esa-sceva/satcom-chunk-collection huggingface dataset:
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 3 XBeats System Architecture
### Hardware Specifications
#### 3.1.4 Edge Classification  
XBeats integrates a binary classifier implemented on the edge to perform preliminary analysis on the collected raw ECG signal for anomaly detection. The classifier can be either integrated into a mobile application or an edge device with enough resources. The objective of the proposed edge classification is to optimize data transmission to the backend system compared to the continuous mode of operation. The classification module also aims to optimize the device's battery life by regulating the power consumption profile of the communication module by actively adjusting the number of enabled leads for ECG data acquisition. Before the classification task, data preprocessing is applied to the collected ECG data. The preprocessing phase extracts the PQRST features from the ECG waveform using the Pan Tompkins algorithm [37]. The PQRST feature points constitute one single heartbeat. The correlation between two consecutive PQRST vectors facilitates interpreting the patient's heart condition and discovering abnormalities if they exist. The preprocessing step includes detecting multiple R points (R-R interval), which helps measure the heart rate. Also, the PR interval, QRS duration, or QT interval contributes to revealing significant heart condition information. That, in return, adds more confidence to the binary classification detection task. The binary classification module (i.e., integrated into the mobile application) classifies the signals into just two categories; normal, which includes one type of signal, and abnormal, representing all other types of signals.  
The binary classification of ECG signals utilizes the online dataset PTB-XL Arrhythmia Database [38] to train the model on larger populations. The implemented model uses lead II and resamples the ECG data to 300 Hz to match the minimum sampling rate of the signals extracted from our ECG patch. Model training and verification are performed according to the proposed approach in [39]. The preprocessing phase includes an algorithm to extract 85 features from the ECG waveforms mentioned in [39]. Accordingly, we propose a feature selection technique called mutual information (MI) ranking criterion to select the 10 most informative features from Lead II to obtain high accuracy. Furthermore, we filter the top 10 features into 5 features that are used as an input for the classification algorithm. This approach suits our system needs due to the power and time limitation. We only need to extract significant information about the heart condition, which adds more confidence in the binary classification task. The 5 features are a collection of R-R-intervals, HBF (Hermite basis functions), and time-domain morphology features explained in [39]. We test several algorithms to evaluate which yields better results, including Random Forest, Support Vector Machine (SVM), Decision Tree, k-nearest neighbors (Knn), Logistics Regression, and Extra Trees.
## 4 Numerical Analysis of Vibration Localization Characteristics
### Numerical Results
#### 4.2.2 Validation of Eigenvalue Splitting  
The numerical results of the eigenvalues under different mistuning strengths and coupling strengths are shown in Figures 13 and 14, respectively. It can be observed that the numerical results align closely with the theoretical results.  
In summary, the numerical results of the vibration localization characteristics of the CMS are basically close to the theoretical results. Notably, when the mistuning strength is large or the coupling intensity is small, the calculation results obtained from the theoretical model exhibit greater accuracy. Meanwhile, theoretical and numerical results indicate that setting an appropriate mistuning strength and minimizing the coupling strength are conducive to achieving broadband vibration reduction. These considerations should be taken into account when designing a pipeline-oriented CMS.
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
## 7 Comparison with Other Mobility Models
### Depth First-Based Mobility Model  
The mobile sink will traverse the network in this model according to a graph's Depth-First Traversal algorithm. As a result, the starting node is selected randomly, and the movement of the mobile sink is divided into pause periods and motion periods. After selecting the starting point, the mobile sink will move to the new location at a specific speed. It will stay there for a specific period when arriving at the new location. After that, the next location is calculated based on the Depth-First Traversal algorithm, and the mobile sink starts moving toward that location [38]. This model was selected because it has similar behavior to the proposed model in this work, as the motion of the mobile sink is divided into rounds, and the new location of the mobile sink is calculated based on a specified algorithm. It also uses the location of nodes and the properties on the topology or graph.  
