esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Symbolic Methods  
Symbolic methods directly perform reasoning based on the explicit structure (entities, relationships, and paths) of the knowledge graph, emphasizing logical rules and transparent operations. Typical methods include path traversal, template matching, and logical query generation. [PERSON] et al. designed a method to extract task-specific subgraphs from a knowledge graph and supplement them with textual documents [13,14]. They then applied graph convolutional networks to extract answers from the enriched subgraphs [13,14]. [PERSON] et al. proposed the IRN model, which generates explicit reasoning paths by iteratively predicting intermediate entities and relationships [15]. The paths are traceable and conform to the explicit logical characteristics of symbolic reasoning [15]. [PERSON] et al. proposed a method for automatically generating templates to convert natural language questions into structured queries for knowledge graphs [16]. The core idea is to automatically construct utterance-query templates by learning the alignment relationship between questions and queries [16]. [PERSON] et al. proposed a method for converting natural language queries to SPARQL based on a language analysis [17]. A language parser is used to extract noun phrases and relations from Arabic sentences, and combined with ontology information, they are mapped to the entities and predicates of the knowledge graph. The knowledge in the ontology is utilized to group noun phrases into triple representations, and SPARQL queries are generated according to the query objectives and modifiers [17]. [PERSON] et al. proposed a semantic parsing method based on the neural attention mechanism for converting natural language questions into SPARQL queries [18]. The core technology adopts a sequence-to-sequence (seq2 seq) architecture, which is combined with the attention mechanism to align the structures of natural language and SPARQL [18]. [PERSON] et al. proposed a skeleton grammar to decompose the semantics of complex questions into an abstract structure containing entities, relationships, and constraints (such as multi-hop relationships and aggregation operations) [19]. The skeleton represents the core logic of the question through a coarse-grained formalization, thereby constructing a high-level abstract structure of the question to achieve the generation of structured queries for complex questions [19]. Symbolic methods are completely transparent and highly interpretable,and the skeleton strictly follows the topology of the knowledge graph, avoiding the path divergence problem of the embedding method. However, it has two limitations: First, it is difficult for predefined rules to cover complex language expressions. Second, it highly depends on the generated equivalent query statements. The cost of manually designing templates or rules is high, and it is difficult to adapt to dynamic knowledge updates.
## 1 Introduction
Notably, no approximation was taken in this paper to derive a differentiable expression. Besides, it is not worthwhile to derive a differentiable expression of intersection volume since calculating the exact volume is not our intention. Moreover, the partial derivatives can be easily derived even without the expression of intersection volume. Inspired by [18], we developed an algorithm to detect vertices of self-intersection regions quickly by only going through triangles in the mesh once. This process is similar to rasterization in computer graphic with linear time complexity. A linked list is applied to store depth values and orientations of triangles intersected with the same detection ray. Then vertices in self-intersection region can be easily detected by going through the linked list. The partial derivatives  
Figure 1: Examples of model-based 3D pose estimation results with self-intersection between body parts. of the self-intersection term with respect to the coordinate of each vertex are easy to derive from the perspective of geometry. The partial derivatives with respect to vertices not in the region of self-intersection is obviously zero, while the partial derivatives with respect to vertices in the region of self-intersection could be obtained from the normal vectors of neighbouring triangles. The value of the penalty term is assigned as the ratio of number of vertices in self-intersection to number of vertices not in self-intersection. In this way the value of penalty term is easy to compute and indicates the degree of self-intersection in some degree. The experimental results show that the proposed penalty term avoids self-intersection strictly and works effectively. The main contributions of this paper are summarized as follows:  
* We proposed a novel self-intersection penalty term which does not require deriving a differentiable expression and generally applies to triangular mesh-based representations. * We performed 3D pose estimation from 2D joints and compared our method with other state-of-the-art approaches qualitatively and quantitatively to demonstrate the practical value and the superiority of our method. * To the best of our knowledge, this is the first time the conception of self-intersection in relation to disconnected meshes in the field of 3D reconstruction has been generalized. The content of this paper consists of five sections. In Section 2 an overview of related work is provided. In Section 3, the details of the proposed self-intersection penalty term are presented. In Section 4, the results of experiments and analysis of the proposed self-intersection penalty term are given. The conclusions are presented in Section 5.
