esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Numerical Results
### Experimental Setup
#### 4.1.3 Baselines  
For the FB15K237 and WN18 RR datasets, we carefully selected models from different families between 2015 and 2023, whose results were taken from their original paper, to serve as baselines. In the machine-learning-based model family, we chose classical models such as DisMult, RotatE, QuatE, MuRE, and MuRP. These models employ spatial geometric embeddings and have been widely utilized in KG completion tasks. In the deep-learning-based model family, we selected the classical convolutional neural network model ConvE, as well as recent models including HypER, MTL-KGC, StAR, and KGLM. All of these models utilize Transformer architectures, which have achieved state-of-the-art performance and have significantly contributed to the advancement of KG link prediction.  
The aforementioned models have not been experimentally recorded on the CoDEx-M and CoDEx-L datasets. Therefore, we selected models and experimental results from the original CoDEx article, namely TransE, RESCAL, ComplEx, ConvE, and TuckER. This choice was made to ensure consistency with previous research and to maintain comparability by using the same benchmarks for evaluation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Datasets** & \(|E|\) & \(|R|\) & **Training** & **Validation** & **Test** \\ \hline FB15K-237 & 14,541 & 237 & 272,115 & 17,535 & 20,466 \\ WN18 RR & 40,943 & 11 & 86,835 & 3034 & 3134 \\ CoDEx-M & 17,050 & 51 & 185,584 & 10,310 & 10,311 \\ CoDEx-L & 77,951 & 69 & 551,193 & 30,622 & 30,622 \\ \hline \end{tabular}
\end{table}
Table 2: Dataset settings.
## 4 Experiments and Results
### Evaluation Metrics  
The anomaly detection method proposed in this paper is based on flow prediction, and the accuracy of detection is related to the accuracy of prediction. The evaluation metrics are all selected as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE). Where: \(x^{t}\) is the observed value, \(\hat{x}^{t}\) is the predicted value, and \(n\) is the total number of samples.  
MAE measures the average absolute value of the prediction errors, which represents the average deviation between the predicted values and the true values. The smaller the value, the more accurate the prediction. The calculation formula is as follows:  
\[MAE=\frac{1}{n}\sum\limits_{t=1}^{n}|x^{t}-\hat{x}^{t}|. \tag{24}\]  
RMSE calculates the root mean square error, which is the square root of the average of the squared errors. Since squaring amplifies larger errors, RMSE emphasizes the impact of large errors. It is suitable for scenarios that are sensitive to large errors. The smaller the value, the better the model's fit. The calculation formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{t=1}^{n}(x^{t}-\hat{x}^{t})^{2}}. \tag{25}\]  
MAPE measures the percentage of the error relative to the true values, providing a dimensionless error metric. The smaller the value, the smaller the model's prediction error. The calculation formula is as follows:  
\[MAPE=\frac{100\%}{n}\sum\limits_{t=1}^{n}\left|\frac{x^{t}-\hat{x}^{t}}{x^{t} }\right|. \tag{26}\]  
The Nash-Sutcliffe Efficiency (NSE) coefficient is commonly used to quantify the predictive accuracy of simulation models and is widely applied in evaluating hydrological, meteorological, and environmental models. NSE is an indicator of the degree of fit between the model's predictions and observed values. It calculates the mean square error between the predicted results and the observed values, and compares it to the variance of the observed values. The range of NSE is from \(-\infty\) to 1, with a value closer to 1 indicating better model performance. The mathematical expression for NSE is as follows:  
\[NSE=1-\frac{\sum\limits_{t=1}^{n}\left(x^{t}-\hat{x}^{t}\right)
# Pedestrian Trajectory Prediction Based on Dual Social Graph Attention Network
## 3 Methods
### Dual Social Graph Attention Network
#### 3.3.2 Spatiotemporal Weighted Attention Network  
The purpose of this study is to better aggregate the weight features of directed edges. Inspired by the weighted signed graph attention network (WSGAT) [37] for link prediction, we improved the graph attention network and propose the spatiotemporal weighted attention network branch STWGAT. As shown in Equation (7), the classic Graph Attention Network (GAT) uses the same weight matrix \(W^{l}\) to perform the calculation with the target node \(i\) and the adjacent node \(j\), and then linearly combines them together, which limits the richness of feature expression.  
