esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Heart Sounds Using Convolutional Neural Network
## 5 Conclusions  
In this study, we proposed a method for classifying normal and abnormal heart sounds. Multiple features extracted from eight domains were fed into the designed CNN model, which was used to select features and classify heart sounds. A GAP layer was introduced into the designed CNN in order to obtain global information about the feature maps to improve the classification performance. This was also shown to reduce the model's complexity and avoid overfitting. Class weights were set during the training process to solve the problem of class imbalance, which is an issue that commonly arises during the processing of medical images and signals. The results obtained in this study have demonstrated the efficacy of the proposed method. Conceptualization, F.C. and H.T. ; methodology, F.L. and H.T. ; software, F.L. ; validation, F.L. and S.S.; formal analysis, F.L. ; investigation, F.L. and H.T. ; resources, H.T. ; data curation, H.T. ; writing--original draft preparation, F.L. ; writing--review and editing, F.C. and K.M. ; visualization, F.L. ; supervision, F.C. ; project administration, F.C. and H.T. and K.M. ; funding acquisition, F.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 91748105&81471742; the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China. The authors declare no conflict of interest. Appendix A Note: Because there are a lot of operations of mean and standard deviation, for simplicity and clarity, in the second column of all the tables, \"m\" and \"sd\" are used to represent mean and standard deviation respectively. Furthermore, \"Sys\" and \"Dia\" denote systole and diastole states separately. Figure 6: The comparison between the Acc and Macc in three different models, respectively. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Skewness Features** & **Concepts** \\ \hline
1–2 & m\_skewness\_S1 & mean and standard deviation of the skewness of S1 \\  & sd\_skewness\_S1 & state \\ \hline
3–4 & m\_kurtosis\_S1 & mean and standard deviation of the kurtosis of S1 \\  & sd\_kurtosis\_S1 & state \\ \hline
5–6 & m\_skewness\_S2 & mean and standard deviation of the skewness of S2 \\  & sd\_skewness\_S2 & state \\ \hline
7–8 & m\_kurtosis\_S2 & mean and standard deviation of the kurtosis of S2 \\  & sd\_kurtosis\_S2 & state \\ \hline
9–10 & m\_skewness\_Sys & mean and standard deviation of the skewness of \\  & sd\_skewness\_Sys & systole state \\ \hline
11–12 & m\_kurtosis\_Sys & mean and standard deviation of the kurtosis of \\  & sd\_kurtosis\_Sys & systole state \\ \hline
13–14 & m\_skewness\_Dia & mean and standard deviation of the skewness of \\  & sd\_skewness\_Dia & diastole state \\ \hline
15–16 & m\_kurtosis\_Dia & mean and standard deviation of the kurtosis of \\  & sd\_kurtosis\_Dia & diastole state \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The summary of the skewness and kurtosis features extracted from four states.
# Evaluating MPTCP Congestion Control Algorithms: Implications for Streaming in Open Internet
### Congestion Control Algorithms in TCP Networks
#### 1.4.1 Single-Path TCP CC Algorithms  
One can find comprehensive surveys of TCP CC algorithms in, e.g., [27; 29]. Currently, several dozen TCP variants exist. They can be grouped as loss-sensitive, where the reduction in the transmission speed is induced by a segment drop, and time-sensitive, when the transmission slows down in response to a growing Round Trip Time (RTT). Generally, time-sensitive ones promise a smoother transmission but lose to the more aggressive loss-sensitive competitors in the case of aggravated congestion. The loss-sensitive ones show a tendency to exacerbate delay variation (jitter).  
For the experimental evaluation conducted in the latter part of this work, the most representative SPTCP variants have been chosen:  
* **Cubic** is the default CC algorithm in contemporary operating systems. It is designed for high-speed and long-distance networks. Cubic's window growth function is a third-degree polynomial, allowing it to perform a smoother transmission than Reno. As a result, a better utilization of available bandwidth, especially in the networks with a high bandwidth-delay product, is obtained [30; 31].
* **Reno** is one of the earliest CC algorithms. While it is currently unused, it can be recognized as a basis of many other CC algorithms, notably the MPTCP ones. Reno increases the transmission speed linearly and reduces it sharply when a loss is detected. Such aggressive changes cause numerous unwanted phenomena, like considerable jitter, session synchronization, and increased vulnerability to buffer bloat [32]. Those phenomena do not harm best-effort transmission significantly, but they highly perturb real-time streaming [17].
