esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 4 Discussion
### DL Methodology  
This review clearly shows that CNNs are the most popular DL models for ECG classification thanks to their excellent capability for feature extraction [8]. As ECG signals are time series in nature, RNNs are another popular type of DL model that has been widely adopted. The transformer is a type of relatively new DL model with the emergence of the attention mechanism and has been used in some recent works. In addition, it clearly shows that more studies leverage hybrid DL models for the arrhythmia classification. Specifically, the CNNs often served as feature extractors right after the input layer of the hybrid model [28]. Other DL structures, such as RNNs and transformers, are exploited to further extract refined features. Their results show that in most cases, the hybrid model could achieve better classification performance but induce higher computational complexity [26; 84; 127; 128; 129; 130]. However, as most selected studies consider traditional DL models such as CNNs and RNNs, the investigation into incorporating novel DL models or structures for arrhythmia classification with ECG signals is still limited. With the emergence of novel DL models such as ViT [131] and MLPMixer [132], the adaption of those novel DL models is expected to be introduced for ECG classification to pursue better performance improvement. In addition, most selected works focus on the improvement in classification performance as much as possible, while the interpretability of DL models is generally not discussed. The interpretable DL models [45] are highly desired to make the ECG classification results trusted in real clinical scenarios and could potentially further help cardiologists relate the heart abnormalities to possible hidden features of ECG signals, such as ECG phenotyping discussed in [7].  
In most of the reviewed studies, the DL models are exploited under the supervised learning framework. However, how to leverage DL models in other artificial intelligence frameworks, such as active learning [133] and reinforcement learning [134], to improve the accuracy of ECG diagnosis could be one future research direction. In addition, how to systematically optimize the DL model structures, such as the size of convolutional kernelsand hyperparameters, such as the minibatch size and learning rate, could be another crucial control knob for ECG classification.  
The number of categories considered in the reviewed studies generally ranges from 2 to 9. As the number of categories to be classified in the DL models increases, learning the mapping for arrhythmia classification becomes more challenging [20]. In MITDB, the total number of categories is 26. However, the amount of ECG data in some categories are very limited. Hence, to achieve accurate classification performance with a higher number of categories to be classified, improving the learning capability of the DL process requires research effort, which could overcome the challenges such as the high complexity of the mapping relationship and lack of data in minority categories.
# Decision Support System for Medical Diagnosis Utilizing Imbalanced Clinical Data
## 3 Proposed Methodology
### Data Standardization  
Prior to the multi-label learning process, it is necessary to standardize the value of whole features. Owing to the fact that all features may be presented by different data types and their values may belong to different ranges, the features with higher range values participate more heavily in the training process than the features with lower range values as it would contribute to bias. Therefore, it is necessary to perform data standardization. Min-Max scaling of all values in the range of [0, 1] is performed as:  
\[x_{i}^{*}=\frac{x_{i}-x_{\text{max}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]where \(x^{*}\) is the standardized feature, \(x_{\max}\) is the maximum value of corresponding feature before the standardization, and \(x_{\min}\) is the minimum value of corresponding feature before the standardization.
