances the model's ability to extract both short- and long-range temporal features. The extracted features are fused through a summation operation, and global average pooling is applied to reduce the dimensionality. The output is then passed through a fully connected layer and softmax activation function for final classification.  
The entire DGAN model is trained under a cross-entropy loss function in a supervised manner, using the predicted action category and the ground-truth label for supervision.
## Materials and Methods
### Topology Optimization  
Topology optimization is also conducted using Inspire software. The optimization function is defined in two different ways: maximizing stiffness, and minimizing mass. In order to obtain realistic solutions, a shape controller is established for obtaining symmetrical pieces with respect to the longitudinal plane of the beams. Although the software allows it, no restrictions on frequencies were considered.  
Inspire allows the use of two variables for maximizing stiffness: the percentage of maintained mass and the minimum thickness. In this context, thickness indicates the thinness of the structure supports in the design region of the beam. Therefore, two experiments are established with the objective of maximizing stiffness: OT1, which sets the minimum thickness at 300 mm and defines a range of maintained mass from 90% to 10% at 10% intervals. Likewise, OT2 sets the percentage of maintained mass at 50% and uses the minimum thickness as a variable, which is established in the range from 200 to 500 mm, at 100 mm intervals. For the experiments whose objective is to minimize the mass, the minimum thickness of the solutions is set 300 mm and the factor of safety (_FOS_) is used as a variable, using values from 1 to 3 at 0.25 intervals (OT3). OT4 establishes a constant _FOS_ of 1.2 and uses the minimum thickness as a variable, which is established in the range of 200 to 500 mm, at 100 mm intervals. In Figure 6, optimization experiments, constants and variables are summarized.  
Figure 5: Display of boundary conditions in Inspire software.  
Figure 6: Decision parameters for topology optimization experiments.  
Finally, the mechanical resistance of the optimized beams is evaluated using the Inspire software according to Section 2.3. Additionally, their fractal dimensions are also estimated. Since the estimation of the fractal dimension is carried out using a box-counting algorithm in Matlab, it is necessary to export the optimized beams from Inspire to a compatible Matlab file. Lightweight solutions are exported using OBJ format. From this format, the XYZ coordinates of the point cloud comprised by the meshing nodes of the optimized beam can be extracted. This file of coordinates XYZ is employed as input for determining the fractal dimension by means of the box-counting algorithm in Matlab.
## 3 Experimental Setup
### Ground Truth GVV  
A ground truth GVV signal is necessary to compare the performance of the proposed algorithm. However, a measurement of GVV is infeasible because there is no sensor available to directly measure the airflow in the glottis. An alternative is to obtain a GVV estimate from an external sensor, e.g., an oral flow mask. Following the same method for IBIF calibration (Section 3.1), the SNF method is used in this work to calculate the ground truth GVV. Even though this ground truth is an estimation of the true glottal flow (due to the difficulty of obtaining directly the latter signal), the SNF method has been successfully applied in previous work related to GVV estimation in sustained vowels [5; 9; 29; 36; 58]. Since we have running speech in this case, the optimization procedure that finds the best notch frequency and bandwidth is done in every 50 ms non-overlapping frame. A simple voice activity detector based on the autocorrelation method [51] is used to remove unvoiced frames. The signal is reconstructed from individual frames by using the overlap-and-add method [51].
# Data-Driven Robust Control Using Reinforcement Learning
## 2 Materials and Methods
### Robust Control Using Reinforcement Learning
#### 2.1.4 Robust Reinforcement Learning Algorithm  
The robust RL algorithm for controlling partially unknown dynamically systems includes the following steps:  
Initialization (Step \(i=0\))
* Select an initial policy \(u(t)=-K_{0}x(t)\).  
Estimation of the Value Function (Step \(i=1,2,\ldots\))
* Apply the control action \(u(t)\) based on the current policy \(u(t)=-K_{i}x(t)\).
* At time \(t+T\), collect and compute the dataset \((X,Y)\), which are defined in Equations (11) and (13).
* Update vector \(w\) by using the batch least-square method (Equation (10)).  
Control Policy Update  
* Transform vector \(w\) into the kernel matrix \(P\) using the Kronecker transformation.
* Update the policy by solving the LMI in Theorem 1.  