# Energy-Efficient Clustering Multi-Hop Routing Protocol in a UWSN
## 3 Energy-Efficient Clustering Multi-Hop Routing Protocol
### Energy-Efficient Clustering Multi-Hop Routing Protocol
From lines 34 to 35, if node \(S_{k}\) is located near the surface, then node \(Si\) or \(D_{m}(S_{k})<D_{m}(S_{i})\), and node \(S_{i}\) adds \(S_{k}\) as a relay node. The cluster head schedules the transmission of all cluster members as a time division multiple access (TDMA). The data will be transmitted in the steady phase. The cluster head is selected at the beginning of each superframe in order to balance the load and energy for each node. ```
Input: node ID, position, initial energy, generated packets Output: cluster {cluster ID, CH, CM}, CRs
1. State 1: Network Initialization
2. For each node \(S_{i}\)
3. Calculate weight \(W_{i}\) as in (8)
4. Broadcast \(HELLO=\) {node ID, position, \(D_{m}\), \(W_{i}\), residual energy, history of cluster head}
5. End For
6. While\(t<timeout\)do
7. Receive \(HELLO\) from other nodes
8. Create list of neighbors at each node \(Neit(S_{i})=\{Neit(S_{i})\cup S_{j}\ \ |\ d(i,j)<R\}\)
9. End While
10. State 2: Nodes elect to become cluster head
11. For each node \(S_{i}\)
12. [\(Sk\), \(Max(W_{k})\)] = max\(\{(W_{i},W_{j})\), \(S_{j}\in Neit(S_{i})\}\)
13. If\(S_{i}\) has the \(Max(W_{k})\)
14. Broadcast \(|JON=\) {Cluster ID, maximum number of cluster member, \(D_{m}\)}
15. Elect to become cluster head
16. Else
17. Wait for \(JON\) message from the cluster head
18. End If
19. End For
20. State 3. Relay node selection
21. For All nodes are not assigned as CH
22. If\(S_{j}\) receives \(JON\) messages from two nodes at \(D_{m}\) and \(D_{m-1}\), respectively
23. Broadcast \(RELAY=\) {node ID, \(D_{m}\), Cluster ID(\(D_{m}\)), Cluster ID(\(D_{m-1}\))}
24. Become a relay node which can coordinate the routing path
25. Else
26. Send the \(RESPONSE\) message to the CH
27. \(RESPONSE=\) {node ID, cluster ID, \(D_{m}\)}
28. End If
29. End For
30. State 4. Multi-hop routing path creation
31. For all nodes that receive RELAY message
32. Find \(D_{m}\) and node ID \(S_{k}\) of RELAY message
33. If node \(S_{i}\) is the CH and \(D_{m}(S_{k})<D_{m}(S_{i})\)
34. Add \(S_{k}\) as the relay node of \(S_{i}\)
35. End If
36. End For
```  
**Algorithm 1**Energy-Efficient Clustering Multi-Hop Routing Protocol.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 3 The Phased Array 5G Smartphone Antenna
In order to demonstrate that the ground plane (GND) of the smartphone PCB has an insignificant effect on the radiation behavior of the phased array Quasi-Yagi antenna, the radiation performances of the 5G smartphone antenna with different lengths of the PCB ground plane have been investigated in Figure 6:0 As illustrated, the size of the PCB ground plane does not have a significant impact on the radiation properties of the array, such as the antenna gain and its efficiency. Less than a 0.3 dB variation has been obtained in the realized gain level of the smartphone antenna array in the main radiation beam (0\({}^{\circ}\)) for different lengths of the PCB ground plane. Figure 6:1 Radiation beams of the 5G smartphone antenna at 0\({}^{\circ}\), (**a**) without GND, (**b**) half, and (**c**) full GND. Figure 6:2 S parameter results of the mm-Wave 5G smartphone antenna. Figure 6:3 The current distribution at 26 GHz. 3D radiation beams of the 5G smartphone antenna are displayed in Figure 6:4 The direction and scanning angle of the phased array radiation beams can be assessed by applying the relative amplitudes and phases to the radiators. It can be seen that the presented 5G smartphone antenna has well-defined directional beams with wide-scanning property and high directivity values. In addition, the values of the antenna realized gains at different angles, which are specified in Figure 16a. As shown, the 5G smartphone antenna design exhibits sufficient realized gain levels (more than 10 dB) in the scanning range of 0\({}^{\circ}\) to 75\({}^{\circ}\), which makes it suitable for future cellular applications. The 5G smartphone antenna also provides good efficiencies at different scanning beams. As can be observed from Figure 16b, more than \(-\)0.5 dB (90%) efficiencies have been obtained in the scanning range of 0-60 degrees. Furthermore, a high directivity characteristic (11-12.5 dBi) has been achieved for the presented design. Table 2 provides a comparative summary of the fundamental characteristics for the proposed phased array with the recently reported 5G antenna array available in the literature. As can be observed  
