esa-sceva/satcom-chunk-collection huggingface dataset:
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
###### Abstract  
Multivariate time series forecasting has long been a subject of great concern. For example, there are many valuable applications in forecasting electricity consumption, solar power generation, traffic congestion, finance, and so on. Accurately forecasting periodic data such as electricity can greatly improve the reliability of forecasting tasks in engineering applications. Time series forecasting problems are often modeled using deep learning methods. However, the deep information of sequences and dependencies among multiple variables are not fully utilized in existing methods. Therefore, a multivariate time series deep spatiotemporal forecasting model with a graph neural network (MDST-GNN) is proposed to solve the existing shortcomings and improve the accuracy of periodic data prediction in this paper. This model integrates a graph neural network and deep spatiotemporal information. It comprises four modules: graph learning, temporal convolution, graph convolution, and down-sampling convolution. The graph learning module extracts dependencies between variables. The temporal convolution module abstracts the time information of each variable sequence. The graph convolution is used for the fusion of the graph structure and the information of the temporal convolution module. An attention mechanism is presented to filter information in the graph convolution module. The down-sampling convolution module extracts deep spatiotemporal information with different sparsities. To verify the effectiveness of the model, experiments are carried out on four datasets. Experimental results show that the proposed model outperforms the current state-of-the-art baseline methods. The effectiveness of the module for solving the problem of dependencies and deep information is verified by ablation experiments.  
Keywords:multivariate time series; deep spatiotemporal information; down-sampling convolution; attention; graph neural network +
Footnote †: journal: Applied sciences
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Practical Challenges
#### 5.1.3 Practical IRS Deployment  
The deployment of the IRSs in a wireless network is another challenging issue. From the optimization viewpoint, when considering a single BS serving a single user, the IRS should be deployed to guarantee clear LOS from the BS with the aim of maximizing the received signal power. However, such a straightforward deployment strategy may not be suitable in multi-user scenarios since one single LOS path between the IRS and the common BS leads to a low-rank channel which will limit the spatial multiplexing for the transmission to multiple users through the IRS [4; 57]. In this case, it is preferable to guarantee a high channel rank and a strong LOS within the BS-IRS link. The IRS deployment also needs to consider the spatial users' density, the inter-cell interference in cellular networks, etc. Therefore, autonomous deployments for IRS-assisted systems is still an open challenge that can be faced with DL and reinforcement learning (RL) techniques by training a NN with some reference allocations and collecting key performance indicators [2].
## Appendix A Nmi  
### Box Plot  
Figure 11 summarizes the model's performance in the box plot using the NMI metric (the range value is between 1, which indicates perfectly uncorrelated images, and 2, which indicates perfectly correlated images either in a positive or negative direction). Again, like the GDC metric, there are no clear winners; the models perform differently based on the datasets. However, the ensemble models perform better than the MC dropout and regression models except for the SJJ dataset, in which the MC dropout versions perform the same as the MA model and are better than the SA model. Yet, none of the models achieved perfect correlation; the Heart dataset scored around 1.7 median correlation as the best result. Also, there is a significant discrepancy between the GDC and NMI metrics on some datasets. For instance, on the Heart dataset, the SA model achieved a very low GDC score, with a median of only 92%. However, the same model achieves a much higher NMI score, with a median around 1.7. Interestingly, even within the MC dropout versions themselves, there is no significant difference in performance. There is a fluctuation among all the datasets. For instance, the Heart dataset achieved the best results with a 60% dropout rate, while the Lung dataset performed best with a 20% dropout rate. This trend continued with slight variations across other versions as well. Moreover, there is no clear superiority between the MA or SA models in segmentation performance. Similarly, the results highly depend on the datasets; for example, in the Knee dataset, The SA model achieved a higher median than the MA model but with a much wider IQR distribution. Conversely, the MA model performed better for the SJJ dataset. For the remaining datasets, the median and IQR distributions are almost the same. Finally, the regression model scored very low NMI for the Knee and Lung datasets but scored competitive results for other datasets. Nonetheless, when the regression and ensemble models were compared, the ensemble models consistently scored higher NMI median. Similar to the GDC metric, all MC models with a dropout rate equal to or less than 60% always score better NMI than the regression model.