Figure 1 shows the simplified diagram of the above algorithm. It is noted that the estimation of the value function is an on-policy learning since it updates \(V^{\pi}(x(t))\) using the V-value of the next state and the current policy's action.  
Figure 1: Data-driven robust reinforcement learning diagram.
# New Insights into the Mechanism of Spatiotemporal Scent Accumulation in Orchid Flowers
###### Abstract  
Orchid flowers have a unique structure that consists of three sepals and three petals, with one of the petals forming the labellum (lip) that can be differentiated into the hypochile and epichile. In orchids, the emission of floral scent is specific and spatially complex. Little is understood about the molecular and biochemical mechanisms of the differing scent emissions between the parts of orchid flowers. Here, we investigated this in the _Cattleya_ hybrid KOVA, and our study showed that monoterpenes, including linalool and geraniol, are the main components responsible for the KOVA floral scent. The KOVA flower was scentless to the human nose before it reached full bloom, potentially because the _1-deoxy-D-xylulose 5-phosphate synthases_ (_RCDXSs_) and _4-hydroxy-3-methylbut-2-enyl diphosphate synthases_ (_RCHDSs_) that biosynthesize monoterpenes were highly expressed in flowers only when it reached full flowering. Additionally, the spatial expression profile of the _monoterpene synthases_ (_RcMTPSs_), which were highly expressed in the basal region of the lip (hypochile), contributed to the highest monoterpene emissions from this part of the flower. This might have caused the hypochile to be more fragrant than the other parts of the flower. These findings enrich our understanding of the difference in scents between different flower parts in plants and provide information to breed novel orchid cultivars with special floral scents.  
Keywords: floral scent; mechanism; orchid; monoterpene; flower +
Footnote †: journal: _plants_
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 3 Experimental Setup  
The reliability of the proposed thermal model in terms of the accuracy was validated experimentally. In particular, an experiment was performed to measure the temperature distribution of the entire belt drive and compare it with the corresponding results obtained from the thermal model under various operating conditions. Therefore, a rigorous experimental arrangement was established before validating the thermal algorithm with the multi-pulley configuration, as described in this section.
## 3 Results and Discussions
### Longitudinal Microchannel  
The distributions of both velocity and wall shear stress within a longitudinal microchannel were studied by other researchers, and they found that the length of the channel did not affect the velocity and the shear stress within the microchannels [22; 26; 55]. In this study, a longitudinal microchannel was designed to investigate the shear stress and the velocity profile distribution within the channel using three different flow rates alongside the width of the microchannels (Figure 2a). In the same way, through a streamline plot, it was possible to study the distribution of the fluid within the microchannel (Figure 6a-c).  
Figure 6a-c shows that the fluid moves parallel to the walls within the microchannel, reaching the highest velocity magnitude in the centre of the channel and a minimum velocity at the walls due to the no-slip condition. The wall shear stress values were calculated for the flow rates using Equation (5) prior to simulation (0.02, 0.06, 0.1 Pa, respectively).  
The results obtained from the simulations show a uniform distribution of the shear stress within the microchannel (Figure 6d-f). Furthermore, to analyse the distribution of fluid within the microchannel, the velocity and the wall shear stress profiles were plotted against the width of the channel. As the ratio between the height and width of the channel is close to 1 (h = 200 \(\upmu\)m and w = 250 \(\upmu\)m), the resulting velocity profile, which is evaluated along the rectangular cross-section microchannel width, has a parabolic shape. The wall shear stress profiles have been calculated at two different heights (h = 0 \(\upmu\)m, representing the bottom of the microchannel and h = 100 \(\upmu\)m at the middle of the microchannel) of the microdevice. As shown in Figure 6h, moving from the bottom (h= 0 \(\upmu\)m, solid lines) of the microchannel to the middle (h = 100 \(\upmu\)m, dotted lines), the wall shear stress has high values near the wall and low values in