Figure 6:5 (**a**) Realized gain (Cartesian), (**b**) efficiency and directivity properties at different angles. Figure 6:6 3D radiation beams of the 5G smartphone design at (**a**) 0\({}^{\circ}\), (**b**) 15\({}^{\circ}\), (**c**) 30\({}^{\circ}\), (**d**) 45\({}^{\circ}\), (**e**) 60\({}^{\circ}\), and (**f**) 75\({}^{\circ}\). from the table, the proposed phased array Quasi-Yagi antenna can support wide scanning angles with high gain and efficiencies. Different from the reported designs, the gain and efficiency characteristics of the design are almost constant over the scanning range of 0-75 degrees. In addition, the antenna elements have more than 18 dB isolation. Furthermore, the Quasi-Yagi antenna elements are also suitable for use in planar phased array designs for base station applications.
## 3 Computed Tomography and Mitral Valve
### CT Assessment of the Mitral Apparatus for Transcatheter Procedures
#### 3.3.1 Annulus Sizing  
Measurement of the mitral annulus is of primary importance since inaccurate sizing has devastating consequences. Moreover, given the complex mitral annulus 3D shape, collecting annular sizing could be challenging [80]. A direct calculation method involves tracing the edges of the annulus on conventional two-, three-, four-chamber, and short-axis views along the posterior mitral leaflet insertion and fibrous continuity. However, this method is time-consuming and can lead to an oversized device, increasing the risk of left ventricular outflow tract obstruction (LVOT). [PERSON] et al. developed a more straightforward method that assumes assimilating the mitral annulus to a planar D shape; given these premises, the two fibrous trigones are connected along a virtually straight line, effectively excluding the anterior horn [81]. Since the dimensions of the mitral annulus change during the cardiac cycle, the calculations are performed in the cardiac cycle phase in which the annulus is larger. Area and perimeter are the most used measures. However, other parameters are developed, such as the trigone-trigone distance; the intercommissural distance, which corresponds to the maximum diameter of the annulus parallel to the trigone-trigone distance; the septum-lateral distance, which corresponds to the maximum diameter of the annulus perpendicular to the IC. It must be emphasized that each device refers to different parameters for sizing [79].
## 3 Methodology
### Bias Correction Methods
#### 3.1.4 Variance Scaling (VARI) of Temperature  
The PT method is suitable for the correction of the mean and variance of precipitation, but not appropriate for the bias correction of temperature, because the temperature is known to have an approximately normal distribution [30]. The VARI method was developed to correct both the mean and variance of normally distributed variables such as temperature [4; 30]. Hence, temperature data are corrected using the VARI method using (9):  
\[T_{cor,m,d}=[T_{raw,m,d}-\mu(T_{raw,m})]\times\frac{\sigma(T_{obs,m})}{\sigma (raw,m)}+\mu(T_{obs,m}) \tag{9}\]
## 6 Discussion
### Significance of the Error Evaluation Indexes  
Based on the results of six error evaluation indexes, namely MAPE, MAE, RMSE, WI, E\({}_{\text{NS}}\), and E\({}_{\text{LM}}\), the significance of the proposed hybrid model is estimated. Taking the forecasting results of dataset 2 as an example, the MAPE values of the proposed model, BPNN, RBFNN, and ARIMA are 3.07%, 5.42%, 6.41%, and 5.61%, respectively. The WI values are 0.9929, 0.9731, 0.9595, and 0.9752, respectively. Combining the results of these together, the hybrid model is proven to be superior to the traditional methods. In conclusion, the forecasting ability of the proposed model can be better analyzed with the help of these evaluation indexes, and the comparison between the proposed model and the other forecasting methods is more comprehensive.