## 3 TQU-SLAM Benchmark Dataset
### Preparing Ground-Truth Trajectory for VO
 \tag{5}\]  
The transformation matrix according to the \(x,y,z\) axes is presented by \(\theta_{1},\theta_{2},\theta_{3}\) in Equation (6):  
\[\theta_{1}= \begin{bmatrix}Tr_{1}\\ Ro_{13}\\ Ro_{12}\\ Ro_{11}\end{bmatrix} \theta_{2}= \begin{bmatrix}Tr_{2}\\ Ro_{23}\\ Ro_{22}\\ Ro_{21}\end{bmatrix} \theta_{3}= \begin{bmatrix}Tr_{3}\\ Ro_{33}\\ Ro_{32}\\ Ro_{31}\end{bmatrix}  \tag{6}\]  
The results of the transformation are shown in the vector \(X^{\prime},Y^{\prime},Z^{\prime}\) in Equation (7):  
\[X^{\prime}= \begin{bmatrix}x^{\prime}_{1}\\ x^{\prime}_{2}\\ x^{\prime}_{3}\\ x^{\prime}_{4}\end{bmatrix} Y^{\prime}= \begin{bmatrix}y^{\prime}_{1}\\ y^{\prime}_{2}\\ y^{\prime}_{3}\\ y^{\prime}_{4}\end{bmatrix} Z^{\prime}= \begin{bmatrix}z^{\prime}_{1}\\ z^{\prime}_{2}\\ z^{\prime}_{3}\\ z^{\prime}_{4}\end{bmatrix}  \tag{7}\]  
where \((x^{\prime}_{1},y^{\prime}_{1},z^{\prime}_{1}),(x^{\prime}_{2},y^{\prime}_{2},z^{\prime}_{2}),(x^{\prime}_{3},y^{\prime}_{3},z^{\prime}_{3}),(x^{\prime}_{4 },y^{\prime}_{4},z^{\prime}_{4})\) are the coordinates of four points of point cloud data in the real-world coordinate system. From this, we have a linear equation, presented as Equation (8). \[ \begin{split} X^{\prime}=& A\times\theta_{1}\\ Y^{\prime}=& A\times\theta_{2}\\ Z^{\prime}=& A\times\theta_{3}\end{split}  \tag{8}\]  
To estimate \(\theta_{1},\theta_{2},\theta_{3}\), we use the least squares method [46], as in Equation (9):  
\[ \begin{split}\theta_{1}=&(A^{T}A)^{-1}A^{T}X^{\prime}\\ \theta_{2}=&(A^{T}A)^{-1}A^{T}Y^{\prime}\\ \theta_{3}=&(A^{T}A)^{-1}A^{T}Z^{\prime}\end{split}  \tag{9}\]  
Finally, the conversion matrix between the camera coordinate system and the real-world coordinate system is of the form \((\theta_{1};\theta_{2};\theta_{3})\). The coordinates of the center of the marker \((x_{c},y_{c},z_{c})\) in the real-world coordinate system are calculated as Equation (10):\[ \begin{split} x_{c}=&\frac{x_{1}^{{}^{\prime}}+x_{2}^{{}^{ \prime}}+x_{3}^{{}^{\prime}}+x_{4}^{{}^{\prime}}}{4}\\ y_{c}=&\frac{y_{1}^{{}^{\prime}}+y_{2}^{{}^{\prime}}+y_ {3}^{{}^{\prime}}+y_{4}^{{}^{\prime}}}{4}\\ z_{c}=&\frac{z_{1}^{{}^{\prime}}+z_{2}^{{}^{\prime}}+z _{3}^{{}^{\prime}}+z_{4}^{{}^{\prime}}}{4}\end{split}  \tag{10}\]  
The ground-truth data results of the motion trajectory in the real-world coordinate system are shown in Figure 7.
# Detecting Small Size Mass Loading Using Transversely Coupled SAW Resonator
###### Abstract  
In the detection of small size mass loading, such as a single cell, a micro droplet or an aerosol particle, the sensors with longitudinally coupled surface acoustic wave resonator (LC-SAWR) structure can hardly avoid waveform distortions. The relative size of mass loading to the sensitive surface of the detector is the main factor affecting the response of LC-SAWR. The smaller the relative size, the worse the waveform distortion. In order to avoid influences from the mass loading's size, in this paper, a transversely coupled SAW resonator (TC-SAWR) was proposed in order to achieve high performance in sensing small size mass loadings. For the design and simulation of TC-SAWR, the two-dimensional coupling of model (2D-COM) theory and finite element method (FEM) were used in this work. In the experiment, SiO\({}_{2}\) was deposited on the sensor's surface as a small size mass loading. The results from simulation and experiment mutually demonstrated the advantage of TC-SAWR to conquer waveform distortion in the detection of small size mass loading.  
surface acoustic wave; small size load; biochemical sensors; waveform distortion; 2D-COM theory; FEM simulation +
Footnote †: journal: Applied Sciences  
Article  
Detecting Small Size Mass Loading Using Transversely Coupled SAW Resonator.
