esa-sceva/satcom-chunk-collection huggingface dataset:
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 4 MSGCN Framework
### Aggregating Node Representations  
In the subgraph convolution step, each subgraph \(G_{i}\) outputs the representation matrix \(Z_{i}\).  
After obtaining the representation matrix \(Z_{i}\) of each subgraph, we take the aggregation operation to get the final node embedding representation \(H\in R^{|V|\times d}\), as shown in Equation (5):  
\[H=softmax(\sum_{i\in[1,|R|]}w_{i}*z_{i}), \tag{5}\]  
where weight \(w_{i}\) is determined according to the number of edges of each subgraph. The equation of calculating \(w_{i}\) is given as follows:  
\[w_{i}=|E_{i}|/|E| \tag{6}\]  
The experimental results show that the weight allocation method is unreasonable. For each node \(v_{j}\in V\), the influence of each subgraph, that is, each edge type, on the node \(v_{j}\) is different. Therefore, we cannot simply calculate the weight of the whole subgraph. For example, comparing one paper quotes another paper with one paper written by an author, we think that the former is more likely to provide the information we want. So for a particular node, some edge types are important, while some edge types have a much lower impact on that node. Accordingly, the weight of each subgraph should be different when the center node aggregates information from subgraphs.  
When determining the weight of each subgraph (edge type) on each node, we choose the method based on the number of edges. For each node \(v_{j}\in V\), the final node representation is defined in Equation (7):  
\[H_{j}=softmax(\sum_{i\in[1,|R|]}D_{ij}*Z_{ij}), \tag{7}\]  
where \(D_{i}\) represents the degree matrix of the subgraph \(G_{i}\), \(Z_{i}\) is the node representation matrix of \(G_{i}\). Here \(i\) indicates which subgraph it belongs to, and \(j\) indicates which node it is. So we can get the final representation of H for all the nodes. The node representations obtained by this method can not only retain the topology information of the network, but also the semantic information of the network and the content information of the nodes, which is very comprehensive.
# Effect of Slot Opening Width on the Air-Gap Magnetic Field of a Direct Drive Permanent Magnet Motor
###### Abstract  
The direct drive permanent magnet motor (DDPMM) is a promising candidate for applications because of its high efficiency, high power density, and low maintenance costs. This study focused on the effect of slot opening width on the air-gap magnetic field of a DDPMM. An exact analytical model based on Fourier analysis was established to calculate the air-gap magnetic field. The analytical general solution of the air-gap magnetic field shows that the slot opening greatly affects the air-gap magnetic field distribution when the amount of the permanent magnet is constant. Then, the air-gap magnetic fields of the DDPMM with closed and open slots were compared at different positions. Furthermore, several finite-element models of motors composed of different numbers of unit motor were established based on the different slot opening widths to study the effect of slot opening width on the air-gap magnetic field. The results obtained using the finite element method verify that the slot opening width greatly affects the air-gap magnetic field.  
air-gap magnetic field; direct drive permanent magnet motor; exact analytical model; Fourier analysis; slot opening width +
Footnote †: journal: Applied Physics Letters
## 3 Multi-AGV Path Planning Based on Time Window
### Multi-AGV Priority Design  
In the process of multi-AGV conflict handling, priority design is a necessary prerequisite to determine the AGV traffic order. This study adopts the method of combining task priority and number priority to set AGV priority. First, all AGVs are numbered in integer order starting from 1. When AGV conflicts occur, AGVs are prioritized according to the following principles:  
1. AGVs that are loading and unloading and are idle have the highest priority;
2. AGVs that are performing transportation tasks have a high priority;
3. When multiple AGVs are performing transportation tasks, the AGV whose transportation task started earlier has a higher priority;Principle 4: When the task start time is the same, the AGV with a smaller AGV number has a higher priority.  