\[e^{l}_{ij}=a^{T}_{k}(W^{l}h_{i}||W^{l}h_{j}) \tag{7}\]  
In Equation (7), \(h_{i}\) and \(h_{j}\) represent the feature vectors of target node \(i\) and adjacent node \(j\), respectively. \(W^{l}\) denotes the learnable weight matrix for the l-th layer, and \(a^{T}_{k}\) is the attention vector. \(e^{l}_{ij}\) represents the attention coefficient between node \(i\) and node \(j\) at the l-th layer, indicating the relationship weight between the two nodes.  
In our method, we calculate the weight of each edge based on the attention mechanism and then aggregate it, which is different from the common GAT method. As shown in Equation (8), we introduce the Multilayer Perceptron (MLP) to propagate the target node \(i\), the adjacent node \(j\), and the weight between them together.  
\[e^{l}_{ij}=MLP^{l}(h^{(l)}_{i}||h^{(l)}_{j}||w^{l}_{ij}) \tag{8}\]  
Thus, we can see that the new edge attention coefficient of node \(j\) to node \(i\) in the \(l\)-th layer can be calculated as follows:  
\[\alpha^{l}_{ij}=\frac{exp(LeakyReLU(MLP^{l}(h^{(l)}_{i}||h^{(l)}_{j}||w^{l}_{ ij})))}{\sum_{k\in V_{l}}exp(LeakyReLU(MLP^{l}(h^{(l)}_{i}||h^{(l)}_{k}||w^{l}_{ ij})))} \tag{9}\]  
Therefore, the node feature of node \(i\) in the new \(l+1\) layer can be represented as:  
\[h^{(l+1)}_{i}=\sigma(\sum_{j\in V_{i}}\alpha^{l}_{ij}h^{l}_{j}) \tag{10}\]  
where \(\sigma\) is the activation function LeakyReLU. After the node features are aggregated in the Weighted Graph Attention Network (WGAT), the features will be sent into the temporal convolution to propagate in the temporal dimension. So far, the features have been propagated in the spatiotemporal weighted attention network.
## 3 Topological Modeling and Power Flow Analysis Based on Petri Net
### Power Flow Analysis and Calculation Method Based on Petri Net  
#### 3.2.1 Power Calculation Method for Switchboards  
In the distribution network of the Petri net, the token value in each library is determined by the load power. For the library corresponding to the load element, the load power is the token value. For the library corresponding to the switchboard and the power source, the initial token value is 0. Thus, the initial token vector of the distribution network can be determined represented by \(K^{0}_{\text{pNet1}}\). The values of each non-diagonal element in \(I_{\text{Net1}}\) and \(O_{\text{Net1}}\) for the distribution network are all 0. Set each diagonal element in \(I_{\text{Net1}}\) as 1 and determine the values of each diagonal element in \(O_{\text{Net1}}\) according to the initial switch state in the system. For a switch whose initial state is open, the element corresponding to the output arc in \(O_{\text{Net1}}\) is 0; for a switch whose initial state is closed, the corresponding output arc is 1.  
The dynamic mechanism of the Petri net is used to deduce the load of each switchboard, and its iterative formula is:  
\[K^{i+1}_{\text{pNet1}}=K^{0}_{\text{pNet1}}+K^{i}_{\text{pNet1}}\times I_{ \text{Net1}}\times O_{\text{Net1}} \tag{7}\]  
The above formula is iterated successively from \(i=0\), and it stops when \(K^{i+1}_{\text{pNet1}}=K^{i}_{\text{pNet1}}\). The power system in the floating nuclear power plant contains two levels of switchboards. After two rounds of iteration, the token number in each switchboard is the final power amount.  
Figure 3: Petri net model for power analysis. (**a**) Simple power network. (**b**) Equivalent Petri net model.
## 3 Routing for Data Centric Storage
### PathDCS  
PathDCS [21] also relies on landmarks, here called beacons, to route messages and queries avoiding the necessity of point-to-point routing (such as geographic routing) that requires location knowledge.  