From Tables 5-7, the proposed mobility model in this paper has outperformed the other three mobility models under different sink speeds and network sizes.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 2 Materials and Methods
### Framework  
When modelling EDI messages, the modeller is faced with identifying the best features to support their model requirements. When trawling through both structured and unstructured log data, millions of lines of text can become quite daunting--one message alone can have thousands of lines written to the many log files spread over different servers.  
Here, we propose our framework that includes feature identification and feature classification that will support the modelling of EDI messages. Our framework can be used as a guide for the following:  
1. Identifying data that are useful to understand queueing systems. In particular, we analysed file sizes, source file sizes, mode, reference IDs, map names, service and inter-arrival times. We also used categorical data from a queueing systems ticketing system to identify current queueing problem areas.
2. In addition, identifying attributes that may be useful for partitioning data, either individually or in combination, even if these attributes may not have a direct influence on system performance.
3. Also consider partitioning based on ranges of data values (e.g., splitting the data into a head/tail, where application-specific values might be confined to a particular region).
4. Model the data against a range of distributions, using different transformations.
5. If the modelling step is unsuccessful, partitioning the data may give insight into the challenges for parametric modelling or offer successes in terms of modelling. Similar partitioning can be applied to understand the correlation.
# An Experimental Investigation of Passive Jet Control Method on Bridge Tower Wake
## 1 Introduction
The particle image velocimetry (PIV) results show that a synthetic jet could generate a vortex pair further downstream, and the proper orthogonal decomposition (POD) analysis indicates that this vortex pair, together with the vorticity shear layers near the cylinder, forms the vortex structures further downstream. [PERSON] et al. [18] performed an experimental investigation on the suction-based flow control method, investigated the pressure distribution on the surface of a circular cylinder with suction slits, and found that the steady suction method exhibits excellent control effectiveness and can distinctly suppress the VIV, the amplitudes of cylinder vibrations, fluctuating pressure coefficients and lift coefficients of the circular cylinder model. They also applied PIV for further study [19], and the results show that the separation points on the test model in the PIV measurement plane are pushed further downstream, and the wake closure length also varies significantly along the span-wise direction of the test model for the cases with the suction flow control. [PERSON] et al. [20; 21] first proposed a passive jet apparatus, performed an experimental and numerical study to investigate a passive control method that has passive windward suction and a leeward jet over a circular cylinder, and found that the mean drag and the dynamic wind loads acting on the cylinder are suppressed. This apparatus could manipulate the shedding process of the wake vortices, and the jets could modify the wake stability by displacing the region of absolute instability further downstream. This method was also investigated by [PERSON] et al. [22] to apply to a bridge girder to control the flow separation. [PERSON] et al. [23] further developed the passive jet method on a rectangular cylinder, taking a tower column as a prototype. The passive jet also has adequate control effects on the aerodynamic forces acting on the column. The WIV of suspenders was found to be caused by the vortices from the bridge tower shedding into the tower wake [9; 24 of Technology. The wind tunnel contains two test sections. The size (width \(\times\) height \(\times\) length) of the larger one is 6.0 m \(\times\) 3.6 m \(\times\) 50 m, and the smaller one is 4.0 m \(\times\) 3.0 m \(\times\) 25 m. Our experiment was performed in the small section, where the wind speed can be adjusted continuously from 3 m/s to 30 m/s, the turbulence intensity of incoming wind speed is no more than 0.46%. The small section equips two turntable systems and a three-dimensional automated traverse measurement system.