Thus, in the process of multi-AGV operations, the improved A* algorithm can be used to obtain the optimal path of each AGV. Then, the time window method is used to determine whether these optimal paths will conflict on a certain grid cell at a certain time. At the same time, according to the AGV before and after the grid position to determine the conflict type, combined with the AGV priority setting rules, the multi-AGV conflict-free path planning can be realized.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 3 Proposed Method
### Loss Function
#### 3.2.1 Edge Loss  
The edge loss is measured in terms of Chamfer distance (CD) [50]. Given two point clouds of ground-truth and predicted edges:  
\[\mathcal{L}_{e}=\frac{1}{n_{g}}\sum_{p_{i}\in S_{1}}\underset{p_{j}\in S_{2}}{ min}\big{\|}p_{i}-p_{j}\big{\|}_{2}+\frac{1}{n_{p}}\sum_{p_{j}\in S_{2}} \underset{p_{i}\in S_{1}}{min}\big{\|}p_{i}-p_{j}\big{\|}_{2}, \tag{3}\]  
where \(S_{1}=\{p_{1},...p_{i},...,p_{n_{g}}\}\subset\mathbb{R}^{3}\) is the set of the ground-truth edges in a given point cloud and \(S_{1}\) contains \(n_{g}\) points, \(|S_{1}|=n_{g}\). Each point of \(S_{1}\) is comprised of three coordinates \(p_{i}=(p_{1}^{1},p_{i}^{2},p_{i}^{3})\), \(1\leq i\leq n_{g}\). Furthermore, \(S_{2}\) is the set of the predicted edge points by EDC-Net model from a given point cloud. \(S_{2}\) contains \(n_{p}\) points denoted by \(S_{2}=\{p_{1},...p_{j},...,p_{n_{p}}\}\subset\mathbb{R}^{3}\), \(|S_{2}|=n_{p}\); and each point of \(S_{2}\) contains three coordinates \(p_{j}=(p_{j}^{1},p_{j}^{2},p_{j}^{3})\), \(1\leq j\leq n_{p}\). For the first summation, given a point \(p_{i}\) in the ground-truth point cloud \(S_{1}\), find the nearest corresponding point \(p_{j}\) in the predicted point cloud \(S_{2}\). A similar process is performed from \(p_{j}\) to \(p_{i}\) for the second summation. \(S_{1}\) and \(S_{2}\) do not necessarily contain the same number of points. Chamfer distance is differentiable and is computationally efficient.
# Recent Spatiotemporal Changes of Subhourly Extreme Rainfall Events in Seoul
## 4 Results  
### Temporal Changes in Extreme Rainfall Events  
Figure 3 presents the observed AMP at the selected stations across different durations for each year, represented through boxplot visualizations. For the 15 min duration, the box sizes in Figure 3 remained relatively consistent across different years. However, from the 30 min duration onwards, there were substantial variations in the box sizes each year. For durations exceeding 1 h, the spatial pattern of AMP in the Seoul region demonstrated significant interannual differences, with these variations becoming more pronounced as the duration increased.  
Figure 3: _Cont._  
The annual AMP mean series from the selected stations for each duration were plotted to examine the temporal variations in AMP across Seoul, as shown in Figure 4. Furthermore, the MK test was applied to the AMP mean series across all durations to assess the statistical significance of these temporal changes. The AMP mean series exhibited a decreasing trend over time across all durations, with the rate of decline less pronounced for shorter durations. For durations of 300 and 360 min, the MK test revealed statistically significant decreasing trends at significance levels of 10% and 5%, respectively. The AMP means in Seoul demonstrated a gradual decline, which became more pronounced with increasing duration. Notably, the AMP means for subhourly durations in 2018 were relatively larger than in other years, whereas the AMP means for hourly durations in 2018 were comparatively smaller. Since Seoul experienced significant flood damage in 2018, it can be inferred that extreme rainfall during subhourly durations contributed to urban flooding in the city.  
Figure 4: _Cont_.  