* **BBR** (Bottleneck bandwidth and round-trip propagation time) is a recent development from Google. Unlike the traditional CC algorithms that react to the packet loss or delay as signals of network congestion, BBR measures the actual bottleneck bandwidth and round-trip propagation time. This allows BBR to achieve higher throughput and reduced latency, making it particularly effective in today's Internet, where packet loss can occur for reasons other than congestion [28].
## 3 Design and Implementation  
### Manufacturing Digital Twin for Dedicated Equipment  
A digital twin used in this study was a system adopted when developing MDT4 DE (the Manufacturing Digital Twin for Dedicated Equipment). MDT4 DE is a software framework for Digital Twin developers and users. It is applied to a servo press, an electric water pump (EWP) quality inspection system, and others for welding and plastic machining instead of general-purpose equipment such as CNC, machining center tool (MCT), and NC [30; 31; 32; 33; 34]. After collecting data from various equipment, they are filtered and integrated according to user needs. They are then are used for the improvement of quality or manufacturing processes through analysis logic. The advantage of MDT4 DE is that it conforms to a standard such as MT-Connect and open platform communication unified architecture (OPC-UA). It can also normalize gathering data by collecting analog or raw digital data from old equipment in the field that does not support the standard. In other words, software design patterns that support an easy standardization of data from non-standard communication  
Figure 5: Manufacturing-process digital twin model. This figure shows the concept of a digital twin in a previous study [19].  
devices are advantages of MDT4 DE. Such an advantage of MDT4 DE can provide digital twin developers with software design patterns to support easy standardization of data from non-standard communication devices. Figure 6 shows the package diagram of the Manufacturing Digital Twin for Dedicated Equipment, which is comprised of CONFIG, HELPER, LOGGER, MODEL, IO, UI, DASHBOARD, ANALYZE and others. Specifically, the CONFIG package manages setup files while the HELPER package consists of a setup file management utility and others needed to implement programs. LOGGER manages logging data collected from the equipment. MODEL provides an interface to communicate with the database. The IO is needed as an interface for the manufacturing equipment on a shop floor through a protocol (e.g., OPC-UA, RS-232C, RS-485, CAN, Standard programmable logic controller (PLC), Non-Standard PLC, etc.). The UI is a user interface. DASHBOARD plays a gauge role, representing analysis data or their real-time changes. The SENSOR collects data from additionally installed sensors. ANALYZE conducts statistical analysis and support vector machine (SVM)-based analysis.
# Semiconductor Laser Linewidth Theory Revisited
## 12 Summary  
We have derived in a self-contained manner the spectral linewidth of edge-emitting multi-section semiconductor lasers starting from the time-dependent coupled-wave equations with a Langevin noise source. For this, we have expanded the forward and backward propagating fields into the longitudinal modes of the open cavity and have obtained very general expressions for the effective linewidth enhancement factor \(\alpha_{\text{H}}\) and the longitudinal excess factor of spontaneous emission (\(K\)-factor), including the effects of nonlinear gain (gain compression) and index (Kerr effect), gain dispersion, longitudinal spatial hole burning for multi-section cavity structures. We have shown that the general linewidth expression contains the effect of linewidth narrowing due to an external cavity by the chirp reduction factor \(F\). Finally, we have investigated the dependence of the population inversion factor on the carrier density and proposed a new analytical formula. Based on the derived expressions, the spectral linewidth as well as \(\alpha_{\text{H}}\), \(K\), and \(F\) have been calculated for a two-section DBR laser as functions of the injection current and the optical output power, taking into account the thermal detuning between gain and reflector sections. The mode jumps appearing with increasing current are accompanied by sudden rises of the spectral linewidth due to the rise of the modulus of \(\alpha_{\text{H}}\) and the drop of \(F\).  
**Funding:** This research was partially funded by the Federal Ministry of Education and Research (BMBF) under grant number 50 RK1972 and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy--The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689, grant AA2-13).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data can be received from the authors on request.  