## 3 Polar Coded SDF Cooperation
### Proposed PCSDF Cooperation
Thus, the relay \(R_{opt}\) is frozen and the source S transmits messages to the destination D. The received signals are then expressed as  
\[y_{sd}^{(2)}=h_{sd}^{(2)}\sqrt{P_{s}}x_{s}+n_{sd}^{(2)}. \tag{20}\]  
Finally, the received signals from all links are combined together using the maximum ratio combination (MRC) method [29] at the destination. The combination of all messages is expressed as  
\[y=\left\{ \begin{array}{l}a_{1}y_{sd}^{(1)}+a_{2}y_{rd_{opt}},\quad\beta_{r_{ opt}}/\beta_{sd}>\theta\\ a_{1}y_{sd}^{(1)}+a_{2}y_{sd}^{(1)},\quad\beta_{r_{opt}}/\beta_{sd}\leq\theta \end{array} \right., \tag{21}\]where \(a_{1}\) and \(a_{2}\) are the combining coefficient and \(y_{sd}^{(1)}\) equals \(y_{sd}\) in (2). The instantaneous SNR at the destination is expressed as  
\[\gamma=\left\{ \begin{array}{l}\gamma_{sd}^{(1)}+\gamma_{{}_{rd_{opt}}},\ \ \ \ \beta_{{}_{req}}/\beta_{sd}>\theta\\ \gamma_{sd}^{(1)}+\gamma_{sd}^{(2)},\ \ \ \beta_{{}_{req}}/\beta_{sd}\leq\theta \end{array} \right., \tag{22}\]  
where the instantaneous SNR in Phase I with link S-D is \(\gamma_{sd}^{(2)}\), that in Phase II is \(\gamma_{sd}^{(2)}\), and \(\gamma_{rd_{opt}}\) is the instantaneous SNR of link R\({}_{opt}\)-D. Then, the maximum transmission rate of the entire system using this scheme is expressed as  
\[C_{\text{PCSDF}}=\left\{ \begin{array}{l}\frac{1}{2}\log_{2}(1+\gamma_{sd}^{ (1)}+\gamma_{rd_{opt}}),\ \ \ \ \beta_{{}_{req}}/\beta_{sd}>\theta\\ \frac{1}{2}\log_{2}(1+\gamma_{sd}^{(2)}+\gamma_{sd}^{(1)}),\ \ \ \ \beta_{{}_{req}}/\beta_{sd}\leq\theta \end{array} \right.. \tag{23}\]  
In the above scheme with only one relay link with a sufficient channel gain ratio (i.e., the corresponding instantaneous SNR) is adopted to deliver the messages. Thus, the diversity cannot be utilized, and there is a higher chance of retransmission. An improved version of the above scheme can be realized by combining several messages from several merged R\({}_{i}\)-D links together to generate equivalent and strengthened hybrid signals with instantaneous SNR larger than the cooperation threshold. This can be easily implemented using the maximum ratio combination (MRC) technique, where their SNR can accumulate linearly. In addition, for the purposes of lower computational complexity, a maximum of five path signals can be chosen and merged together for possible signal strengthening to exceed the cooperation threshold. Using this method, the signal diversity is fully adopted with a significant reduction in retransmission and better transmission efficiency. In summary, the main procedures of the proposed PCSDF cooperation approach are shown in Figure 3 and Table 1.  
Figure 3: Flow chart of the proposed SDF cooperation scheme.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 6 Results
### Awgn  
Figure 10 shows the relation between SNR and FER for each modulation scheme, with and without the convolutional encoding (solid and dashed lines respectively).  
Transition of FER values from \(10^{-1}\) to \(10^{-2}\) happens in just 0.8 dB for coding systems with convolutional encoder, while it is approximately 1 or 1.2 dB for schemes without the convolutional encoder.  
As expected, results demonstrate the influence of the convolutional encoding on the robustness of the received signal. Sensitivity differences of 6.5 dB for DBPSK, 8.5 dB for DQPSK and 9.5 dB for DQPSK have been obtained in the tests.  
When the convolutional encoder is used, the difference between DBPSK and DQPSK is approximately 3 dB of SNR in order to obtain the same FER value, whereas for D8 PSK, sensitivity is 4.5 dB below DQPSK.  