Figure 3: Boxplots of annual maximum precipitation (AMP) for selected stations for each year at various durations: (**a**) 15 min, (**b**) 30 min, (**c**) 45 min, (**d**) 60 min, (**e**) 120 min, (**f**) 180 min, (**g**) 240 min, (**h**) 300 min, and (**i**) 360 min.
## 3 Results  
### Chemistry  
Scheme 1 represents the performed synthesis of the three C-ring oxidized E1 acetate analogs explored in the present study as previously described [32; 39; 40; 41]. The acetylation of compound **E1** (96% yield) was successfully achieved using Ac\({}_{2}\)O and DMAP [39]. Compound **4** was synthesized at an acceptable yield through the 9\(\alpha\)-hydroxylation procedure involving the in situ formation of dimethyldioxirane as described in the literature [40]. The oxidation of **E1** acetate using CAN [41] allowed the preparation of 9\(\alpha\)-hydroxy-11\(\beta\)-(nitrooxy)-17-oxoestra-1,3,5(10)-trien-3-yl acetate (compound **5**). The preparation of steroid **6** was firstly attempted by means of a DDQ-mediated dehydrogenation of estrone acetate **2**[32]. However, in addition to incomplete substrate consumption, a mixture of inseparable products was obtained. Therefore, we decided to start the synthesis of compound **6** by the successful transformation of estrone **E1** into \(\Delta^{9,11}\)-estrone **3**, again by using DDQ, as previously described [24; 32]. Then, the acetylation of compound **3** allowed the preparation of compound **6** with an 89% yield. Starting from this 3-acetylated compound, two additional C-ring oxidized E1 derivatives were also tried to prepare. For this, by application of allyic oxidation reaction conditions, previously described by some of us [42], we attempted to synthesize 12-oxo-\(\Delta^{9,11}\)-estrone acetate. However, low reactivity was observed, accompanied by the formation of a complex mixture of products (TLC control). In addition, by using \(m\)-chloroperoxybenzoic acid [43], we could prepare 9\(\alpha\),11\(\alpha\)-estrone acetate. However, in the purification step to isolate the pure 9\(\alpha\),11\(\alpha\)-diastereoisomer, we found that this compound was unstable and was quickly transformed into several products (TLC control). This fact was also previously reported [44]. Therefore, this compound was not included in the present study.  
Spectral analysis (IR, 1H- and 13C-NMR; spectra presented in Supplementary Information) for all prepared compounds are in agreement with the literature [22; 28; 30; 31]. The existence of the acetate group in compounds **2** and **6** was evidenced by a signal near 2.26 ppm (1H-NMR). The presence of the 9\(\alpha\)-hydroxyl functional group of compound **4** was detected by the appearance of OH signals in the IR spectrum as well as by a signal at 70.21 ppm in the 13C-NMR spectra when compared with spectral data for compound **2**. In addition, the signal of C11 proton (compound **6**) at 6.21 ppm indicated the presence of the \(\Delta^{9,11}\) double bond. Moreover, the triplet at 5.76 ppm corresponds to the typical signal of the 11\(\alpha\)-hydrogen of the 9\(\alpha\)-hydroxy-11\(\beta\)-nitrooxy derivative **5**[22].
# External Slot Relationship Memory for Multi-Domain Dialogue State Tracking
## 4 Experiments  
### Datasets  
MultiWOZ is a task-oriented, multi-domain, manually annotated multi-turn dialogues dataset. It is currently the largest annotated dialogue dataset for task-oriented dialogue systems. The dataset consists of seven domains: attraction, hospital, police, hotel, restaurant, taxi, and train. It contains a total of 10,438 dialogue instances. Approximately 70% of the dialogues consist of more than ten turns, showcasing the complexity of the corpus. As shown in Figure 3, the average number of turns for single-domain and multi-domain dialogues is 8.93 and 15.39, respectively, totaling 115,434 turns. MultiWOZ decomposes the dialogue modeling task into three subtasks to evaluate the quality of the dialogue model based on the performance of each subtask: dialogue state tracking, dialogue act-text generation, and dialogue context-text generation. In this paper, we experiment with the proposed ER-DST model using the MultiWOZ 2.0 [9] and MultiWOZ 2.1 [10] as the testing benchmarks.