**Acknowledgments:** The authors are indebted to [PERSON] for a critical reading of the manuscript.  
**Conflicts of Interest:** The author declares 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.
## 5 ICT Fluency and the Digital Divide
### Measuring Digital Inequality
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \(N=195\) & Gender & Age & Location & ClassYear & Major & PC,Owners & Educ,Att & Family\_Size & IncomePerCap EmplStatus \\ \hline Gender & 1 & 0.007 & \(-\)0.086 & 0.047 & 0.048 & \(-\)0.043 & \(-\)0.228 ** & 0.073 & \(-\)0.036 & 0.047 \\  & & (0.939) & (0.342) & (0.065) & (0.959) & (0.638) & (0.008) & (0.421) & (0.691) & (0.608) \\ Age & 0.007 & 1 & 0.193 * & -0.028 & 0.016 & 0.013 & 0.073 & 0.213 * & \(-\)0.139 * & 0.023 \\  & (0.939) & & (0.033) & (0.762) & (0.859) & (0.883) & (0.424) & (0.018) & (0.032) & (0.804) \\ Location & \(-\)0.086 & 0.193 * & 1 & \(-\)0.119 & 0.047 & 0.062 & \(-\)0.033 & 0.073 & \(-\)0.042 & \(-\)0.026 \\  & (0.342) & (0.033) & & (0.191) & (0.065) & (0.496) & (0.717) & (0.420) & (0.643) & (0.771) \\ ClassYear & 0.047 & \(-\)0.028 & \(-\)0.119 & 1 & 0.017 & 0.033 & 0.021 & 0.109 & \(-\)0.130 & \(-\)0.031 \\  & (0.640) & (0.724) & (0.191) & (0.851) & (0.717) & (0.817) & (0.230) & (0.153) & (0.731) \\ Major & 0.048 & 0.016 & 0.047 & 0.017 & 1 & \(-\)0.007 & \(-\)0.078 & 0.152 & 0.020 & 0.034 \\  & (0.597) & (0.859) & (0.605) & (0.851) & (0.936) & (0.392) & (0.093) & (0.827) & (0.760) \\ PC,Owners & \(-\)0.043 & 0.013 & 0.062 & 0.033 & \(-\)0.007 & 1 & 0.096 & \(-\)0.203* & \(-\)0.023 & \(-\)0.277 * \\  & (0.688) & (0.883) & (0.496) & (0.717) & (0.930) & (0.289) & (0.024) & (0.803) & (0.002) \\ Educ\_Att & \(-\)0.228 **+** & 0.073 & \(-\)0.033 & 0.021 & \(-\)0.078 & 0.096 & 1 & \(-\)0.184 * & \(-\)0.080 & 0.050 \\  & (0.008) & (0.424) & (0.717) & (0.817) & (0.392) & (0.289) & (0.042) & (0.381) & (0.581) \\ Family\_Size & 0.073 & 0.213 * & 0.073 & 0.109 & 0.152 & 0.203 * & 0.184 & 1 & 0.058 & \(-\)0.173 \\  & (0.421) & (0.018) & (0.420) & (0.220) & (0.230) & (0.024) & (0.042) & (0.528) & (0.056) \\ IncomePerCap & \(-\)0.036 & \(-\)0.193 * & \(-\)0.042 & \(-\)0.130 & 0.020 & \(-\)0.023 & \(-\)0.080 & 0.058 & 1 & \(-\)0.001 \\  & (0.691) & (0.032) & (0.643) & (0.153) & (0.827) & (0.803) & (0.381) & (0.528) & & (0.992) \\ EmplStatus & 0.047 & 0.023 & \(-\)0.026 & \(-\)0.031 & 0.044 & \(-\)0.277 * & 0.050 & \(-\)0.173 & \(-\)0.001 & 1 \\ PC,Owners & (0.648) & (0.804) & (0.771) & (0.731) & (0.706) & (0.002) & (0.581) & (0.056) & (0.992) & \\ \hline \hline \end{tabular}
* * Correlation is significant at the 0.05 level (2-tailed). **.** Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 5: Correlation between variables characteristics of Matrix M and W for the 3 rd year.
## 3 Nonuniform Background Elimination
### Definition of Structural Operators  
Based on the features of surveillance images described in Section 2, we can eliminate stray light nonuniform backgrounds by using the information of varying gray values between the target and background pixels. We define two structural operators, \(\Delta B\) and \(B_{b}\), to better utilize this difference, as shown in Figure 4a,b.  
In Figure 4, \(B_{o}\) and \(B_{i}\) are defined as the outer and inner structural operators of \(\Delta B\), respectively. \(\Delta B=B_{o}-B_{i}\) refers to the ring-like area between \(B_{o}\) and \(B_{i}\). \(B_{b}\) represents a uniform square area, whose size is bigger than \(B_{o}\), and \(K\), \(L\) and \(Q\) refer to the sizes of \(B_{i}\), \(B_{o}\) and \(B_{b}\), respectively, with \(K<L<Q\).  