the middle (Equation (3)). Since no significant colour variation was observed within each microchannel when the flow rate increased, both velocity and wall shear stress remained uniform throughout the channel (Figure 6a-f). Figure 6i indicates the mesh study of the longitudinal microchannel and shows a total of 310,604 elements, which is adequate to guarantee a grid-independent solution. Additionally, cell culture studies by other scientists have demonstrated that constant velocity and shear stress profiles through the channel can promote cell adhesion [26] and cell alignment in a fluid direction within the reorganisation of their cytoskeleton [24], along with focal adhesion formation, which are fundamental properties to ensure cell survival [53]. Other researchers have also studied fluid flow behaviour in straight channels [24; 56; 57]. However, their studies have only used a single flow rate value. This study, instead, focused on evaluating the effects of different flow rates within the channel on shear stress and velocity.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 3 Problem Formulation
### Problem Definition  
The special characteristics of the resource allocation problem in the C-V2X mode 3 network include two main aspects: the high vehicle mobility and the different requirements for different types of links. Considering the rapid variations in channels' conditions caused by the high vehicle mobility, our proposed resource allocation scheme depends solely on the slowly varying large-scale channel parameters and the distributions of the small-scale channel parameters, thus significantly reducing the signaling overheads and making the scheme more practical. On the other hand, the V2I connections desire large transmission capacity and allocation fairness among CUEs, while V2V connections place greater emphasis on link reliability and low communication latency. Thus, the resource allocation problem can be formulated as \[\max\sum_{m\in\mathcal{M}}\sum_{n\in\mathcal{N}}x_{m,n}U_{n}\Big{(} \mathbb{E}\Big{\{}R_{m,n}^{C}\Big{\}}\Big{)}\] \[\text{s.t.}\] \[\text{C1:}\quad P\{D_{k}>D_{\max}\}\geq p_{0},\ \forall k\in \mathcal{K}\] \[\text{C2:}\quad\lambda_{k}=-\frac{1}{\theta_{k}^{VT}}\ln\mathbb{E }\Big{\{}e^{-\theta_{k}^{VT}TW\log_{2}(1+\gamma_{k,n}^{V})}\Big{\}},\ \forall k\in \mathcal{K}\] \[\text{C3:}\quad\sum_{n\in\mathcal{N}}x_{m,n}\mathbb{E}\Big{\{}R_ {m,n}^{C}\Big{\}}\geq R_{0},\ \forall m\in\mathcal{M}\] \[\text{C4:}\quad 0\leq\sum_{n\in\mathcal{N}}x_{k,n}\leq 1,\ \forall k\in \mathcal{K} \tag{11}\] \[\text{C5:}\quad 0\leq\sum_{n\in\mathcal{N}}x_{m,n}\leq n_{q},\ \forall m\in\mathcal{M}\] \[\text{C6:}\quad 0\leq P_{k}^{V}\leq P_{\max}^{V},\ \forall k\in \mathcal{K}\] \[\text{C7:}\quad 0\leq\sum_{n\in\mathcal{N}}x_{m,n}P_{m,n}^{C} \leq P_{\max}^{C},\ \forall m\in\mathcal{M}\] \[\text{C8:}\quad 0\leq P_{m,n}^{C}\leq P_{n,\max}^{C},\ \forall m\in \mathcal{M},\ \forall n\in\mathcal{N}\]  
where \(p_{0}\) is the LVP threshold of the VUEs, and \(R_{0}\) denotes the minimum ergodic capacity requirement of the CUE. Constraints C1 and C3 represent the reliability requirements and minimum capacity for each VUE and CUE, respectively. Constraint C2 stipulates that the channel's effective capacity is equal to the source data rate. Constraint C4 presents that each VUE can be assigned at most one RB. Constraint C5 models our assumption that each CUE is assigned at most \(n_{q}\) RBs. \(n_{q}\) denotes the quota of a CUE, which can be calculated by \(\lfloor P_{\max}^{C}/P_{n,\max}^{C}\rfloor\). Constraints C6 and C8 represent the VUE's and CUE's maximum power limits, and constraint C7 stipulates the maximum transmission power on an RB of the CUE, which is denoted by \(P_{n,\max}^{C}\).
## 4 BPSK FDA System Design
### Transmitted Packet Structure  
The structure of one transmitted packet is shown in Table 1. Note the structure of Table 1 matches visually with the BPSK modulation in Figures 3 and 4. The Barker code (BC) is used to bookend the bit information. The 'buffer bits' have a constant value of \(1\) and are used during regions without modulation.
## 3 System Description and Main Components
### The Physical World
On one side, IoT power meters are connected to the building electric panel boards, measuring the energy consumption of the individual electric lines. On the other side, HVAC, lighting, and other energy consumers (e.g., workstations) are managed by means of IoT smart plugs. Additionally, environmental IoT sensors collect indoor temperature, relative humidity, and presence data.