## 4 Results and Discussion
### Method for Calculating the Reuse Distance  
The method for calculating the distance 'D' from the center of one co-channel cell to the other is as explained as follows; given that A and B are co-channel cells, \(\theta\) is the angle from one co-channel to the other. Therefore,  
\[D^{2}=A^{2}-2 AB\cos(\theta)+B^{2} \tag{1}\]  
\[D=\sqrt{\left(i\sqrt{3}R\right)^{2}+\left(j\sqrt{3}R\right)^{2}-2\left(i\sqrt {3}R\right)\left(j\sqrt{3}R\right)\cos(120)} \tag{2}\]  
\[D=R\sqrt{3(i^{2}+j^{2}+ij)}=R\sqrt{3N} \tag{3}\]  
The co-channel reuse ratio is given as  
\[Q=\frac{D}{R}=\sqrt{3N} \tag{4}\]  
Thus, for the L(3,1) coloring algorithm of the hypothetical cellular grid shown in Figure 14, the co-channel reuse ratio is  
\[Q=\sqrt{3\times N} \tag{5}\]  
where N is the cluster size and is equal to 12. Thus,  
\[Q=\sqrt{3\times 12}=\sqrt{36}=6. \tag{6}\]  
Q is related to cluster size. In a six-sided geometry,  
\[\text{Q=D/R}=\sqrt{(3\text{N})}. \tag{7}\]  
A small value of Q indicates a bigger capacity of the network grid due to the small cluster magnitude. Meanwhile, a large value of Q implies a higher transmission quality as a result of reduced  
Figure 14: Description of cell cluster.  
co-channel interference. Thus, the cellular grid in Figure 14 labeled with the L(3,1) algorithm improves signal transmission quality and reduces co-channel interference.
## 3 Simulation Results
As it can be seen, the current consumption is strongly dependent on the location of the transceivers, as expected from the received signal variations previously shown. These results can be used in order to design the optimal network layout, as a function of the number of employed nodes, the variables to transmit (and hence, the required transmission bandwidth) and the resulting sensitivity level. These previous results could be employed in combination with optimization methods in order to estimate the optimal network topology. Moreover, the density of the nodes within the network would have a clear impact on energy consumption, due to the fact that link balance limitations would be lower, since inter-node distance would also be smaller. Care should be taken however, since a larger density of nodes can also lead to increased interference levels, which could degrade system performance. This comment also applies in the case of changes in the transmitted service, associated with a certain allocated bandwidth and hence, changes in sensitivity levels. Bit Error Rate (BER) is a key parameter that is used in assessing systems that transmit digital data from one location to another. BER expression for QPSK modulation can be calculated by:  
\[BER_{QPSK}=Q(\sqrt{2E_{b}/N_{0}}) \tag{8}\]  
where \(E_{b}=P_{RX}/R_{b}\). The received power (\(P_{RX}\)) has been calculated with the 3D ray launching algorithm for each spatial sample in the considered scenario. With these values, the BER has been calculated and it is shown in Figures 10 and 11, for different values of data rate (\(R_{b}\)) and different values of \(N_{0}\). It can be seen the high variability between the different cases, with higher values of BER when \(N_{0}\) is higher. In addition, it is observed the differences between the different data rates considered, leading to a lower BER for all cases with the lowest data rate considered. These results can be very helpful in order to optimize the design and deployment of a WSN depending on the modulation, the used data rate and the level of \(N_{0}\). Figure 9: Estimation of energy consumption in terms of current values in mA. Figure 11: Bit Error Rate for QPSK modulation for different values of N\({}_{0}\) for data rate of 57,600 bps. Figure 5:0 Bit Error Rate for QPSK modulation for different values of N\({}_{0}\) for data rate of 250 Kbps.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 4 Simulation Result  
In this section, simulation results are presented. The AUV represented by the nonlinear dynamic model is driven to the docking position, where the docking station can catch it. The AUV model, the environment operating scenario and the control system was developed within the MATLAB/Simulink environment. The set of different simulation modules, functions and subsystems are described in Sections 3 and 4 and in Appendix A, using the parameters collected in Appendix B.  