# A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
## 3 Algorithms
### Clustering  
After the trajectory similarity matrix is extracted according to the method described above, the distance matrix is clustered based on the K-means algorithm. The brief process is shown in Figure 7.  
It is worth noting that this paper uses the k-means algorithm under the condition of labels, which can select the initial cluster center in the labeled data to get a better initial center distribution so as to obtain faster clustering speed and better clustering performance.  
This paper only uses the basic clustering algorithm to compare the effectiveness of the obtained similarity matrix. In practical applications, the clustering method can be changed according to the application conditions.
# A Semi-Supervised Deep Learning Framework for Change Detection in Open-Pit Mines Using SAR Imagery
## 1 Introduction
Finally, [PERSON] [33] introduced TransUNet++SAR, an end-to-end SAR image change detection network that combines transformer and UNet++ architectures. The proposed method can effectively model global semantic relations, preserve spatial resolution, and achieve accurate localization. However, despite the extensive research conducted on SAR-based amplitude change detection methods, these techniques have not been specifically applied to study the time evolution of open-pit mines. The unique characteristics of open-pit mines, such as their complex and heterogeneous land use types, dynamic mining activities, and pseudo changes, justify the need for a trained model for this specific task [10]. To the best of our knowledge, this paper presents the first deep learning model in the literature that can accurately detect changes within open-pit mines using high-resolution SAR images, i.e., TerraSAR-X. Our model was trained on a manually labeled dataset of three open-pit mines. We showcase the effectiveness of the proposed method, highlighting the fundamental role of data augmentations and the coherence layer as critical components for enhancing the model's performance. In addition, we also demonstrate how, in the presence of a few labels, a pseudo-labeling pipeline taken from the literature can improve model robustness, without degrading performance by introducing misclassification points related to pseudo changes. The final results show the reliability and efficacy of our deep learning approach for SAR change detection in the open-pit mining sector. The rest of this paper is organized as follows: Section 2 describes the dataset that has been used for the training, validation, and testing of our approach. Section 3 describes our proposed deep learning methodology for change detection in the mining sector using SAR images. In Section 4, we present our experimental results and evaluate the performance of our approach. Finally, Sections 5 and 6 summarize our contributions and discusses potential future work.
## 2 Materials and Methods
### Ground Data Collection  
#### 2.2.1 _f_PAR Data  
_f_PAR was measured by the SunScan plant canopy analyzer (Produced by Delta-T in the UK), which consists of a 1 m probe and a handheld PDA. The probe contains a total of 64 quantum sensors and is connected to the PDA using an RS-232 cable. When measuring the _f_PAR of each plot, the incident light and effective radiation were measured separately for 3 canopy incident (PAR\({}_{\rm in-can}\)), 3 canopy reflectance (PAR\({}_{\rm re-can}\)), 3 soil incident (PAR\({}_{\rm in-soil}\)), and 3 soil reflectance measurements (PAR\({}_{\rm re-soil}\)) of photosynthetically active radiation (PAR). The _f_PAR of the plot was obtained by averaging these three measurements. The spatial relationship between the probe and the winter wheat plots is shown in Figure 2. Due to the time-consuming nature of _f_PAR observations, to ensure that all measurements were completed between 10 a.m. and 2 p.m., only one replicate  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Irrigation Scheme** & **Irrigation Data** & **Phenological Phase** & \multicolumn{2}{c}{**Total Irrigation**} \\  & & & **Volume (m\({}^{3}\) ha\({}^{-1}\))** \\ \hline IS-A & 2 April 2023 & Jointing & 1500 \\ IS-B & None & None & 0 \\ IS-C & 28 November 2022 & Overwintering & 750 \\ IS-D & 7 March 2023 & Regreen & 750 \\ IS-E & 2 April 2023 & Jointing & 750 \\ IS-F & 9 April 2023 & Jointing & 750 \\ IS-G & 16 April 2023 & Booting & 750 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Irrigation time and amount of irrigation for the irrigation schemes in the experiment.  