## 2 Materials and Methods
### A.1.--Data Pre-Processing  
The strategic objective of this experiment is to represent each vessel through the statistical profile of its route. In the dataset provided by the Navy, there are only two variables that characterize the route of a vessel: the velocity and direction of the bow at the time of radar detection. This is regardless of the stretch of sea crossed.  
For example, by dividing the variable \"velocity\" into regular intervals (bins), you can measure how often the velocity of each vessel falls within each of them, during its journey. With an appropriate transformation, one can define the general probability with which a vessel can be found in each of these intervals. By establishing a defined number of intervals (bins) for each variable that characterizes the vessel's navigation, the statistical profile of each vessel's route can be established through the use of the probability density function.  
Utilizing these intervals, we decided to characterize the only two variables available in the database for each vessel: Punctual velocity and direction at every radar detection point along the route. However, it is important to note that these measurements are taken in an unsystematic manner, rather than following a strict sampling plan.  
A new variable is introduced to the statistical profile of the route of each vessel: delta velocity. Table 2 illustrates the variables that define the statistical profile of the route of each vessel, according to the pre-processing strategy adopted in experimentation no. 3.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Data** & **PAX-TMP** & **TM** & **TMC** & **TMO** & **TU** & **TUG** & **TOT** \\ \hline No. Vessels & 871 & 1074 & 576 & 2857 & 849 & 303 & 6522 \\ No. Vessels (no. pts \(\geq\) 100) & 502 & 634 & 323 & 1717 & 304 & 194 & 3669 \\ Min Route Distance & 103 & 101 & 101 & 101 & 100 & 100 & 100 \\ Max Route Distance & 4774 & 5315 & 3550 & 4813 & 3610 & 4985 & 4985 \\ No. Routes-avg & 1240.08 & 1143.85 & 873.88 & 950.60 & 570.01 & 1280.52 & \(\sim\) \\ No. Routes-std dev & 956.32 & 971.77 & 602.44 & 650.53 & 495.49 & 937.40 & \(\sim\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of vessels belonging to each of the six classes, before and after data cleaning. The maximum, minimum, and average readings of each class and the standard deviation from the averages are also shown.  
Figure 1: Flowchart of the data-cleaning procedure. (1) Identifying and eliminating any data-formatting errors; (2) removing records with missing data; (3) discarding records with incorrect data (such as velocity exceeding 100 knots, positional data on land, etc.); (4) excluding all vessels for which the number of sequential detections is below a certain threshold (100 points); (5) excluding any vessels that appear in two different and distant parts of the world within a short time span.
## 3 Results and Discussion
### Validation of the Modified Folin-Ciocalteu Assay Suited to Honey Analysis
#### 3.2.3 Limit of Detection (LOD) and Limit of Quantitation (LOQ)  
For the modified FC assay the LOD was found to be 2.55 \(\upmu\)g/mL, and the LOQ was 7.74 \(\upmu\)g/mL.
