esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 3 Proposed RBF-GAT Framework  
To acquire the downlink instantaneous CSI of the LEO satellite, we follow the literature [19] and propose a pilot transmission strategy to design the uplink pilots and the IRS phase shifts in the pilot phase. Specifically, all users send their pilot sequences with length \(L\) to the satellite simultaneously. Each pilot can be decorrelated at the LEO satellite because all users' pilot sequences are designed to be orthogonal. We denote the received pilots of user \(k\) at the satellite as \(p_{k}\), which contains rich CSI between satellite and user \(k\). The conventional approach of acquiring CSI uses minimum mean-squared error (MMSE), and its calculation is very complicated, especially for the IRS cascade channel.  
Notably, GAT is an effective way to process structured data that are represented as a graph. In this work, the distribution of users can be regarded as a graph. \(K\) users constitute nodes of the graph, and each node is encoded as a feature vector denoted as \(a_{k}\), which is transmitted to the satellite via uplink and contains the common features (e.g., the locations of IRS and satellite) and the private features (e.g., user locations, category and priority). GAT can track the spatial fluctuations of the network in real-time by processing this feature.  
In this section, we commit to training an RBF-GAT network to directly establish the mapping from \(p_{k}\) and \(a_{k}\) to the precoding matrix and reflect beamforming to maximize the system sum rate. We first introduce the RBF-GAT architecture in detail and then discuss the unsupervised training approach.
# An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest
###### Abstract
This paper proposes a WSN data anomaly detection framework by combining the boxplot method and Isolation Forest algorithm. Firstly, use the boxplot to filter the sub-dataset extracted from the historical datasets--and the sub-sampling sets that more likely contain abnormal samples--to train and construct multiple isolation tree anomaly detectors, which improve the stability and detection performance of isolation trees. Secondly, fitness is calculated by the accuracies of the isolation trees in the training set and the correlations between isolation trees. According to the fitness, the best part of the isolation trees is selected from the isolation tree set species to form the base forest anomaly detector, which eliminates the isolation trees with poor performance and improves classification performance. Finally, when the current data points are at the intersection area of positive and negative samples, then data points with the highest similarity are jointly used to judge whether the current data points are abnormal. Having solved the problem that a single isolation forest anomaly score cannot completely separate abnormal data points from normal data points, the stability and anomaly detection performance are improved.
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 3 Theoretical and Experimental Framework
### Physical Mechanism of Attenuation Due to Rain
On the other hand, Mie approximation describes the scattering when the size of the scatterer is approximately equal to the wavelength and i.e., \(a\approx 1\). The average diameter of a raindrop is about 0.1-5 mm. In the case of Rayleigh scattering by raindrops, which applies when the size of raindrops is much smaller than the wavelength (a condition that is well satisfied for frequencies up to 3 GHz), the scattering function is shown in (3). Specifically, this function refers to the scattering properties of raindrops and thus it depends on the drop radius, its shape, the complex permittivity, and the frequency of the transmitted signal [22; 23]:  
\[f=\frac{\varepsilon-1}{\varepsilon+2}\frac{\pi^{2}}{\lambda^{2}}D^{3} \tag{3}\]  
where \(\varepsilon\) is the droplet's complex permittivity. The scattering function derived by Mie's approximation is  
\[f=\frac{j\lambda^{3}}{\pi^{3}D^{2}}\Bigg{[}\sum_{n=1}^{\infty}(2n+1)(a_{n}+b_ {n})\Bigg{]}^{\star} \tag{4}\]  
where \(j\) is the imaginary unit and \(a_{n},\ b_{n}\) are the Mie's coefficients which are constituted of Bessel functions of order \(n\)[22]. Based on (3) and (4), the specific rain attenuation for vertical and horizontal polarization, \(\gamma_{h,v}\), can be expressed as  
\[\gamma_{h,v}=8.686\cdot 10^{3}\cdot\frac{2\pi}{k}\cdot Im\int\ f_{h,v}(D)\cdot N (D,R)dD\ [dB/km] \tag{5}\]where \(k\) is the propagation constant, and \(N(D,R)\) is the distribution of raindrop size that can be approximated by the relationship of  
\[N(D,R)=8000\text{-}e^{\frac{-4+D}{R^{8.21}}} \tag{6}\]  
where \(R\) corresponds to the rain rate in mm/h [24].