The effect of environmental conditions can change when performing such a task, and in the following two sets of simulation results are given. The first test considersan environmental scenario with no tidal current, so that the vehicle is driven in ideal conditions. The second test considers the AUV performing the docking task in the presence of a tidal current with a speed \(V_{T}=0.514\) m/s and oriented with an angle \(\beta=-0.1745\) rad with respect to the final docking position heading angle. The control system used in the following has the tuning parameters defined as in Table 1.
###### Abstract  
Recent years have seen a growing interest in underwater communication and some progress has been made in this area. However, underwater communication is still immature compared with terrestrial communication. A prime reason for this is that the underwater environment is intrinsically not suitable for propagation of electric waves. Instead, ultrasonic waves are mainly used for underwater communication. Since ultrasonic waves cannot provide sufficient communication speed or capacity, they cannot use existing network technologies, which assume use of radio waves. In particular, communication in shallow water is still an uncharted territory. Few communication technologies are employed in environments where people enjoy scuba diving. This paper addresses problems faced by recreational scuba divers. It proposes constructing an ad hoc mesh-shaped network between divers within a group and use ultrasonic waves as transmission media in order to enable the detection of a stray diver. It also proposes a communication protocol in which messages are relayed in multiple hops, and a message collision avoidance method, which is intended to reduce the rate of packet loss caused by message propagation delay. We have implemented the proposed methods in a network simulator, and compared them with an existing communication method that has no message collision avoidance function, in terms of the packet loss rate, the stray driver detection rate, and the rate of the ability to communicate in multiple hops.  
message collision avoidance; ultrasonic multi-hop message communication; stray diver detection 2018 824 24  
for smooth communication [2; 3; 4; 5; 6; 7]. Since high-frequency radio waves are heavily absorbed in water, ultrasonic waves are normally used for underwater communication. However, the bandwidth of ultrasonic waves is so narrow that channel contention can occur frequently, resulting in high packet loss.  
The present paper proposes a multi-hop message communication protocol that allows scuba divers to communicate with other divers in a group using ultrasonic waves so that they can check whether any member has gone astray. It also discusses the characteristics of underwater propagation attenuation and proposes a method of avoiding channel contention in underwater communication. This method is compared with a method that has no function to avoid message collisions. This paper is organized as follows. Section 2 presents underwater communication technology and technology for avoiding contention in communication involving multi-access to the transmission medium. This section also identifies the position of the present research. Section 3 gives some examples of group management used in scuba diving. Section 4 proposes message communication protocols designed for underwater communication. Section 5 describes the experimental system we have developed to implement and evaluate the proposed protocols. Section 6 discusses evaluation results. Finally, Section 7 gives the conclusions and future issues.