The authors indicate that point-to-point routing presents problems in WSNs, so that is better to define tree-structures in order to route messages. In particular, pathDCS creates one tree per beacon node, rooted at the beacon node. They use existing algorithms for the tree creation [24; 25] and use [26] to elect the beacon nodes.  
Each node is assigned with a logical identifier. The authors remark that in DCS, based on a key (event type), \(k\), the home node should be consistent for any production event or consumption query. Then, the novelty of this proposal is that an event type is not mapped to a spatial location, but to a path. Roughly speaking, based on the event type there will be a set of indications: \"you have to go to beacon 1, later you have to travel two hops towards beacon 2 and finally 1 hop towards beacon 3\". Therefore a path consists of a sequence of \(p\) beacons \(b_{i}\), and lengths \(l_{i}\), with i=1,2,...,\(p\). Then, an event (production) or query (consumption) is sent to beacon \(b_{1}\) (using the tree created by \(b_{1}\)), next it is forwarded \(l_{2}\) hops towards beacon \(b_{2}\), and so on, until it is sent from the last node \(l_{i}\) hops towards \(b_{i}\), where \(l_{1}\) is the distance in hops between the source node and the beacon \(b_{1}\).  
A beacon node \(b_{i}\) is the node whose identifier is the closest one to _hash(k,i)_, whereas the length of the _i-th_ segment is obtained as: \(l_{i}=h(k,i)\ mod\ hops(n_{i},b_{i})\), where \(n_{i}\) is the node that starts the _i-th_ segment and _hops(n,b)_ the distance in number of hops between the node \(n\) and the beacon \(b\). Therefore, in order to apply this proposal two parameters are required, the number of beacon nodes within the network and the number of segments used in a path, \(p\).  
Moreover, they also propose to use local replication in the k-hops home node's neighbours to reduce the load of the home node, which is a local replication mechanism.  
PathDCS is a very interesting approach that has been implemented and tested in real sensor nodes. As HVGR, it relies on an infrastructure that provides references to all the nodes within the sensornet to perform the routing without knowing its own spatial location, but this seems simpler than the HVGR one. The main disadvantage is that pathDCS needs to create one routing tree per beacon node, which implies additional energy consumption. The energy for creating the beacon infrastructure depends on the size of the WSN and the number of beacon nodes used.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 7 OCTA Measurements of Hemodynamics
### Hematocrit Quantification  
Since OCTA signal depends on the RBC density, can OCTA be used to quantify hematocrit? The differences in rheology, geometry, and light scattering in capillaries versus macrovessels suggest different approaches for each. In macrovessels, backscattering or attenuation (signal slope) are possible observables which may help to determine hematocrit. However, due to the high scattering coefficient and anisotropy of RBCs, multiple scattering events are very likely, except at superficial path lengths (Figure 2C,D). In particular, at physiological hematocrits, dependent scattering and shadowing effects lead to a highly nonlinear relationship between the RBC concentration and scattering coefficient [80; 81]. This nonlinear relationship hampers efforts at quantifying hematocrit based on light scattering and the signal slope alone. Additionally, the oxygen saturation dependence of hemoglobin refractive index and RBC scattering further complicate efforts to measure hematocrit based on attenuation [26]. The orientation-dependence of light scattering from RBCs (Figure 2C) makes quantifying hematocrit from backscattering alone challenging. Thus, quantification is challenging in macrovessels.  
The single file flow and relatively lower hematocrit in capillaries makes multiple scattering within these vessels less problematic. However, RBCs may re-orient themselves and possibly deform as they squeeze through the smallest diameter capillaries, thereby changing their backscattering cross-sections. Moreover, measuring backscattering directly would need absolute calibration, which can be difficult in vivo. However, backscattering may still measure relative changes in the red blood cell content in capillaries [82] and, possibly, at the surfaces of macrovessels over time. Thus, while quantification of hematocrit changes is possible in capillaries, absolute measurements of hematocrit with conventional OCTA are currently challenging.