###### Abstract
The efficiency of this elastic energy use is known as mechanical efficiency (ME) [17], and it can be estimated in the quadriceps femoris muscle-tendon unit (MTU) as the ratio between the positive work during knee extension and the total work of the contraction [18]. This ratio indicates how effectively stored strain energy is utilized. Because the magnitude of the stored and reused strain energy depends on the force applied to the tendon and the magnitude of tendon elongation, the quadriceps femoris may perform differently under various conditions, resulting in different ME [19,20,21]. For example, by increasing initial muscle tension, the muscles can resist more stretching, allowing for greater tendon elongation during contraction [21]. This, in theory, could increase the magnitude of strain energy storage and reuse, thereby enhancing ME. The mechanical load during knee flexion influences the degree of knee flexion and determines the lengthening magnitude of the quadriceps femoris MTU, which also affects tendon lengthening. In laboratory measurements, the amount of work during knee flexion can be pre-programmed to a given level; therefore, the degree of knee flexion will depend on the participant's ability to stop knee flexion under given conditions, making the measurement more individual. In this case, ME can be assessed at an individual range of motion of the knee while maintaining the same stretch load for all participants [18]. Currently, limited information is available on the influence of quadriceps femoris MTU's morphological parameters on ME during SSC contractions across different modalities. Expanding this knowledge could improve our understanding of the relationship between the MTU's structural-functional characteristics. This study aims to investigate the association between ME and the morphological parameters of the quadriceps femoris MTUduring various SSC contractions, focusing on lower contraction velocities. As part of a larger project, this research assesses the quadriceps femoris MTU properties to understand their effects on elastic energy storage, utilization, and ME. We hypothesize that a smaller muscle-to-tendon CSA ratio (AmAt), which indicates a closer match between the muscle CSA and the tendon CSA, is related to enhanced energy storage and thus an improvement in ME. A thinner and longer tendon may exhibit greater compliance, while a thicker and shorter tendon is likely to be stiffer. Therefore, under low stretch load conditions, ME is expected to be higher with a thinner and longer tendon, while under high stretch load conditions, ME should be greater with a thicker and shorter tendon.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 1 Introduction
### Autonomous Vehicles (Decentralized Control)
#### 1.3.2 Decision-Making
Although the Nussbaum gain approach [52] has been suggested to stabilize such issues, systematic methods related to CBF problems are yet to be established. To overcome the limitations of existing AIM methods, we propose an aCBF-based approach that directly addresses the challenges posed by power transmission loss, ensuring robust safety in autonomous intersection management. Power transmission loss, which results in partial or complete loss of controllability, are a critical issue in AIM scenarios, as they can lead to collisions or delays in vehicle navigation. Our method mitigates these issues through specially designed update laws that adapt to controllability loss, maintaining safety constraints even under fault conditions. The proposed approach provides the following advantages:  
1. Adaptive fault compensation --By integrating an adaptive update mechanism, the method effectively compensates for power transmission loss, ensuring safety constraints are upheld even in the presence of controllability loss. 2. Decentralized control--The framework operates in a decentralized manner, eliminating the need for traffic lights, reducing vehicle parking delays, and demonstrating scalability in large autonomous systems. 3. Computational efficiency--Unlike network-learning-based methods, aCBF-based control does not require pre-training, reducing computational overhead while guaranteeing safety in multi-agent systems. 4. Broad applicability--The ability to simultaneously address power transmission loss robustness and broader AIM challenges makes this approach a promising solution for autonomous intersection management. This paper is organized as follows: CBF preliminaries are covered in Section 2. Section 3 presents the safe adaptive AIM problem and proposes a CBF-based control algorithm for multi-agent systems. Simulations and experiments verifying the algorithm are detailed in Section 4, and conclusions are provided in Section 5.
## 4 Mapping YANG to OPC UA  
The proposed mapping involves a two-stage approach. As depicted in Figure 6, the first stage involves the offline generation of the OPC UA information models from the 5G network systems' YANG schema. The model generator takes the YANG schema as input and generates the mapped OPC UA information model files based on the mapping metadata. In the second stage, at runtime, the adapter converts the YANG model instances into corresponding OPC UA information model instances for the connected device. The primary motivation of this article is to propose a solution to address the first stage with the support of generating the OPC UA information models from YANG models. The second stage shall be addressed in future work.  
The NodeSet2.xml structure is too complex for tools to generate, so the OPC Foundation has introduced the UAModelDesign.XSD schema to represent the model design in a more simplified XML file structure. This article presents a proposal for mapping YANG statements to OPC UA model design XML constructs, thus allowing the generated XML file to be converted into an OPC UA NodeSet2.xml file using the existing UA model compiler.  