# Comparing a Fuzzy Hybrid Approach with Invariant MGCFA to Study National Identity
## 5 Results
### Fha
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Population Segment** & **RNISI** & **Rank** & **MGCFA-FS** & **Rank** & **MGCFA-PS** & **Rank** \\ \hline \multicolumn{6}{c}{**Country (Year)**} \\ \hline Denmark (2003) & 0.6854 & 5 & 0 & 8 & 0 & 12 \\ Denmark (2013) & 0.5513 & 15 & \(-\)0.1599 & 12 & \(-\)0.1806 & 15 \\ France (2003) & 0.5134 & 18 & \(-\)0.6305 & 20 & \(-\)0.3672 & 20 \\ France (2013) & 0.5380 & 16 & 0.1553 & 5 & \(-\)0.2679 & 19 \\ Germany (2003) & 0.5190 & 17 & 0.0615 & 7 & 0.0632 & 8 \\ Germany (2013) & 0.5111 & 19 & \(-\)0.3238 & 18 & \(-\)0.1932 & 17 \\ Ireland (2003) & 0.6108 & 10 & \(-\)0.2464 & 17 & 0.2807 & 5 \\ Ireland (2013) & 0.4997 & 20 & \(-\)0.1703 & 13 & 0.0360 & 9 \\ Norway (2003) & 0.5853 & 12 & \(-\)0.5625 & 19 & \(-\)0.1880 & 16 \\ Norway (2013) & 0.5690 & 14 & \(-\)0.0714 & 10 & \(-\)0.1983 & 18 \\ Portugal (2003) & 0.7838 & 3 & 0.2637 & 4 & 0.3274 & 4 \\ Portugal (2013) & 0.6409 & 9 & \(-\)0.1710 & 14 & \(-\)0.0095 & 13 \\ Russia (2003) & 0.7815 & 4 & 0.2888 & 2 & 0.3326 & 3 \\ Russia (2013) & 0.8279 & 2 & 0.2868 & 3 & 0.4218 & 1 \\ Spain (2003) & 0.6477 & 7 & \(-\)0.2116 & 16 & 0.1314 & 6 \\ Spain (2013) & 0.5768 & 13 & \(-\)0.1358 & 11 & \(-\)0.0337 & 14 \\ Great Britain (2003) & 0.6026 & 11 & \(-\)0.2105 & 15 & 0.0234 & 10 \\ Great Britain (2013) & 0.6424 & 8 & \(-\)0.0272 & 9 & 0.0226 & 11 \\ United States (2003) & 0.8491 & 1 & 0.6072 & 1 & 0.3402 & 2 \\ United States (2013) & 0.6641 & 6 & 0.0705 & 6 & 0.0940 & 7 \\ \hline \multicolumn{6}{c}{**Political Orientation (Year)**} \\ \hline Far left (2003) & 0.5765 & 8 & 0.1083 & 6 & \(-\)0.0273 & 8 \\ Far left (2013) & 0.4713 & 10 & \(-\)0.2005 & 10 & \(-\)0.2514 & 10 \\ Left, centre left (2003) & 0.6186 & 7 & 0 & 8 & 0 & 7 \\ Left, centre left (2013) & 0.5196 & 9 & \(-\)0.1073 & 9 & \(-\)0.2057 & 9 \\ Centre, liberal (2003) & 0.6956 & 5 & 0.1059 & 7 & 0.1257 & 4 \\ Centre, liberal (2013) & 0.6750 & 6 & 0.1212 & 5 & 0.1180 & 5 \\ Right (2003) & 0.7292 & 3 & 0.1711 & 3 & 0.1494 & 3 \\ Right (2013) & 0.7070 & 4 & 0.1529 & 4 & 0.0581 & 6 \\ Far right (2003) & 0.7829 & 2 & 0.3897 & 2 & 0.3145 & 1 \\ Far right (2013) & 0.7912 & 1 & 0.5536 & 1 & 0.2136 & 2 \\ \hline \multicolumn{6}{c}{**RINISI: Relative national identity synthetic indicator obtained from FHA. MGCFA-FS: National identity indicator obtained from MGCFA full equivalent scalar model. MGCFA-FS: National identity indicator obtained from MGCFA partial equivalent scalar model.**} \\ \end{tabular}
\end{table} 
Table 4: Relative national identity synthetic indicators (RNISI), MGCFA-FS, MGCFA-PS. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Population Segment** & **Born** & **Citizenship** & **Living** & **Language** & **Religion** & **Civic** & **Feeling** & **Ancestors** \\ \hline \multicolumn{6}{c}{**Country**} \\ \hline Total & 0.3449 & 0.3397 & 0.2378 & 0.4425 & 0.4688 & 0.2967 & 0.2946 & 0.4461 \\ Denmark (2003) & 0.3402 & 0.3379 & 0.2401 & 0.2866 & 0.4564 & 0.2041 & 0.2334 & 0.3942 \\ Denmark (2013) & 0.3508 & 0.3546 & 0.2572 & 0.5221 & 0.4052 & 0.2833 & 0.3045
## 3 Diversity and Reliability
### Outage Probability Analysis  
In order to calculate the outage probability, we proceed to calculate the CDF of the SIR as \(\mathbb{P}(\text{SIR}<\theta)=F_{\text{SIR}}(\theta)\) and formulate it for the different transmission schemes in consideration. Note that the threshold is \(\theta=2^{r}-1\)[34], where \(r\) is the target rate. Let us define \(\delta=\frac{d_{0}}{d}\), the ratio between the typical link distance to the other interfering RRH distances from UE.  
