esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 5 Evaluation and Results
### Peer-to-Peer Scenario  
The purpose of this experiment is to evaluate the performance of communication between peer nodes, with the paths between source and destination defined by the SD6 WSN peer-to-peer application in comparison to the paths obtained by RPL. The latter optimizes the communication between the nodes and 6 LBR, causing peer-to-peer paths to possibly involve a deviation to a common ancestor. The SD6 WSN application has the unified view of the topology and uses a centralized shortest path algorithm to compute the paths, installing the corresponding flows along them. The simulation scenario consists of 25 nodes in a grid-type topology, plus the 6 LBR, highlighted in gray in Figure 22. All nodes have been compiled with a server and a client, so they can send and receive packets from any node in the grid, simulating the traffic between peer-nodes.  
Figure 21: Average latency for the 150 m-range scenario.  
Figure 22: The \(5\times 5\) m grid topology.  
The number of possible source-destination pairs is 600 for the topology of Figure 22. Among them, sixty pairs are randomly selected (offline) in three groups of 20. In each group, the origin is not repeated, but the destination is chosen without restrictions, except when the destination is the origin itself. An auxiliary CoAP resource is compiled to each node so that it can receive a configuration message informing its destination. Each source node sends 30 packets with a 20-byte payload, one every 10 s. The simulation scenario is shown in Figure 23. Paths are computed ordinarily by RPL. The SD6 WSN application computes the paths according to the unified topology, installing the corresponding flows on the nodes along them. In the case of topology changes, the SD6 WSN application recomputes the affected paths and proceeds with the changes of the corresponding flow tables.  
COOJA is executed in \"nogui\" mode, using the UDGM model, with transmission range set to 25 m and interference range to 50. The quality of wireless media is set to 75% of success in transmission and 100% in reception. We performed 10 simulations, each with three rounds of 20 origin-destination pairs. In each round, each origin node sends 30 UDP packets, with the random seed of the UDGM model changed. Figure 24 shows the average latencies with the 95% confidence interval. It can be observed that average latency is 30.87% lower in SD6 WSN when compared to RPL. This result was expected because RPL is optimized for traffic between nodes and the 6 LBR; however, the gain is significant and indicates the potential of the proposed architecture.
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 3 Cluster-Based Control Message Management for FANET  
In this section, we introduce the Cluster-bAsed control Plane messages management in sOftware-defined flying ad-hoc NEtwork, called CAPONE. It aims to reduce the network overhead while guaranteeing the control message delivering in FANET scenarios. Based on UAV contextual information, the controller is able to predict UAV information without disseminating control messages. It divides the network into groups of UAVs to reduce the network overhead while guaranteeing the control message delivering in FANET scenarios. In this sense, CAPONE determine the number of clusters using the Gap statistics method, which is input for a Fuzzy C-means method to determine the groups. In this following, we present the main functionalities introduced by CAPONE, how the network operates during the clustering process, and how the nodes behave while inside a group.
# Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions
## 6 Conclusions  
The developed frequency contour-strip method, from the theoretical perspective, is an extension of the frequency contour-line method. From the technological perspective, the frequency contour-strip method functions as a promotion of the frequency contour-line method. The developed method enjoys two predominant features in characterizing damage in beam-type structures: instantly establishing eigen-frequency panorama and accommodation of noise effect, substantially overcoming the limitations of existing frequency contour-line method. The experimental results show that the frequency contour-strip method can characterize damage in beam-type structures with more efficiency, greater accuracy, and stronger robustness against noise. Noticeably, the proposed method is developed based on the eigen-frequency, a concept within the scope of linear dynamics. Therefore, the viability of the proposed method is confined to linear or approximately linear dynamic systems. Future following work will address the following issues: (i) validation of the proposed method in beam-type structures under other boundary conditions; (ii) extension of the proposed method for multiple damage detection in beam-type structures; (iii) extension of the proposed method to structures with more complex geometries; (iv) extension of the proposed method to real-time online damage identification in structures.  