Figure 6: YANG to OPC UA model mapping.  
The following sections offer a detailed proposal for mapping YANG constructs to OPC UA model design XML constructs.
## 3 Methods
### Encoder  
The audio encoder and the visual encoder are independent but share the same structure. The network architecture of one layer is shown in Figure 4. Our encoder structure consists of six stacked Transformer encoder layers, each based on a sparse attention mechanism. Each layer includes a positional encoding layer, a sparse attention mechanism, a feed-forward neural network, layer normalization, residual connections, and a dropout layer.
## 4 Results
### Agreement Score  
The agreement scores were calculated in two ways. First, all selected microgestures for each command (2 to 4 gestures were selected by each participant) were used to calculate the agreement score using Equation (1) (Figure 6). In this case, the command _shrink/enlarge_ had the highest agreement score and _object translation_ had the lowest score. However, prior studies [37; 41; 42; 44] calculated the agreement score using the single most preferred gesture from each participant for a given command. Thus, a second method was used to calculate the agreement score by only using the most preferred microgesture for a command (Figure 7). In this case, the command _cursor_ had the highest agreement score. The impact of the number of microgestures assigned to a command was of interest. Therefore, we performed an ANOVA analysis to compare differences in agreement scores between the two methods of calculating agreement and found a significant difference (_p_ = 0.006). However, the correlation between the two methods of calculating the agreement scores was strongly correlated (R = 0.62).
# Automated Traumatic bleeding Detection in Whole-Body CT Using 3D Object Detection Model
## 2 Materials and Methods
### Evaluation  
We evaluated the performance of the proposed method using a six-fold cross-validation strategy (Figure 4). The dataset was randomly partitioned into six subsets, each consisting of approximately 12 to 13 cases and 50 to 52 bleeding regions. In each fold, four subsets were allocated for training, one for validation, and the remaining one for testing. The proposed CAD software was implemented using Python 3.8.12, PyTorch 2.1, and CuPy 13.4.0. All computations were performed on a workstation equipped with an Intel Core i9-9900K 3.6 GHz eight-core processor, 64 GB RAM (Intel, Santa Clara, CA, USA), and an NVIDIA RTX 4000 SFF Ada Generation GPU (Nvidia, Santa Clara, CA, USA). The operating system was Ubuntu 22.04.3 LTS running on Windows 11 via the Windows Subsystem for Linux 2 (WSL2), with NVIDIA Driver version 537.70, CUDA 12.1, and cuDNN 8.9.5.  
The detection performance was evaluated using free-response receiver operating characteristic (FROC) analysis [28]. A bleeding candidate was classified as a true positive (TP) if it satisfied at least one of the following conditions: (a) the center of the predicted bounding box is located within the corresponding bleeding region, or (b) the Euclidean distance between the predicted box center and bleeding center is less than the bleeding radius. Candidates who did not meet these criteria were classified as FP. We applied the bootstrap method [29] to compute 95% confidence intervals (CIs) by drawing 1000 case-level replicates, each preserving the original number of cases. FROC curves were recalculated for each method for each replicate.
## 3 Experimental Results and Discussions
### Residual Displacement  
Residual displacement refers to the irrecoverable displacement experienced by a structure after being subjected to seismic activity. Residual displacement versus lateral peak displacement of the three bent specimens is presented in Figure 12. The residual displacement is obtained by taking the absolute mean value of both loading directions.  
The residual displacement of all three bents increases with peak lateral displacement. Among them, B2-PGS exhibits the most significant residual displacement, reaching up to 37.80 mm when the peak lateral displacement \(\delta\) = 75 mm. Comparatively, at the same peak lateral displacement, the residual displacement of bent B2-PGT is significantly less than that of B2-CIP and B2-PGS. The results demonstrate that the GSS-GCT connection has a constraining effect and can provide the precast segmental bent with better self-centering capability.