## 4 Discussion
### Implications and Contributions  
Despite the goal of supporting ESRD patients' independent management of their dialysis therapies, our findings strongly suggest that many patients long for partnership in care via bidirectional monitoring between the patient and the patient's provider. Bidirectional monitoring would allow a patient to self-manage their therapies while simultaneously being under safety supervision and support. Remote safety supervision and support entails the recording and sharing of patients' individual health information across geographically diverse regions and allows healthcare workers to access both longitudinal and near-instantaneous recordings of patients' physiological and treatment measures. Remote supervision through monitoring allows health care workers to identify instances of urgent medical and equipment needs or longitudinal trends suggesting patients' need for additional education or support. Such remote patient monitoring has the potential to help facilitate medical decision-making and support patients in self-managing their dialysis therapies outside of medical centers [35, 79, 80, 81]. To our knowledge, the expectation has been partially resolved through a health information sharing platform that is currently available on the market [82]. Although the current health information sharing platform shows potential in supporting patients' successful dialysis by incorporating it into the mobile dialysis system, the platform mainly focuses on in-center treatments and aims to support clinicians' workflow more than patients' self-management of treatment. To better support dialysis across different user groups and locations, our findings can be integrated into data management platforms for both the current and future dialysis devices. Such integrations that are tailored according to users' needs and expectations may strengthen the link between a dialysis community and information technology that in return helps support patients' successful dialysis experiences.  
Building on our previous research, this study deepens our understanding of patients' and care partners' preferences and expectations for the design of a mobile dialysis device. Researchers at the Center for Dialysis Innovation at the University of Washington are currently designing and developing the Ambulatory Kidney to Improve Vitality (AKITV). The AKITV is a mobile dialysis device that allows patients with ESRD to undergo continuous dialysis treatments outside of dialysis centers [57]. The overall goal of the AKITV is to improve ESRD patients' quality of life by providing them with the freedom of treatment location and flexibility in treatment timing. The AKITV is expected to provide similar or greater levels of benefits than current existing home dialysis treatments as it will be designed to allow the patient a greater number of dialysis hours without restrictions on their mobility. This method of treatment will more closely approximate renal functioning and improve biological variability that occurs with the current thrice-weekly dialysis treatments [2].
## 6 Results
### The Effect of Healthy Tissue Blood Perfusion Rate  
The findings in Table 7 reveal the temperature variations caused by different healthy tissue blood perfusion rates at various depths within the breast tissue and breast sizes. At T1 depth, the temperature variations range from 0.9 \({}^{\circ}\)C (2.7%) for a blood perfusion rate of 0.16/s to 0.4 \({}^{\circ}\)C (1.2%) for a rate of 0.54/s. Similarly, at T3 depth, the temperature variations range from 0.87 \({}^{\circ}\)C (2.61%) to 0.2 \({}^{\circ}\)C (0.6%), whereas at T9 depth, they range from 0.85 \({}^{\circ}\)C (2.55%) to 0.1 \({}^{\circ}\)C (0.3%). However, breast size has no detectable effect on the breast surface temperature as the healthy tissue blood perfusion rate is increased. Increasing the tumor depth may also result in undetectable heat variation, especially for smaller breast sizes.