Methodology, J.P., H.X., M.C. and H.J.; validation, J.P., H.J., T.D., D.S. and M.C.; data analysis, J.P., X.Z., D.S. and T.D.; resources, M.C. and D. S.; writing--original draft preparation, J.P. and X.Z.; Experiment: H.J., T.D. and J.P.; writing--review and editing, H.X., T.D. and D.S. All authors read, discussed, and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key R&D Project of Jiangxi Science and Technology Department (No. 20192 BBH80021), National defense fundamental scientific research project (XXXX2018204B011), the Nanjing Science and Technology Project (No. 202002014), and the Fundamental Research Funds for the Central Universities of China (No. 2019B52914).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data
## 2 Materials and Methods
### 4 DUS Analysis and Contour Structure  
Each 4 DUS dataset is loaded into a custom interactive toolbox developed in MATLAB (MathWorks Inc., Natick, MA, USA), where data are first reoriented to align to a standard axis and then the endo- and epi-cardial boundaries are manually tracked across a representative cardiac cycle (Figure 1A). The standard axes follow a cartesian coordinate system, and are defined by: (1) the left-ventricular apex and center of the base both fall on the \(z\)-axis, (2) the anterior and posterior walls fall along the \(y\)-axis, and (3) the septal wall falls on the negative \(x\)-axis (i.e., standard radiological orientation). Following reorientation, the \(z\)-axis location of the apex and base are tracked across the cardiac cycle (Figure 1B). Then, iteratively at each point in time, four equally spaced parallel short-axis slices are interpolated from the reoriented 4 DUS data, corresponding to 25, 50, 75, and 100% of the distance from the apex to base. The initial tracking of base and apex locations allows for through-plane motion to be compensated for during subsequent wall-tracking.  
In order to create a final three-dimensional mesh of the endo- and epi-cardial boundaries of the left ventricle, a structured subset of points was defined (Figure 1C) such that (1) each of the four parallel slices contains six points for each of the two boundaries, (2) those points are constrained to equally spaced rotations around the central \(z\)-axis (i.e., 30, 90, 150, 210, 270, and 330 degrees relative to the positive \(x\)-axis), and (3) the distance between each point and the central \(z\)-axis is a function of relative time across the cardiac cycle. Once all points are individually repositioned across the cardiac cycle to define the regional kinematics, hobby splines were used to interpolate a three-dimensional mesh of the left-ventric at a standardized array of cycle-positions. Specifically for this work, the  
Figure 1: Schematic for post-acquisition analysis on left-ventricular 4 DUS data, including: (**A**) spatial reorientation to align with a central \(z\)-axis (i.e., inferior (\(-z\)) to superior (\(+z\))), (**B**) tracking of apex and base locations, and (**C**) definition of boundary points on the endo- and epi-cardial boundaries (Equation (1)), excluding papillary muscles, to be tracked across the cardiac cycle.  
final 4D mesh of the left-ventricle included 60 locations around the \(z\)-axis, 60 locations from the apex-to-base along the \(z\)-axis, and 60 time-points across the cardiac cycle.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 1 Introduction  
Wireless-sensor-network (WSN) technology has rapidly developed because of its convenience and prospects. It can significantly improve living quality in many important fields, such as environment monitoring [1] and surveillance [2], vehicle tracking [3, 4], exploration [5, 6], and other sensing tasks [7]. According to Reference [1], WSNs extend the human ability \"to monitor and control physical world\". This means that WSNs provide a more intelligent way to link the physical world with humans. It is worth noting that the positions of sensor nodes are key information for WSNs to fulfil various tasks. Therefore, as a preliminary task, cooperative localization has aroused increasing interest, especially in indoor scenarios where satellite communications cannot be employed. In general, cooperative localization has two main categories: rangefree methods and range-based methods [8]. Rangefree methods are easier to implement, but their accuracy is lower than range-based methods [9]. The most common range-measurement techniques of range-based localization are based on received signal-strength indicator (RSSI) [10] and time of flight (TOF) [11]. The RSSI technique has a lower cost than the TOF technique, but the latter has higher accuracy. In this paper, we mainly study the localization problem based on range-based technology. Some related work in this field is listed as follows.
## 2 Materials and Methods
### Tracking Method
#### 2.5.3 Tracking Model  
An improved model is shown in Figure 5.  