The rate of the convolutional encoder is \(1/2\). The lengths of the PPDUs without convolutional encoders are approximately the half of the ones with convolutional encoder, decreasing the transmitted energy for each PPDU by approximately 3 dB. Not using the convolutional encoding has a saving in energy of 3 dB, but a consequence in the reception sensitivity of 6.5 dB, 8.5 dB and 9.5 dB (for DBPSK, DQPSK and DSPSK respectively). This situation makes the modes without convolutional encoding have much worse performance for the same input energy, making them not usable in practice, considering that a certified PRIME product has to be capable of using both. This result is in line with the conclusions obtained in [23].  
Figure 10 shows that DBPSK mode without convolutional encoding is particularly useless as the usable rate is similar to the DQPSK with convolutional encoding but with reduced robustness.  
Figure 10: Relation FER-SNR for AWGN flat channel with payload of 256 bytes.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 3 Results
### Manufacture and Integration of the ARC  
Using the aforementioned design methods and specifications, we manufactured three multi-band ARCs to calibrate the Gapfen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on. Figure 6 is an outer view of the ARC. We can see from Figure 6 that the receiving and transmitting antennas, the radio frequency box (RF box), and the digital control box (DC box) were installed on the same flatform; therefore, there is no relative motion among the antenna, RF box, and DC box when the ARC works. So, the error due to cables connected to the antenna, RF box, and DC box is decreased a lot. Furthermore, 4G antennas for remote communication and a GPS/Beidou antenna for acquiring time were installed on the ARC.  
Figure 7 shows an outer view of the manufactured multi-band antenna, and Figure 8 shows the patterns for the four bands measured in the anechoic chamber of Science and Technology on Antenna and Microwave Laboratory, Xidian University of China. We also show the cross-polarized pattern for comparison. We can see that the cross-polarization level is more than 30 dB lower than the co-polarization level for the L-band and Ka-band and 40 dB lower than the co-polarization level for the C-band and X-band.  
Figure 6: Outer view of the multi-band ARC.  
Figure 7: Outer view of the multi-band antenna.  
The integration of ARC was performed after all the subsystems had been fulfilled. Actually, the main step in integration is the integration of the RF box and DC box. Figure 9 shows an outer view of the integration of the RF box and DC box on the table. During the integration of the RF box and DC box, the standard of the receiver gain and transponder gain were decided. After the integration of RF box and DC box, the RF box and DC box were installed on the ARC, and the RCS absolute calibration of ARC was done.  
Figure 8: Examples of measured patterns: (**a**) L-band; (**b**) C-band; (**c**) X-band; (**d**) Ka-band.  
Figure 9: Outer view of the integration of the radio frequency (RF) box and digital control (DC) box on the table.  
Figure 10 is an outer view of the RF box (RF subsystem) for the multi-band ARC. We can see from Figure 10 that the RF subsystem is very complicated and that a modular design was used. The maximum gain is about 67 dB for the L-band, 55 dB for the C-band, 60 dB for the X-band, and 64 dB for the Ka-band and these can be reduced by 15 dB in 5 dB steps. The gain can also be reduced by 20 dB using a voltage-tuned attenuator.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Selection and Data Extraction  
#### 2.3.1 Literature Search Results  
(i) Scopus database search results  
We first searched the Scopus database in August 2023. This search yielded a total of 254 articles. The retrieved articles were exported into Mendeley, and duplicates were removed. Articles were screened, as discussed, to include relevant articles and exclude nonrelated articles. Then, the full texts were investigated to identify relevant articles. All journal articles were read in detail. A total of 54 articles were eventually included in the review.  
(ii) WoS database search results  
A search on WoS yielded a total of 728 studies. The retrieved articles were exported into Mendeley, and duplicates were removed. Articles were screened, as discussed, to include relevant articles and exclude nonrelated articles. Then, the full texts were investigated to identify relevant articles. All journal articles were read in detail. A total of 324 articles were eventually included in the review.  