Figure 1: RGB image of the research area captured by UAV on 5 August 2023 and the distribution of varieties in all plots of each IS.  
experiment for each water treatment was selected for ground data collection, resulting in a total of 77 ground plot measurements per session. The horizontal angle between adjacent measurements was set to 45\({}^{\circ}\). The _f_PAR of each plot is defined as:  
\[f\mathrm{PAR}=\frac{\mathrm{PAR_{in-can}-PAR_{re-can}-(PAR_{in-soil}-PAR_{re-soil} )}}{\mathrm{PAR_{in-can}}} \tag{1}\]
## 3 Numerical Method
### Turbulence Model Validation  
In this section, the turbulence model is verified based on Geometry 2 and five turbulence models such as SST \(k\)-\(\omega\) and \(k\)-\(\varepsilon\) commonly utilized in the numerical calculation are selected. The skin-friction coefficient calculated by the numerical calculation is compared with the experimental results from Ref [3]. Figure 4 shows the comparison between the model prediction results and the experimental results. The three operating conditions with Reynolds numbers of \(2.09\times 10^{7}\), \(2.91\times 10^{7}\) and \(3.93\times 10^{7}\) in the verification are located in Region IV. The model prediction results are in good agreement with the experimental results and the relative deviation is less than 5%.  
The results of the turbulence model validation are shown in Figure 5. It can be seen that the \(k\)-\(\varepsilon\) model is closest to the experimental results when the Reynolds number is high. As most of the working conditions in this paper are concentrated under high Reynolds numbers, the \(k\)-\(\varepsilon\) turbulence model is selected for the calculation. The \(k\)-\(\varepsilon\) model is the most widely used turbulence model in engineering applications. When the wall function is utilized, it has high stability, economy and calculation accuracy and is suitable for high Reynolds number calculation.  
Figure 4: Comparison between Region IV model and experimental results [3].  
Figure 3: Verification points and their distribution.
## 2 Materials and Methods
### Multimodal Approach and Late Fusion  
As previously mentioned, the aim is to develop a second verbal-based prediction model to improve the performance of the initial visual prediction model. In order to test whether the subsequent model improves the first one, cross-testing and validation evaluations must be implemented. Because an existing model was already trained, tested, and validated in previous work, the development of a second model can be performed through **late fusion testing** (decision-level fusion). Late fusion training consists of two or more separate classifiers that were trained and tested in a similar way [41]. Each classifier outputs a prediction set file after its final testing phase, and this file is saved for future use, and these predictions are later combined using a fusion rule. Through this method, the probabilities and the weights are averaged from each classifier, based on the performance of the validation sets. Majority voting is then used, based on the class labels predicted by each classifier. Finally, a **meta-classifier** is developed, trained, and tested in the fused dataset, following the same training and testing methods. The final results depict the predictions of the model for the combined prediction data from each of the classifiers.  
The **early fusion (concatenation)** technique is also feasible in this case, with simplicity of implementation as well as the ability to allow classifiers to learn relationships between features directly, making it a strong candidate. Instead of utilizing the predictions of the two classifiers, early fusion extracts both visual and audio features from both models and concatenates them into a single training dataset, which is then used to train and test a single model [42]. However, the flexibility of late fusion provides a more robust way to differentiate classifiers for each modality and handles various importance and feature scales of modalities in a superior way. Late fusion's application is less prone to overfitting compared to that of its counterpart, early fusion, which is crucial for this proof of concept [43]. Also, late fusion is ideal when data modalities are different from each other. In this case, audio data and visual data were collected from two different datasets, with no interrelationship with each other. For these reasons, late fusion will be the focus of this model.