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 5 Results and Discussions
### Surface Roughness  
Figure 9 demonstrates the surface roughness of five kinds of materials in the D-band range. The symbols denote the estimated surface roughness by the MMSE criterion and the average of them are 78.0 \(\upmu\)m, 11.2 \(\upmu\)m, 97.9 \(\upmu\)m, 266.7 \(\upmu\)m, and 322.8 \(\upmu\)m respectively, corresponding to wood, plexiglass, drywall (plaster), concrete board and red brick. It is evident from the insets (i)-(v) of Figure 9 that the surface roughness increases with frequency but the change is small. The reason is that the signal at higher frequency suffers a larger penetration loss. Therefore, the signal distribution will be concentrated on the surface and more scattering will occur, which increases the roughness of surface [31]. The black bold line is set as the threshold (291 \(\upmu\)m) to distinguish whether the surface of an object is rough or smooth. It is determined by (5) and [45]. Therefore, the surface of plexiglass is regarded as quite smooth. The wood and drywall (plaster) are considered relatively smooth although they are rougher than the plexiglass. Note that the surface roughness of the concrete board is so close to the threshold that it is classified as rough material like red brick. As can be seen from Table 7, the smooth plexiglass has the lowest MMSE whereas the rough materials concrete and red brick have higher values. Chances are that the MMSE can imply the surface roughness of building materials in our method. Despite the high surface roughness of the concrete board, which means more power loss in the specular direction, its reflection loss is lower than that of the other four kinds of materials shown in Table 6. In fact, based on the analysis of (7) before, it is not surprising that the concrete board has the lowest reflection loss of the five kinds of materials because it has the largest relative permittivity. This suggests that the reflection characteristics of indoor building materials are determined not only by surface roughness, but also by many other factors, such as relative permittivity, frequency, and incident angle.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 4 Discussion  
In this paper, we propose a two-dimensional seismic data denoising method combining SVD and deep learning. This method attenuates strong interference noise, which cannot be completely removed with traditional SVD filters, and resolves discontinuous weak seismic signals when the effective signal and noise are distinguished only according to the singular value. The improved SVD filtering technology can improve the low efficiency of artificial rank selection and the accuracy and effectiveness of noise suppression.  
When SVD is applied to 2D seismic data, only the effective signal and noise are distinguished based on the RSV. The effective signal and noise patterns corresponding to the RSV are rather straightforward compared with field seismic data, reducing the challenge of obtaining sample data. The morphological complexity and number of RSV categories after SVD are significantly reduced compared with the complex and varied seismic data. The morphological characteristics of the RSV corresponding to different seismic data do not vary significantly. Improved SVD filtering effectively overcomes the weak generalization ability of the deep learning training model. Compared with traditional SVD filtering, the improved SVD filtering successfully eliminates Gaussian noise and strong amplitude noise in the synthetic seismic data. It maintains the integrity of seismic events in the field of seismic data. However, the disadvantage is that the seismic data targeted are single, mainly for horizontal lineups. Our future work will denoise seismic data containing cross and inclined lineups.  
Conceptualization, [PERSON] and C.W.; methodology, [PERSON]; software, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
Figure 10: (**a**) Original field seismic data; filtered results achieved using conventional SVD filtering with (**b**) K = 1 and (**c**) K = 2; (**d**) improved SVD filtering.  
**Funding:** This research was funded by the National Natural Science Foundation of China, (41874166), and the Guizhou Science and Technology Cooperation Platform Talents Program ([2021]5629).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data of this study is available from the authors upon request.  
**Acknowledgments:** We thank the support provided by the Guizhou Science and Technology Cooperation Platform Talents Program of China ([2021]5629) and the National Natural Science Foundation of China (41874166). At the same time, we also thank the reviewers for their valuable time. The results of this study and codes are available from the authors upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Feature Selection and Decision  
### Review of Feature Extraction  
Extracting valuable features after preprocessing is necessary for accurate BP and CI estimations using PPG with ECG signals [13; 23; 24; 28]. Therefore, the time and frequency domains of the ECG and PPG signals were analyzed. However, this study considered that the frequency information was concentrated in the low-frequency band below 1.5 Hz; thus, valuable features could not be extracted in the frequency domain. Therefore, the features were extracted using the pulse morphology of the PPG signal and time between the ECG and PPG signals on the time axis, as shown in Figure 2. First, the PTT was obtained, which is the time interval between the arrival of blood flowing distally and the opening of the aortic valve [14; 28; 30].  