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline \multicolumn{8}{c}{**(a)**} \\ \multicolumn{8}{c}{**Surface temperatures on the skin of the breast without tumor \({}^{\circ}\)C**} & \multicolumn{3}{c}{**Surface temperatures on the skin of the breast with tumor \({}^{\circ}\)C**} & \multicolumn{3}{c}{**Temperature variations with and without tumor \({}^{\circ}\)C**} \\ \hline \multicolumn{8}{c}{**Breast size**} \\ \hline Healthy \(w_{\text{b}}/\text{s}\) & 7.5 cm & 10 cm & 17 cm & 7.5 cm & 10 cm & 17 cm & 7.5 cm & 10 cm & 17 cm \\ \hline Healthy & 33.35 & 33.35 & 33.35 & 33.35 & 33.35 & & & & \\ \hline \(0.16\times 10^{-3}\) & 32.5 & 32.5 & 32.5 & 33.4 & 33.4 & 33.4 & 0.9 & 0.9 & 0.9 & (2.7\%) & (2.7\%) \\ \hline \(0.18\times 10^{-3}\) & 33.35 & 33.35 & 33.35 & 33.6 & 33.6 & 33.6 & 0.25 & 0.25 & 0.25 & (0.75\%) \\ \hline \(0.22\times 10^{-3}\) & 33.65 & 33.65 & 33.65 & 34 & 34 & 34 & 0.35 & 0.35 & 0.35 & (1.05\%) \\ \hline \(0.54\times 10^{-3}\) & 33.9 & 33.9 & 33.9 & 34.3 & 34.3 & 34.3 & 0.4 & 0.4 & (1.2\%) & (1.2\%) \\ \hline \multicolumn{8}{c}{**(b)**} \\ \multicolumn{8}{c}{**Surface temperatures on the skin of the breast without tumor \({}^{\circ}\)C**} & \multicolumn{3}{c}{**Surface temperatures on the skin of the breast with tumor \({}^{\circ}\)C**} & \multicolumn{3}{c}{**Temperature variations with and without tumor \({}^{\circ}\)C**} \\ \hline \multicolumn{8}{c}{**Breast size**} \\ \hline Healthy \(w_{\text{b}}/\text{s}\) & 7.5 cm & 10 cm & 17 cm & 7.5 cm & 10 cm & 17 cm & 7.5 cm & 10 cm & 17 cm \\ \hline Healthy & 33.35 & 33.35 & 33.35 & 33.35 & 33.35 & 33.35 & & & \\ \hline \(0.16\times 10^{-3}\) & 32.5 & 32.5 & 32.5 & 33.37 & 33.37 & 33.37 & 0.87 & 0.87 & 0.87 & (2.61\%) \\ \hline \(0.18\times 10^{-3}\) & 33.35 & 33.35 & 33.35 & 33.5 & 33.5 & 0.15 & 0.15 & 0.15 & (0.45\%) \\ \hline \(0.22\times 10^{-3}\) & 33.65 & 33.65 & 33.65 & 33.8 & 33.8 & 33.8 & 0.15 & 0.15 & 0.15 \\ \hline \(0.54\times 10^{-3}\) & 33.9 & 33.9 & 33.9 & 34.1 & 34.1 & 34.1 & 0.2 & 0.2 & 0.2 & (0.6\%) & (0.6\%) \\ \hline \hline \end{tabular}
\end{table}
Table 7: (a) Healthy tissue blood perfusion rate (T1 depth), (b) healthy tissue blood perfusion rate (T3 depth), and (c) healthy tissue blood perfusion rate (T9 depth).
## 2 Methodology  
In this MR simulation, a computer with X-Plane software represented the simulation platform, and a real quadcopter within an airfield represented the real platform. In this paper, the interaction between the real and the virtual quadcopters in real time is called MR simulation. The data flow between the real and the virtual quadcopters are shown in Figure 1. More precisely, we developed a connection interface between the X-Plane flight simulator and the quadcopter ground control station (GCS) using the transmission control protocol/internet protocol (TCP/IP) [30] and the user datagram protocol (UDP) [31] in MATLAB. Both are the suite of communication protocols used for data transferring. For the simulation of the performance of the real quadcopter on the X-Plane platform, we needed the position (latitude, longitude, and altitude) and attitude (pitch, roll, and heading) data from the real quadcopter. The GCS receives the real-time flight data of the real quadcopter through a radio telemetry device. In our case, the developed connection interface needed two ways of communication; one to receive the real-time data from the GCS and the second to send the real-time position and attitude data from the received flight data to X-Plane. Therefore, here, we used, TCP/IP communication between the GCS and developed interface, and UDP communication between the developed interface and X-Plane. By using the developed connection interface, the GCS sends the real-time position and attitude data to X-Plane, and the virtual quadcopter in X-Plane follows the real quadcopter. Consequently, here, the virtual quadcopter interacted with the real quadcopter in real time (i.e., MR simulation).
# Improvements to Supercomputing Service Availability Based on Data Analysis
## 2 System Configuration
### Software Configuration  
Nurion is a Linux-based system that uses Bright Cluster Management (BCM) software for provisioning and managing each node. In addition, a batch scheduler (PBS Pro) is used to manage user-submitted jobs efficiently, and Intel OPA management software is used to manage the interconnection network. Lustre is used as the parallel file system; it uses DDN IME with burst-buffer software for high-performance I/O. To support the execution of various user programs, a range of compilers and message passing interface (MPI) libraries are provided, and commercial software, such as ABAQUS, ANSYS, and GAUSSIAN, can be used.  