## Abbreviations  
\begin{tabular}{l l} _AF_ & Acceleration factor \\ _BX_ & Cycles that are an accumulated failure rate of X\%: durability index \\ \(D\) & (Thermodynamic or propelling) force \\ _E\({}_{a}\)_ & Activation energy of a chemical reaction, eV \\ \(e\) & Effort in a multiport system \\ \(f\) & Flow in a multiport system \\ _F(t)_ & Unreliability \\ \(h\) & Testing time (or cycles) \\ _h\({}^{*}\)_ & Nondimensional testing cycles, \(h^{*}=h/L_{B}\geq 1\) \\ _h\({}_{a}\)_ & Actual test time (or cycles) \\ \(k\) & Boltzmann's constant quantity, \(8.62\times 10^{-5}\) eV/deg \\ \(K\) & Reaction speed \\ \(L\) & Phenomenological transport quantity \\ _L\({}_{B}\)_ & Target BX life and \(x=0.01\)_X_, on the circumstances that \(x\leq 0.2\) \\ \(n\) & Number of test samples \\ \(r\) & Unsuccessful numbers \\ \(S\) & Stress \\ \(T\) & Temperature, K \\ _ti_ & Testing cycle for individual sample \\ _TF_ & Time to failure \\ \(X\) & Cumulated failure rate, \% \\ \(x\) & \(x=0.01\)_X_, on condition that \(x\leq 0.2\). \\ Greek symbols \\ _\(\xi\)_ & Electrical field applied \\ _\(\eta\)_ & Characteristic life \\ _\(\lambda\)_ & Cumulative damage quantity in Palmgren-Miner's rule \\ _\(\chi^{2}\)_ & Chi-square distribution \\ \(a\) & Confidence level \\ Superscripts \\ _\(\beta\)_ & Shape parameter in Weibull distribution \\ \(n\) & Stress dependence, \(n=-\left[\frac{\partial ln(T_{j})}{\partial ln(S)}\right]_{T}\) \\ Subscripts \\
0 & Normal stress circumstances \\
1 & Elevated stress circumstances \\ \end{tabular}
## 3 Three-step WLS Localization Algorithm for Hybrid Positioning
\begin{split} W=Cov(\varphi)^{-1}=E\left(\varphi\varphi^{T} \right)^{-1}=\left[\begin{array}{ccccc}\sigma_{toa}^{2}&0&0&\ldots&0\\ 0&r_{2}\sigma_{tdaa}^{2}&0&\ldots&0\\ 0&0&{r_{3}}^{2}\sigma_{tdaa}^{2}&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&{r_{M}}^{2}\sigma_{tdaa}^{2}\end{array}\right]^{-1}\\ =\frac{1}{\sigma_{tdaa}^{2}}\left[\begin{array}{ccccc}k^{2}&0&0&\ldots&0\\ 0&{r_{2}}^{2}&0&\ldots&0\\ 0&0&{r_{3}}^{2}&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&{r_{M}}^{2}\end{array}\right]^{-1}\.\end{split}  \tag{17}\]  
where \(Cov(\varphi)^{-1}\) is the inverse of the covariance matrix of \(\varphi\), \(k\) is obtained by prior probability statistics of the ranging errors after the system deployed. Scaling of \(W\) does not affect the result of WLS [17]. Therefore, the weighting matrix \(W\) can be written as:  
\[W=\left[ \begin{array}{ccccc}\frac{1}{k^{2}}&0&0&\ldots&0\\ 0&\frac{1}{r_{2}}^{2}&0&\ldots&0\\ 0&0&\frac{1}{r_{3}}^{2}&\ldots&0\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&\ldots&\frac{1}{r_{M}}^{2}\end{array} \right]. \tag{18}\]  
However, the distances from ANTs and UE is unknown, to approximated the matrix \(W\), an initial guess of UE position is need to be obtained in first-step WLS.