(iii) IEEE Xplore database search results  
A search on IEEE Xplore yielded a total of 2579 studies. The retrieved articles were exported into Mendeley, and duplicates were removed. Articles were screened, as discussed, to include relevant articles and exclude nonrelated articles. Then, the full texts were investigated to identify relevant articles. All journal articles were read in detail. A total of 392 articles were eventually included in the review.  
Although not exhaustive, this profile produced many documents for further analysis. We identified 2029 potential studies from all databases before searching for duplicates and excluding them. After removing duplicates and screening, 770 journal articles were identified. A search on Scopus, WoS, and IEEE Xplore returned 54, 324, and 392, respectively. Thus, the search yielded a total of 770 journal articles. The search results are presented in Figure 2. This method is known as \"Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)\" [30].  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Databases** & **Public Target** & **Link** \\ \hline \multirow{4}{*}{Scopus} & Scopus is Elsevier’s abstract and citation & \multirow{4}{*}{[[https://www.scopus.com/](https://www.scopus.com/)]([https://www.scopus.com/](https://www.scopus.com/))} \\  & database of peer-reviewed journals in fields & \\  & such as life sciences, social sciences, & (accessed on 28 October 2023) \\  & physical sciences, and health sciences & \\  & Web of Science provides access to the most & \\  & reliable, integrated, multidisciplinary & \\ Web of Science & research connected through linked content & \\  & citation metrics from multiple sources & \\  & within a single interface. & \\  & IEEE Xplore provides web access to over & \\  & three million full-text documents from & \\ IEEE Xplore & some of the world’s most highly cited & \\  & electrical engineering, computer science, & (accessed on 28 October 2023) \\  & and electronics publications. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of databases.
# Study of Mobility Enhancements for RPL in Convergecast Scenarios
## 3 Related Work
It is impossible for this node to update its own C_ID, since it discards all the DIOs. Moreover, Co-RPL suffers from overhead due to periodical broadcasting and congestion when there are many nodes broadcasting at the same time. [PERSON] et al. [11] proposed a new version of the trickle algorithm (reverse trickle timer) in order to allow mobile nodes to move seamlessly into a routing topology and limit overhead at the same time. They modified DAO by introducing a mobility flag (MF) in order to help other nodes identify mobile nodes. Compared with the standard trickle algorithm, the reverse trickle timer starts from the maximum interval between two consecutive DIOs. They argue that the more the mobile nodes spend time connected to the same parent, the more they are likely to move outside the coverage of the parent. Therefore, after each DIO, the reverse trickle timer is divided by two until the minimum interval is reached. However, the reverse trickle timer only makes sense when the mobile connects to a new parent and remains connected to this parent for a long time. When nodes move away from this parent, the reverse trickle timer will cause a delay on updating information. In order to avoid congestion, standard RPL sets a delay for DAO before sending it. However, the authors did not take this delay into account. This delay will cause DAO message loss when a node moves out the range of its parent node during this delay period. [PERSON] et al. [12] proposed a mobility support called mobility-enhanced RPL (ME-RPL). ME-RPL is mainly based on identification of mobile nodes and dynamic control message management. They modified DIO messages by adding mobile identifier in order to help nodes identify mobile nodes. A node is more likely to choose a fixed node as its preferred parent by checking the identifier in DIO even if the rank of mobile nodes is lower than that of the static nodes. Moreover, a dynamic DIS management is proposed in order to update topology information according to node movement. Authors argue that if the preferred parent changes frequently, the node is in an unstable environment and the next DIS period should be shorter than the previous period in order to quickly obtain topology information. If the preferred parent stays the same, the next DIS period should increase. However, frequent changing of the parent node will result in many DIS messages being broadcast in a short period, causing a large number of DIO messages, which results in large overhead and network congestion. The state-of-the-art of RPL mobility enhancements show that most of the proposals are suited for networks with few mobile nodes. Indeed, in this paper we will show the performance of these proposals when the network has few fixed nodes or only the sink as a fixed node. We will also propose a mechanism that is suited for such scenarios.