The pulse arrival time (PAT) denotes the time interval between the R peak of the ECG signal and PPG rise points, and both PAT and PTT are essential features for estimating BP values [14; 24; 25]. Another essential feature is the PPG's pulse intensity ratio, represented to be inversely proportional to the diastolic trough [14]. The waveform associated with the heart rate cycle can be observed through the PPG signal. Thus, the PPG signal waves were defined as pulses, each corresponding to a cardiac cycle, with the rising edge as the systolic time (ST) and falling edge as the diastolic time (DT) [14]. In addition, the area in the pulse corresponding to the ST was used as the systolic area (Sa), and the area in the pulse corresponding to the DT was used as the diastolic area (Da) [24]. As shown in Figure 2, each pulse waveform was divided into these two areas. Therefore, the features of each pulse were extracted, including Sa, Da, ST, DT, and cycle duration, to extract features that effectively estimate BPs and CIs. Figure 3 shows the 1 st and 2 nd derivatives of the PPGs. These results  
Figure 2: Feature extraction from the PPG with ECG signal, where (**a**) is an ECG signal example, (**b**) denotes a PPG signal example, and (**c**) is a target signal (ABP) example.
## 3 Proposed Method--Depression Ensemble Stacking Generalization Model (DESGM)
### Linear Support Vector Machine (LSVM)  
The LSVM algorithmic method addresses samples' complexity by seeking separators with a \"large margin\". The justification for employing decision boundaries with significant margins is rooted in their tendency to exhibit a lower generalization error, unlike models with narrow margins. The following procedures will be used to train and classify the SVM:  
1. Training LSVM: \[\bullet\] Compute the gram matrix. \[\mathrm{G}_{\mathrm{ij}}=\mathrm{y}^{(\mathrm{i})}\mathrm{y}^{(\mathrm{j})} \mathrm{x}^{(\mathrm{i})}\mathrm{x}^{(\mathrm{j})}\] (7)
2. Let \(\alpha^{*}\) value: \[\max_{\alpha}\left[\sum\limits_{i=0}^{\mathrm{N}}\alpha_{i}-\frac{1}{2}\sum \limits_{i=1}^{\mathrm{N}}\sum\limits_{j=1}^{\mathrm{N}}\alpha_{i}\alpha_{j} \mathrm{y}^{(\mathrm{i})}\mathrm{y}^{(\mathrm{i})}\mathrm{x}^{(\mathrm{i})} \mathrm{x}^{(\mathrm{j})}\right]\mathrm{Subject\ to}\left\{\begin{array}{l}0 \leq\alpha_{i}\leq\mathrm{C},\forall_{i}\\ \sum\limits_{i=1}^{\mathrm{N}}\alpha_{i}\mathrm{y}^{(\mathrm{i})}=0\end{array}\right.\] (8) The value of parameter C controls the margin's width.
3. Determine the weight. \[\mathrm{w}^{*}=\sum\limits_{i=1}^{\mathrm{N}}\alpha^{*}{}_{i}\mathrm{y}^{( \mathrm{i})}\mathrm{x}^{(\mathrm{i})}\] (9)
4. Calculate the intercept. \[\mathrm{w}_{0}{}^{*}=1-\min_{\mathrm{i}\mathrm{y}^{(\mathrm{i})}=1}\mathrm{w }^{*\mathrm{T}}\mathrm{x}^{(\mathrm{i})}\] (10)
5. Classification LSVM (For a new sample x) \[\bullet\] Determine the support vector's \(\mathrm{x}^{(\mathrm{i})}\). \[\mathrm{k}_{\mathrm{i}}=\mathrm{x}\cdot\mathrm{x}^{(\mathrm{i})}\] (11)
6. Calculate f(x). \[\mathrm{f}(\mathrm{x})=\mathrm{w}_{0}{}^{*}+\sum\limits_{i}\alpha^{*}{}_{i} \mathrm{y}^{(\mathrm{i})}\mathrm{k}_{\mathrm{i}}(:\mathrm{w}_{0}{}^{*}+\mathrm{ w}^{*\mathrm{T}}\mathrm{x})\] (12)
7. Examine sign (f(x)).