###### Abstract  
In order to research the effect of different layout forms of steel plate on the axial compression behavior of a steel plate-concrete composite shear wall, this paper presents the experimental results and analysis of the axial compression behavior of a composite shear wall, with different layout forms of steel plate. A total of three tests were carried out, composed of two composite walls with built-in steel plate, and one composite wall with two skins of steel plate. The gross dimensions of the three specimens were 1206 mm \(\times\) 2006 mm \(\times\) 300 mm. Experimental results show that the composite wall with two skins of steel plate has an optimal ability of elastic-plastic deformation, and the maximum axial compressive bearing capacity among the three specimens. Using the energy method, the critical local buckling stresses of steel plate were calculated, and compared with the yield stresses. According to different confined actions of concrete, concrete constitutive models were proposed, and the axial compressive strengths of confined concrete were calculated. Considering the local buckling of steel plate and confined concrete, the calculation formula of the axial compression of the composite wall was put forward, and the calculated results were in good agreement with the test results. Therefore, the different layout forms of steel plate have a great influence on its buckling, and on the concrete inhibition effect, which can affect the axial compressive bearing capacity of the composite wall.  
composite shear wall; axial compressive bearing capacity; local buckling; concrete inhibition effect 1
## 2 Study Area and Data Processing
### Ground Weather Data  
The weather data set at seven stations (Figure 1) from 1 January 2014 to 31 December 2016 was collected from the Department of Meteorology and Hydrology (DMH), Myanmar. These seven weather stations are not included in the Global Precipitation Climatology Centre (GPCC) gridded gauge-analysis precipitation data set or the Climate Prediction Center (CPC) Unified Gauge-based Analysis of Global Daily Precipitation data set. The local time is six and a half hours ahead of Coordinated Universal Time (UTC). Local weather stations measure precipitation at 3-h time intervals in the daytime (00:00-12:00 UTC), while precipitation data are collected only once at night (12:00-00:00 UTC). Thus, precipitation data in one diurnal cycle are composed of four 3-h precipitation amount records in the daytime and one 12-h record at night time. The weather data set also includes the daily maximum and minimum near-surface air temperature data from 1 January 2014 to 31 December 2016. DMH has conducted data quality control on the ground weather data, which includes checks of internal consistency, extreme values, and spatial consistency.  
The 12-h precipitation records at night time were evenly decomposed into four 3-h precipitation values, and the inverse-distance weighting method was used to interpolate the gauge-based precipitation and air temperature data to the 0.25\({}^{\circ}\) spatial resolution to facilitate 3-hourly flood simulations using a distributed hydrological model (Section 3.3).
## 2 Related Work
### Human Motion Analysis with Dynamics Model  
Dynamic modeling is crucial to understand and predict human motion, as this physics-based approach can describe the spatiotemporal relationships of joints during complex movements. From a biomechanical perspective, various simplified and physics-based human dynamic models have been proposed [26; 27; 28]. [PERSON] et al. [29] hypothesized that the human body is a multi-particle system, and employed [PERSON]'s second law to estimate the external forces acting on the body. However, in human motion tracking, the complex structure and non-rigid deformations of the human body result in significant errors when simplifying it to a particle system. [PERSON] et al. [30] proposed a Multibody Dynamic Model (MDM) with 23 degrees of freedom, which estimated poses and motions by analyzing forces. In the MDM framework, joint forces are first determined through inverse dynamics calculations, then the forward kinematic equations are integrated using a Runge-Kutta integrator to obtain the generalized coordinates at any given time. Finally, the generalized coordinates are converted into poses, thereby enabling the prediction of poses or motions at any specific time.  