We analyze the system performance in terms of outage probability (i.e., \(1-\) Reliability) for all considered transmission schemes. In the following subsection, we provide closed-form solutions for the outage probabilities and expected values. We also define performance metrics such as rate control, energy efficiency, and throughput-reliability trade-off for the considered transmission schemes to test performance and resource usages. In the sequel, the analytical results are then corroborated via Monte Carlo simulations and discussed in Section 4.
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
## 3 Principle of Analysis  
### Equivalent Circuit Model of the Proposed Absorber  
To validate the design's effectiveness, an equivalent circuit model (ECM) of the proposed tunable absorber is introduced, as illustrated in Figure 7. The relative dielectric constant of the F4 BME substrate is denoted as \(\epsilon_{r}\), with the substrate thickness as \(t\) and the air spacer thickness as \(h\). The PIN diode is modeled as a parallel circuit with variable resistance (\(R\)) and capacitance (\(C\)), in series with an inductor (\(L_{CHIP}\)). The elements \(L_{1}\) and \(R_{1}\) represent the distributed inductance and ohmic loss resistance of the semi-split elliptical ring, respectively, while \(L_{2}\) and \(R_{2}\) correspond to the distributed inductance and ohmic loss resistance of the middle strip of the unit. Each cell contains two gaps, with \(C_{1}\) representing the equivalent capacitance of one gap.  
The initial values of the distributed inductance and capacitance were inspired by the calculation formulas in Refs. [17; 18]. Further optimization of the equivalent circuit model was performed using Advanced Design System (ADS), resulting in the following component values: \(L_{1}=0.3\)\(\,\)nH, \(L_{2}=0.2\)\(\,\)nH, \(C_{1}=0.03\)\(\,\)pF, and \(R_{1}=R_{2}=5\)\(\,\)\(\Omega\) (accounting for both the ohmic loss of patches and the dielectric loss of the substrate). Figure 8 compares the results obtained through the simulation and ECM methods. Overall, there is good consistency between the two approaches, although some differences between the ECM and simulation results remain.  
Compared to the wavelength corresponding to the center frequency, the periodic unit length is not sufficiently small, which is the primary factor affecting the accuracy of the ECM method. Additionally, the empirical formulas used are specifically applicable to square rings and rectangular patches. However, the unit topology of the proposed tunable absorber does not follow a regular shape, causing deviations in the initial calculated values and affecting the optimization results.  
Figure 7: Equivalent circuit of the tunable absorber.
## 3 Simulation Results
### Change in the OFDM Subcarrier Space (SCS)  
In the previous sections, simulations are performed for 30 kHz OFDM SCS use; however, 5G networks can also use OFDM SCSs of 15, 60, 120, and 240 kHz to serve other applications with different bandwidth requirements. This flexible use of different OFDM SCS corresponds to the numerology term. However, only the 15 kHz SCS option is available for LTE networks. In this section, we demonstrate that the EVM vs. RB measurement successfully detects jamming attacks for different SCS use cases. For this purpose, simulations are performed for 15 and 60 kHz SCS selections.  
The jamming signal types and jamming frequencies are the same as those described in the previous sections. When the SCS is reduced from 30 to 15 kHz, the transmission bandwidth is halved, resulting in half of the jamming frequencies occupying the spectrum (Figures 8(a) and 10(a)). Conversely, when the SCS is increased to 60 kHz, all jamming frequencies are observed in the transmission bandwidth (Figures 9(a) and 10(a)). Figures 8(b), 9(b), 10(b) and 10(b) show that the EVM vs. RB measurement successfully detects all jamming attacks included in the transmission bandwidth regardless of the OFDM SCS applied.