We used the scheduler's queue, which is a group of computing nodes that provide particular services depending on the user's purpose. Based on the node type, for KNL, we divided the submissions into exclusive, normal, long, flat, and debug queues. For SKL, we divided them into commercial and norm_skl queues. The job-submit policy for each queue is defined by the maximum time (wall-clock time (WCT)), the maximum number of submitted jobs, and the maximum number of running jobs per user. User-submitted jobs are executed in the order of priorities that are periodically calculated based on user jobs' wait time, requested resource size, and the predefined queue priorities in which jobs reside. All submitted jobs follow the job-submit policies in Table 1. An exclusive queue is for dedicated resources that support large projects and group research. Normal is a general resource queue for both free service users (creative research field, national strategy field, and innovation support field) and paid service users. Long is a general resource queue that requires long-term work and can be used for up to 120 h (5 days). Flat is also a general resource queue with a flat memory that can designate MCDRAM and DDR4 for up to 102 GB of memory use. Debugging is a queue for debugging KNL nodes, in which a general resource queue and interactive work can be used for debugging (shared resources). Commercial is a general resource queue for commercial applications (shared resources). Norm_skl is a general resource queue on SKL nodes, which plays the same role as the KNL normal queue.  
From the user perspective, users can submit jobs according to this supercomputer queue policy. In this process, waiting time is an important factor for users. The users' waiting time is controlled by setting the maximum number of submitted jobs and maximum number of running jobs for each queue. An appropriate configuration for the maximum number of submitted jobs and running jobs is essential because it is an environment that provides supercomputing services to users.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 4 Result and Analysis
### Confusion Matrix  
The confusion matrix for model evaluation is given in Figure 7, where the matrix consists of four key values: true positives (1668), where the model correctly identified class \"1\"; true negatives (1671), where it correctly identified class \"0\"; false positives (329), where it mistakenly classified a \"0\" as \"1; and false negatives (332), where it incorrectly classified a \"1\" as \"0\". From these values, key performance metrics were calculated.  
The performance matrix can be expressed in terms of accuracy, precision, recall, and F1-score, which are calculated as follows:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Values** \\ \hline Epoch & 100 \\ K-Shot & 10 \\ K-Query & 1 \\ Optimizer & Adam \\ N-Way & 5 \\ Inner Learning Rate & 0.1 \\ Outer Learning Rate & 0.01 \\ Batchsz & 2000 \\ Input Image Size & 224 \(\times\) 224 \\ No. of Layers & 3 \\ Feature Embedding Size & 768 \\ Device & Cuda \\ \hline \hline \end{tabular}
\end{table}
Table 1: The MAML parameters and hyper parameters used in the implementation.  
Figure 6: Output of video anomaly detection.  
Figure 7: Confusion matrix for model evaluation.  
* Accuracy = (TP + TN)/(TP + TN + FP + FN) = 3339/4000 = 0.834
* Precision = TP/(TP + FP) = 1668/(1668 + 329) = 1668/1997 = 0.835
* Recall = TP/(TP + FN) = 1668/(1668 + 322) = 1668/2000 = 0.834
* F1-Score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall) = 0.834  
For video anomaly detection, this confusion matrix indicates that the model performs reasonably well, correctly classifying 83.4% of cases. The F1-score, a balance between precision and recall, is 83.4%, showing consistent performance. However, the presence of 332 false negatives suggests that some anomalies are being missed, which could be critical in applications where detecting anomalies is the priority. The false positives (329) indicate some normal events are being flagged as anomalies, which may lead to unnecessary alerts. Given the nature of anomaly detection, improving recall (currently 83.4%) is crucial to minimize missed anomalies. Fine-tuning the model, adjusting thresholds, or incorporating additional features (temporal) using BiLSTM or 3D swin model as a feature extractor could help reduce false negatives and improve overall detection performance.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 3 Simulation
### Comparison Between Controllers  
Firstly, multilayer MPC, switching MPC, and nonlinear MPC are compared. The reference velocity of the multilayer MPC controller was set to 5 m/s, and that of the other two controllers was set to 2.5 m/s. Figure 4 shows the tracking results. Multilayer MPC and switching MPC successfully completed the control task, however, switching MPC failed. According to [5; 16; 17; 18], switching MPC can complete path tracking when the reference velocity is low or the radius of the reference path is large.  