This study proposed a new tracking method for tracking targets in panoramic scenes, and the tracking flow is shown in Algorithm 1. The inputs to the algorithm were a video sequence (V), a target detector (Det), an appearance feature extractor (F), a detection score threshold (\(\tau\)), and a new track threshold (\(\eta\)). The outputs were the tracks (T) of the video, each containing the bounding box and the unique ID of the object in each frame. For each frame in the input video, this algorithm used a detector to detect the detection frames in which all targets appear in the frame and the corresponding scores of each detection frame. Then, according to the set threshold, the detected frames were categorized into \(\mathrm{D}_{\mathrm{high}}\) and \(\mathrm{D}_{\mathrm{low}}\) according to their scores. Those with detection scores higher than the threshold were categorized as \(\mathrm{D}_{\mathrm{high}}\) and the target features were extracted and stored in \(\mathrm{F}_{\mathrm{high}}\), while those lower than the threshold were categorized as \(\mathrm{D}_{\mathrm{low}}\). After distinguishing the high score detection frame and the low score detection frame, first, we obtained the affine matrix (\(A_{\mathrm{k-1}}^{k}\)) by calculating the data of the \(\mathrm{k-1 st}\) frame and the \(\mathrm{kth}\) frame and then used the Kalman filtering method to predict the trajectory (\(\tau\)) of the previous frame to obtain the new position in the current frame. Finally, we used the calculated affine matrix (\(A_{\mathrm{k-1}}^{k}\)) to correct the prediction frame of the new position and obtain the final prediction frame. Figure 5: The flow of the proposed method. Next, the first association was started. The first correlation between the high score detection frame and all the trajectories was performed. We used a combination of motion and appearance information for the association. First, the IOU between the high-score and trajectory detection frames was calculated to obtain \(\text{C}_{\text{iou}}\). The features extracted from the high-score detection frame and the features of the trajectory detection frame were calculated to obtain \(\text{C}_{\text{emd}}\), and finally, the Hungarian protocol was used to perform the matching. The detection frames that were not matched successfully were saved in \(\text{D}_{\text{remin}}\), and the trajectories that were not matched were deposited in \(\tau_{\text{remin}}\). We used the set threshold to judge the detection frames in \(\text{D}_{\text{remin}}\), and to determine whether they were in the lower left or lower right part of the video, and if they were, we stored them in \(\text{D}_{\text{cor}}\). Input: Video sequence (V); target detector (Det); appearance feature extractor (Enc); high-score detection box threshold (\(\tau\)); new track establishment threshold (n); corner range judgment threshold (cor). Output: Target tracks.
## 4 Prediction Algorithm of Maritime Traffic Condition
### Training and Experiment  
The training model was designed using LSTM considering that maritime traffic data are time-series data. Although LSTM has the drawbacks of requiring a long training time,  
Figure 5: Average/maximum/minimum maritime traffic congestion (days 3, 6, 12, and 24).  
consuming a large amount of memory, and being vulnerable to overfitting and sensitive to random weight initializations, it was chosen due to its ability to handle long-term dependencies and overcome the vanishing gradient problem commonly found in time series prediction tasks. The LSTM includes three gates: input, forget, and output; each gate determines the important information from the past and present input information via the sigmoid and hyperbolic tangent functions [37]. The LSTM was composed of three layers, and the hidden layer was set to 256 dimensions. The dimension of input layer changes depending on the scenario. Figure 6 illustrates the configuration of the LSTM in the developed algorithm while Table 2 summarizes the hyperparameters for training.  
The ratio between the training and validation data for the model was set as 8:2. We conducted an evaluation by dividing the data into 7:3 for training and validation and compared the results with the previous 8:2 division. We did not observe significant differences in the trend of result. The normalized mean square error (NMSE) was used as the loss function. The mean square error (MSE) and NMSE are calculated using  
\[\text{MSE}(\text{x},\ \text{y})=\sum_{i}(\text{x}_{i}-\text{y}_{i})^{2}/n \tag{14}\]  
Figure 6: LSTM configuration.  
\[\text{NMSE}(\text{x},\text{y})=\text{MSE}(\text{x},\text{y})/\text{MSE}(\text{x}, \text{0})=\|\text{x}-\text{y}\|_{2}^{2}/\|\text{x}\|_{2}^{2} \tag{15}\]  
A total of eight training scenarios were created by dividing the features into the trend and prediction. Table 3 lists the training and experimental scenarios.