# Tracking and Registration Technology Based on Panoramic Cameras
## 3 Method
### Panoramic Pose Estimation Sub-Network
However, in real-world environments, moving objects and dynamic occlusions are common, and many surfaces exhibit reflective properties. These issues violate the illumination-invariance assumption critical to pose estimation tasks, thus impairing the network's ability to predict accurate poses. To address these challenges, the camera pose estimation sub-network incorporates an occlusion mask module, designed to generate multi-scale occlusion masks that mitigate the effects of such violations within the image sequence. The occlusion mask module consists of convolutional layers followed by a Sigmoid activation function. Since the panoramic depth estimation sub-network predicts depth maps at two different scales, corresponding reconstructed images are generated via the view reconstruction module. Occlusion masks are applied to these reconstructed images when computing the photometric loss, and it is therefore essential to ensure resolution consistency between the occlusion masks and the reconstructed images. Consequently, the  
Figure 3: Panoramic pose estimation sub-network architecture. occlusion masks are estimated from feature maps output by the first two residual blocks of the feature extraction module, matching the respective scales. The occlusion mask is a grayscale image with values ranging from 0 to 1, where pixels with intermediate intensity represent regions occluded due to object motion, helping to reduce the adverse impact of occlusion on accurate pose estimation. The output feature dimensions and hyperparameters for each module within the camera pose estimation sub-network are summarized in Table 3.
# 3-D Printed Fabry-Perot Resonator Antenna with Paraboloid-Shape Superstrate for Wide Gain Bandwidth
## 3 Design of the Proposed FPRA with Wide Gain Bandwidth
### Proposed FPRA with a Paraboloid-Shape Superstrate  
To broaden the gain bandwidth of the FPRA, a paraboloid-shape superstrate is designed to replace the traditional planar superstrate. Compared to a planar one, a cambered superstrate provides multiple resonant heights, and thereby satisfies the resonant condition in a wide frequency band.  
Figure 7 gives the simulated gain-frequency response against \(H_{p}\) for the proposed FPRA. One can observe that the higher \(H_{p}\) (from 4 mm to 12 mm), the wider gain bandwidth can be achieved, but the peak gain will drop slightly. When \(H_{p}\) is up to 10 mm, the increase of \(H_{p}\) will not bring the improvement of bandwidth any more. For the purpose of wideband and high-gain performance, \(H_{p}\) is set as 10 mm.  
Figure 4: (**a**) Simulation model of the unit cell; (**b**) reflection phase of the planar superstrate.  
Figure 5: Gain-frequency response against \(H_{c}\) for the FPRA with a traditional planar superstrate.  
Figure 6: Simulated performances of the FPRA with a traditional planar superstrate. (**a**) \(\mid\)S\({}_{11}\mid\); (**b**) radiation pattern at 6 GHz.  
Figure 7: Gain-frequency response against \(H_{p}\) for proposed FPRA.
# Borehole Condition and Limit Pressure Differential Analysis in Carbonate Reservoirs
###### Abstract  
In deep carbonate reservoirs, testing and production with open-hole completion can help release the maximum production capacity. However, because the reservoir is subjected to high in situ stress, if the test pressure differential is too large, the wellbore collapse and instability will occur easily, causing downhole accidents. Therefore, it is necessary to determine the state of the borehole during the open-hole test in the carbonate reservoir and analyze the ultimate test pressure differential accordingly to ensure test safety. Considering the characteristics of open-hole completion, based on the mechanical properties of the carbonate reservoir and the stress distribution around the borehole during testing, a calculation method of the elastic zone, plastic zone, and residual failure zone around the open-hole wellbore was proposed. Regarding actual engineering data, a criterion for the overall stability of the open-hole section had been established from three aspects: the volume ratio of the plastic zone; the failure zone around the wellbore; and the failure angle on the borehole wall. According to this criterion, it is possible to determine the ultimate pressure differential during the open-hole test process and provide theoretical support for designing the open-hole completion test and production parameters for deep carbonate reservoirs.  
carbonate; open-hole completion; wellbore stability; limit pressure differential Article