Figures 5-7 show the longitudinal velocity, articulated angle, and articulated angle speed, respectively. All control inputs and states are within the system constraints. The displacement error is shown in Figure 8. The heading error is shown in Figure 9. The errors of switching MPC are divergent. The maximum errors of the other controllers are shown in Table 2. The maximum displacement and heading errors of multilayer MPC are 92.92% and 77.02% smaller than those of nonlinear MPC, respectively.  
Figure 4: The trajectory of the comparison between controllers.  
Figure 3: Displacement error and heading error.  
Figure 5: The longitudinal velocity of the comparison between controllers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Nonlinear MPC** & **Switching MPC** & **Multilayer MPC** \\ \hline Displacement error & 0.7886m & divergent & 0.0558m \\ Heading error & 0.1510 rad & divergent & 0.0347 rad \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum errors.  
Figure 6: The articulated angle of the comparison between controllers.  
Figure 7: Articulated angle speed of the comparison between controllers.  
Multilayer MPC shows strong real-time performance, as shown in Figure 10. Its maximum calculation time in each control period was 0.0130 s, and its average calculation time was 0.0086 s. The real-time performance of multilayer MPC is significantly better than that of nonlinear MPC. Of course, the best control method for real-time performance is switching MPC, but this control method cannot control the articulated vehicle tracking reference path under this simulation condition.  
Figure 8: Displacement error of the comparison between controllers.  
Figure 10: The computation time of the comparison between controllers.  
Figure 9: Heading error of the comparison between controllers.  
By comparing these control methods, we can see that multilayer MPC has good control precision and real-time performance. From a theoretical point of view, this controller can automatically adjust velocity according to the reference path.
## 1 Introduction  
Pulmonary arterial hypertension (PAH), included in the first group of pulmonary hypertension (Nice, 2018) [1], is a severe, progressive disease in children, that causes pulmonary vascular remodeling and right ventricular (RV) dysfunction over time.  
Transthoracic echocardiography is the most accessible, non-invasive diagnostic tool for the initial assessment and clinical follow-up of children with PAH [2; 3; 4]. In the current guidelines, the importance of the multiparametric approach in the echocardiographic assessment of these children is emphasized [3].  
According to the risk stratification model implemented by the 6 th World Symposium on Pulmonary Hypertension--Pediatric Task Force in 2018 (Nice), and by the updated consensus document of the \"European Pediatric Pulmonary Vascular Disease Network\", some echocardiographic parameters have been suggested as risk factors and predictors of outcome such as right atrial (RA) and right ventricular (RV) enlargement, reduced left ventricular (LV) size, increased RV/LV ratio, reduced tricuspid annular plane systolic excursion (TAPSE), increased systolic/ diastolic (S/D) ratio, pulmonary artery acceleration time, low RV fractional area changes (FAC) and pericardial effusion [3; 4]. However, because these parameters have only level of evidence C (due to lack of pediatric data) and because of the significant operator and interpretation variability of some echocardiographic parameters, the need for identification of more valid, easy-to-determine predictors and treatment goals in pediatric patients with PAH is emphasized [3].  
Tissue motion annular displacement (TMAD) is a speckle-tracking derived echocardiographic parameter in which the annular tissue is tracked toward the ventricular apex. This angle-independent echocardiographic parameter has been used in assessing left (TMAD of the mitral valve) [5; 6; 7; 8] and right (TMAD of the tricuspid valve) [9; 10; 11] ventricular function. However, its usefulness in the assessment of the right ventricular function in PAH children has not been studied so far.  
The aim of our study was to determine the prognostic value of TMAD of the tricuspid valve in children with PAH.
## 2 Materials and Methods
### Characterization  
To investigate the structural properties, field emission scanning electron microscopy (FE-SEM; Siron FEI, Hillsbro, OR, USA) images were obtained at an accelerating voltage of 10 kV. To obtain the exact thickness of PVA NF-based filter media, the total thickness of ten filter media was measured using Vernier calipers (CD-15 CPS, Mitutoyo, Kawasaki, Japan), and then the average thickness of one filter medium was calculated. To investigate the optical properties, the reflectance spectra of the PVA NF-based filters were obtained using a UV-VIS-NIR spectrophotometer (Cary 5000 UV-Vis-NIR, Agilent, Santa Clara, CA, USA).