# THz-TDS Techniques of Thickness Measurements in Thin Shim Stock Films and Composite Materials
## 3 Experiment System and Measurement  
### Measurement System  
Figure 2 shows a photo of the THz-TDS system, which is a non-destructive testing device. This system is used to collect the material characteristics and scan the image of the specimen. The T-ray system used in this study was produced by TeraView Ltd. Cambridge in the United Kingdom. This system was composed of the time domain spectroscopy (TDS) pulse device and the frequency domain continuous wave (CW) device. It was composed of TDS technologies for generating, controlling, and searching terahertz pulses. The THz-TDS system can obtain an image and improve data acquisition, and its unique structural characteristics for manipulating the T-ray have a direct influence on the image production experiment. This TDS system had a frequency range from 50 GHz to 4 THz, and the delay time reached up to 300 ps. The T-ray beam was concentrated on the focal distances of 50 mm and 150 mm, and the full width at half-maximum (FWHM) values were 0.8 mm and 2.5 mm, respectively. This TDS device can be set for measuring the penetration or reflection (small-angle pitch-catch). The frequency range of the CW device was between 50 GHz and 1.5 THz. The focal distances of the CW device were also 50 mm and 150 mm. The TDS and CW devices were connected to each other through the optical fiber. Figure 3 shows the schematic diagram of the T-ray system.  
Figure 2: A photo of the THz-TDS system for imaging and measuring material parameters.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 5 Conclusions  
Existing navigation systems based on GNSS cannot provide accurate positions in tunnel environments. This study demonstrated that the magnetic field inside a tunnel can be used to accurately estimate the position of a vehicle inside a tunnel, where GNSS is not received. Although the magnetic field manifests high resolution and repeatability in the tunnel, the issues concerning the magnetic sensor calibration and attitude estimation of smartphones must be resolved to accurately estimate the vehicle position using magnetic field readings. After converting the frame of the user's smartphone into the DB frame, and the magnetic sensor was calibrated based on the position before tunnel entrance and the magnetic field DB. In addition, the magnetic field values accumulated over a certain period of time were used to estimate the vehicle position. Moreover, subsequence DTW was applied to compare the user buffer data and minor DB. Consequently, the average position accuracy estimated using magnetic sensor readings accumulated for 25 s was less than 10 m in all scenarios. Thus, the proposed method could accurately estimate the exact vehicle position inside tunnels. Furthermore, drivers will be able to use seamless and stable navigation services in a tunnel environment by combining the proposed technology with existing navigation systems.  
All authors contributed to this research. [PERSON], [PERSON]-H.L., C.Y. and H.K. performed the experiment and analyzed the experimental results. [PERSON] drafted the manuscript. [PERSON] designed the proposed method. [PERSON] reviewed the entire manuscript. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Wi-Fi [11]** & **WSN [12]** & **RFID [13]** & **VLS [16]** & **Proposed Method** \\ \hline
**Mean Error (m)** & Less 30 & Less 3 & Less 3 & Less 1 & 7.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with other technologies.  
**Funding:** This work was supported by the National Research Council of Science and Technology (NST) grant by the Korea government (MSIT) [CRC-20-02-KIST]. It was partly supported by the Institute for Information and Communications Technology Program (IITP) Grant funded by the Korea Government (Ministry of Science and ICT, MSIT) under Grant 2019-0-01401 and by the Multi-source based 3D Emergency LOCALization using machine learning techniques (MELOC).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion  
### Model Validation  
Two FE models without muscles were tested and compared with the results reported by [PERSON] et al. [25]. For validation, the same components and material properties were considered for comparison. RoM was compared using a model consisting of only the L4-sacrum, disc, and ligaments under a preload of 150 N combined with 7.5 Nm on the top of the L4 planes, whereas the nucleus IDP was compared using a model of L3-L4 under a preload of 500 N combined with 7.5 Nm on the top of the L3 plane. Flexion, extension, lateral bending, and axial rotation were considered as boundary conditions. In the Figure 4a, the RoMs for the two versions of the FE model without muscles were compared to previously reported numerical data [37; 38; 39; 40; 41]. Under flexion, a maximum error of about 1.9\({}^{\circ}\) was calculated for all sections, compared to 2.4\({}^{\circ}\) under extension, and the average error for all cases was about 0.6\({}^{\circ}\). The global spine motion exhibited a similar trend.  