For the structural operator \(\Delta B\), the coordinate of its center point \(O\) is \(\left(x_{0},y_{0}\right)\), \(x\in\left(-\frac{L}{2},\frac{L}{2}\right)\), \(y\in\left(-\frac{L}{2},\frac{L}{2}\right)\).  
\[\Delta B=f(x,y)=\begin{cases}1,&\sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0} \right)^{2}}>\frac{K}{2}\\ 0,&\sqrt{\left(x-x_{0}\right)^{2}+\left(y-y_{0}\right)^{2}}\leq\frac{K}{2} \end{cases} \tag{5}\]  
For the structural operator \(B_{b}\), \(x\in\left(-\frac{Q}{2},\frac{Q}{2}\right)\), \(y\in\left(-\frac{Q}{2},\frac{Q}{2}\right)\).  
\[B_{b}=g(x,y)=1 \tag{6}\]  
With the current algorithms, due to all the pixels covered in the filter being used for calculation, it is difficult to distinguish a complex background region from a target region with high accuracy. The structural operators that we define contribute to resolving it.
## 3 Use Case Demonstration Overview
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**City and Bus Type** & **Demonstration Month** & **Operational Scenario** & **Route** \\ \hline \multirow{3}{*}{BCN, 12 m *} & December & 25.7 km in 160 min & \\ \cline{2-4}  & & 16.7 km in 68 min & \\  & February & 13.3 km in 31 min & \\  & & 26.3 km in 126 min & \\ \hline BCN, 18 m * & June & 109.8 km in 558 min & \\ \hline \multirow{3}{*}{OSN, 12 m *} & March & 49.1 km in 243 min & \\  & March & 64.0 km in 310 min & \\  & & 63.2 km in 357 min & \\ \cline{2-4}  & May & 88.7 km in 473 min & \\ \hline OSN, 18 m * & April & 88.1 km in 252 min & \\ \hline \multirow{3}{*}{GOT, 12 m *} & May & 168.3 km in 784 min & \\  & & 99.5 km in 434 min & \\ \cline{1-1} \cline{2-4}  & October & 148.4 km in 575 min & \\ \hline \hline \end{tabular}  
* BCN: Barcelona, OSN: Osnabruck, GOT: Gothenburg; 12 m and 18 m refers to the bus length.  
\end{table} 
Table 2: Overview of the demonstration scenarios. \begin{table}
\begin{tabular}{c c c} \hline \hline
**City and Bus Type** & **Demonstration Profile Characteristics** & **Standard Profile Characteristics** \\ \hline \multirow{3}{*}{BCN, 12 m *} & Avg. vel. 9.65–26.2 km/h & \\  & Max. vel. 59.8–78.4 km/h & \\  & Max acc. 1.30–2.06 m/s\({}^{2}\) & Avg. vel. 9.52 km/h \\ \hline \multirow{3}{*}{BCN, 18 m *} & Avg. vel. 11.8 km/h & Max. vel. 29.8 km/h \\  & Max. vel. 72.0 km/h & Max. acc. 0.51 m/s\({}^{2}\) \\ \cline{1-1} \cline{2-4}  & Max acc. 2.36 m/s\({}^{2}\) & \\ \hline \multirow{3}{*}{OSN, 12 m *} & Avg. vel. 10.6–12.8 km/h & \\  & Max. vel. 43.2–59.0 km/h & \\ \cline{1-1} \cline{2-4}  & Max acc. 1.30–3.51 m/s\({}^{2}\) & Avg. vel. 19.8 km/h \\ \hline \multirow{3}{*}{OSN, 18 m *} & Avg. vel. 21.0 km/h & Max. vel. 61.9 km/h \\ \cline{1-1}  & Max. vel. 67.3 km/h & Max. acc. 1.06 m/s\({}^{2}\) \\ \cline{1-1} \cline{2-4}  & Max acc. 2.39 m/s\({}^{2}\) & \\ \hline \hline \end{tabular}  
* BCN: Barcelona, OSN: Osnabruck, GOT: Gothenburg; 12 m and 18 m refers to the bus length.  
\end{table} 
Table 3: Comparison between the demonstration and the standard driving profile characteristics.
## 2 Time-Lag Effect
### Seasonal Characteristics  
Firstly, the data of a typical day in spring and summer was selected, respectively. Then, the time-lag curve of temperature and temperature-induced strain was drawn, see Figure 3. It is obvious that the temperature and response strain curve possess typical seasonal characteristics. By comparing Figure 3a,b, it is observed that the hysteresis loop in spring, as shown in Figure 3a, is relatively more compact than that in summer, as shown in Figure 3b. It reflects that in spring the change in temperature is comparatively slower. Hence, the degree of strain response lagging temperature is also less than that in summer. It also gives us a new way to measure the extent of time-lag effect through the hysteresis loop area.  