Recently, motion analysis and prediction methods that integrate dynamic equations with neural networks have garnered extensive attention. [PERSON] et al. [31] proposed a human motion prediction method based on a dynamic model, establishing an arm dynamic model through Lagrangian mechanics, utilizing RNNs to predict future muscle forces, and employing an Unscented Kalman Filter (UKF) to handle the nonlinear dynamic model, thereby forecasting future arm movements. [PERSON] et al. [32] extended the Mixture of Experts (MoE) concept from deep learning to the domain of human motion and dynamics prediction, effectively predicting the interaction torques between the human body and the ground. [PERSON] et al. [33] used deep neural networks to estimate joint torques and external contact forces, thereby capturing the underlying dynamic information during human motion.  
In skeleton-based motion prediction or action recognition, simple dynamic information, such as acceleration obtained from second-order differencing, is either input alongside positional information into the backbone network or provided as additional information to the prediction head [34; 35]. [PERSON] et al. [36] attempted to estimate the interaction forces between joints as dynamic information, integrating kinematic and dynamic knowledge to describe motion from different aspects. However, the role of dynamic information has not been thoroughly explored. To extract and fully utilize human dynamic information, we model the human body as a floating-base multi-rigid-body dynamic system, extract joint torques from pose sequences, and ultimately use them for motion prediction.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 4 Experiments
### Effect of WML  
In this subsection, we evaluate WML combined with the above mentioned features. LBP + WML, DLBP + WML and TDLBP + WML denote LBP, DLBP and TDLBP with the proposed WML, respectively. We choose \(r=150\) in Equation (13) when learning \(M\), and the number of PSMs \(K=8\). The results are shown in Table 5 where we can observe that TDLBP + WML achieves the best performance in all multi-view recognitions once again. Comparing Table 5 with Table 4, the proposed WML achieves  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**One View** & **The Other View** & **LBP + ML** & **DLBP + ML** & **TDLBP + ML** \\ \hline MOC\_e & CAMS\_e & 34.26 & 65.98 & **67.24** \\ MOC\_e & IAP\_e & 43.81 & 72.46 & **73.53** \\ CAMS\_e & IAP\_e & 34.63 & 66.71 & **69.25** \\ CAMS\_e & MOC\_e & 41.96 & 71.35 & **72.12** \\ IAP\_e & CAMS\_e & 36.50 & 67.23 & **68.89** \\ IAP\_e & MOC\_e & 46.75 & 73.88 & **74.05** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Multi-view cloud recognition accuracies (%) using LBP, DLBP, and TDLBP with metric learning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**One View** & **The Other View** & **LBP** & **DLBP** & **TDLBP** \\ \hline MOC\_e & CAMS\_e & 31.38 & 63.25 & **64.87** \\ MOC\_e & IAP\_e & 41.24 & 69.56 & **70.85** \\ CAMS\_e & IAP\_e & 32.54 & 65.18 & **66.32** \\ CAMS\_e & MOC\_e & 39.17 & 68.82 & **69.65** \\ IAP\_e & CAMS\_e & 33.86 & 65.23 & **67.74** \\ IAP\_e & MOC\_e & 42.95 & 70.83 & **71.41** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-view cloud recognition accuracies (%) using different features.  
better results than ML when using the same features, because it considers the imbalanced sample problem by using a weight strategy.  
In order to further verify the effectiveness of the WML, we compare WML with SMOTEBoost [43] and RUSBoost [44] based on TDLBP. SMOTEBoost and RUSBoost are the representative methods for alleviating the problem of class sample imbalance where we make use of the default optimal parameters for them. From Table 6, the proposed TDLBP + WML still achieves the best recognition result in all multi-view recognition cases. The performances of SMOTEBoost and RUSBoost are very similar, but RUSBoost is a preferable alternative for learning from imbalanced data because it is simpler, faster, and less complex than SMOTEBoost.