In the Figure 4b, the IDP for L3-4 in terms of flexion, extension, lateral bending, and axial rotation was compared to data reported by [PERSON] et al. [42] and [PERSON] et al. [43]. For flexion, lateral bending, and axial rotation, the proposed model predicted slightly lower IDPs compared to the existing models and predicted significantly lower IDPs for extension. [PERSON] et al. [25] reported that the IDP for vertebral motion can be affected by the inherent sacral slope in each individual. Thus, though the IDPs predicted for extension were not similar, the difference was most likely due to differences in the shape of the model: The current model predicted 3-fold less IDP for extension.
## 2 Materials and Methods
### Study Design and Study Population
The main outcomes were defined as the composite of Killip class III/IV, in-hospital mortality, one-month mortality, duration of hospitalization, necessity of cardiovascular surgical intervention, and triple vessel disease. Follow-up was carried out by monitoring 30-day mortality or survival on the national health insurance platform and by contacting the patients or their families directly via a telephone call. The research was carried out in compliance with principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee of \"Carol Davila\" University of Medicine and Pharmacy, Bucharest, Romania (protocol code PO-35-F-03, dated 1 October 2021). All participants provided written informed consent and steps were taken to ensure complete anonymity of the data. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**VARIABLE** & **MEAN** & **SD** & **MIN** & **25\%** & **50\%** & **75\%** & **MAX** \\ \hline Age & 63.423 & 10.41 & 41 & 56 & 64 & 72 & 89 \\ \hline Male & 0.639 & 0.483 & 0 & 0 & 1.0 & 1.0 & 1.0 \\ \hline Female & 0.361 & 0.483 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline BMI \textgreater{} 30 & 0.361 & 0.483 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline Dyslipidemia & 0.567 & 0.498 & 0 & 0 & 1.0 & 1.0 & 1.0 \\ \hline Smoker & 0.515 & 0.502 & 0 & 0 & 1.0 & 1.0 & 1.0 \\ \hline Arterial hypertension & 0.794 & 0.407 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline Dyslipidemia & 0.567 & 0.498 & 0 & 0 & 1.0 & 1.0 & 1.0 \\ \hline Diabetes mellitus & 0.392 & 0.491 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline Old myocardial infarction & 0.237 & 0.428 & 0 & 0 & 0 & 1.0 \\ \hline Systolic blood pressure & 141.753 & 21.907 & 90 & 130.0 & 140.0 & 155.0 & 200.0 \\ \hline Diastolic blood pressure & 82.01 & 12.99 & 50.0 & 74.0 & 80.0 & 90.0 & 120.0 \\ \hline Heart rate & 83.309 & 13.329 & 55.0 & 75.0 & 80.0 & 90.0 & 120.0 \\ \hline Killip class III/IV & 0.186 & 0.391 & 0 & 0 & 0 & 0 & 1.0 \\ \hline MCV & 88.612 & 5.96 & 65.91 & 85.8 & 88 & 93.2 & 104.9 \\ \hline MPV & 9.427 & 1.126 & 6.7 & 8.7 & 9.285 & 10.02 & 12.4 \\ \hline RDW & 14.133 & 1.218 & 11.7 & 13.38 & 13.9 & 14.7 & 18.03 \\ \hline PDW & 17.003 & 0.622 & 15.8 & 16.55 & 17.0 & 17.35 & 18.9 \\ \hline Indication for surgical revascularization & 0.289 & 0.455 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline Triple vessel disease & 0.361 & 0.483 & 0 & 0 & 0 & 1.0 & 1.0 \\ \hline \end{tabular} The table is an original contribution by the authors.  
\end{table} 
Table 1: Primary features of the study cohort.