Furthermore, to illustrate the time-lag effect as a common phenomenon of concrete box-girder bridges, this paper investigated three months of data. Additionally, the hysteresis loop area of temperature and strain data of every single day are plotted in Figure 4. From Figure 4, a time-lag phenomenon of different extent in almost every day indeed exists. Moreover, the hysteresis loop area in summer (July) is larger than that in winter (November).  
In summary, this part gives us a clear understanding of the time-lag phenomenon of temperature effects, a very common phenomenon in concrete box-girder bridges. Moreover, this phenomenon in the form curve annular feature can be directly reflected by the temperature vs. strain graph. Based on the analysis of data in different seasons, a seasonal characteristic was found.  
Figure 3: Plots of strain vs. temperature.
## 4 Improved Algorithms
### I-APF Algorithm  
According to the principles of the classic APF method, the algorithm has some shortcomings. When there is an obstacle at the starting point, the repulsive force causes the path to be tortuous. When the target point is near an obstacle, the obstacle makes it difficult for the object to reach the target point. When the gravitational force and the repulsive force on the object are equal, objects fall into local minima.  
In view of the local minima problem of the classic algorithm, some scholars have proposed the idea of using virtual sub-targets [24] to make the robot escape from the local minimum. However, the location of the virtual sub-target is random, which inevitably leads to the blindness of the algorithm; that is, the robot deviates from the target or enters an obstacle area. This paper proposes a heuristic method for deviating from local minima based on the number of adjacent obstacles. The method includes three steps. Step 1: Make a judgement on the local minimum. Step 2: If the object is stuck, take the local minimum as the center, draw a circle with twice the step length in the RRT algorithm as the radius, record the number of obstacles in the circle as O, and record the total number of obstacles in the space as S. Step 3: Introduce a heuristic function to calculate the new potential field force. The specific process is as follows.  
When the object falls into a local minimum, the gravitational field and repulsive force field received by the robotic arm are equal in size but in opposite directions. By judging the size and direction of the gravitational field and repulsive force field of the robotic arm, whether the robotic arm falls into a local minimum can be evaluated. When the robotic arm falls into a local minimum, the number of nearby obstacles is obtained by using twice the step length in the RRT algorithm as the radius, and then a heuristic function is established to calculate the new force field. The heuristic function F is as follows:  
\[\text{F }=\ \alpha(\text{O}/\text{S})\text{M}+\beta(1-\text{O}/\text{S}) \text{N} \tag{7}\]  
where \(\alpha\) and \(\beta\) are the repulsion scale factor and the gravitational scale factor, respectively, O is the number of obstacles, S is the total number of obstacles in space, M represents the repulsive force of the obstacles in the circle on the robotic arm, and N represents the gravitational force of the target point on the robotic arm. According to the heuristic function, the more obstacles there are in the circle, the greater the repulsive force of the robot arm will be, and the path will expand in the direction deviating from the obstacle to the target point, which is beneficial for the robot arm to quickly avoid the obstacle. Figure 5 shows a path planning diagram of the I-APF method.
## Article
### LMIs-Based LPV Control of Quadrotor with Time-Varying Payload
To address the chattering issue, the saturation function was used in place of the signum function in the control law. Changes in MOI, mass flow rate, and actuator limitation were not taken into consideration. In [20], an adaptive SMC control algorithm was suggested for the quadcopter's altitude tracking in the presence of time-varying payload and ground effect. Position control, MOI variations with mass, and mass flow rate were ignored. In the LPV control algorithms described in the literature, time-varying mass and large tilt angles have not been considered simultaneously, whereas, in the nonlinear adaptive control schemes suggested for a quadrotor to handle time-varying mass, the controller gains will take time to adapt to the new values. In addition, the computation and execution of gains are online, which will increase the computational burden compared to the LPV approaches, where the gains are computed offline but executed online. The motivation of this paper is to suggest an LMIs-based LPV control scheme for the quadrotor's position control in the presence of time-varying mass, time-varying inertia, mass flow rate, large tilt angles, noise, and external disturbances without actuator saturation.
# IoT's Tiny Steps towards 5G: Telco's Perspective
## 4 IoT in 5G Performance Requirements
### Reliability  
**Reliability** is the maximum tolerable packet loss rate at the application layer [105]. 5G must bring a reliability of 99,999% [27; 30; 31; 212], or higher for specific use cases [212], (e.g., tele-protection in a smart grid network [207] or driverless cars [106]). Reliability is the main characteristic of uMTC monitoring and managing/controlling activities. Reliability will be a particularly challenging task in high-speed trains because of speed, load, and cell distance [16; 107].