esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Heart Sounds Using Convolutional Neural Network
## 2 Materials and Methods
### Heart Sound Classification Based on the Designed CNN  
#### 2.4.1 Model Structure  
The conventional CNN [38] typically alternates between two different types of layers--convolution and pooling. The last stage usually consists of at least one fully connected layer and a classification layer with the sigmoid function (for binary classification) [39] or the softmax function (for multi-class classification). For images or temporal series, convolution layers preserve the spatial information by using local receptive fields. The strategy of sharing the same weights can be used to learn a set of position-independent latent features. Pooling layers are used to reduce the dimensions of the feature maps generated by the convolution layers.  
As shown in Figure 2, the proposed model is composed of three Conv-blocks, a global average pooling (GAP) layer and a classification layer with the sigmoid function. This classification step was completed by Keras one of deep learning architectures with python 3.6. Each Conv-block includes a convolutional1D (Conv-1D) layer and a maxpooling1D layer, followed by the strategy of dropout to prevent overfitting [40]. The numbers of filters for the Conv1D layers were set to 32, 64 and 128, respectively and each filter had a kernel size of 3 and strides 3. The pooling size was 2 with two strides 2. Relu was adopted as the activation function in each convolution layer. We tested the dropout rate from 0.2 to 0.5 and found that the model achieved the best performance when the dropout rate was 0.25. Adam, with a learning rate of 0.001, \(\beta_{1}(beta\_I)=0.9\) and \(\beta_{2}(beta\_I)=0.999\) and binary cross-entropy were used as the optimizer function and the objective function, respectively.  
In the proposed classification method depicted in Figure 2, as the input of the model comprises all of the extracted features, we assume that the designed model is used only to select features and classify normal and abnormal heart sounds. Therefore, the designed model is a shallow or compact model. we performed valid convolution operations on the convolution layers to explore the relationship between the different features described in Section 2.3. We also adopted pooling layers to reduce the feature dimensions. So, the dimensions of the input features gradually decreased from the bottom layers to the top layers of the Conv-blocks. This procedure reorganizes and reduces the dimensionality of the input (497 features in total), which can also be viewed as feature selection in signal processing.  
The feature maps generated from the last Conv1D layer are fed into the global average pooling (GAP) layer, the principle of which is introduced in the following subsection. Each feature map outputs a value named an eigenvalue. Then, all of these eigenvalues make up the eigenvector shown in Figure 2. Finally, the eigenvector is fed into the classification layer with one neural and one sigmoid activation function to output a predicted probability.
# A Brief Review of Multipath TCP for Vehicular Networks
## 3 Congestion Control and Scheduler
### Congestion Control
#### 3.1.3 Lesson Learned
First, the machine learning-based approach requires some time to train the model. Second, more efforts need to be taken on the training accuracy. In a complex scenario, it is challenging to accurately perceive the environment and therefore the information acquired at each agent is inaccurate. The machine learning process should fully address these inaccuracies in the input, process, and output steps of the training. Third, TCP-friendliness is not considered adequately in some learning-based approaches. Some issues should be further discussed in future work. First, AI-based congestion control algorithms need to be tested under real-world network scenarios to verify their effectiveness. Second, AI-based schedulers need to be further investigated to improve the efficiency of multipath congestion control by jointly utilizing a pre-trained model or proper mechanisms according to the scenarios. Third, it is also a promising approach to consider combining different congestion control methods that complement each other. Third, TCP-friendliness issues should be discussed more carefully in learning-based congestion control algorithms. Last but not the least, it is also important to discuss the energy efficiency problem of congestion control algorithms. We will introduce some energy efficient MPTCP variants in the next section.
## 4 Parameter Effects on VFA-SF and VFA-SF-OPT  
As for the initial simulation parameters, the number of sensor nodes \(N=500\) was chosen in the target region. The mass of each sensor is normalized to \(m=1\) without considering the hardware design and the mechanical parts of the sensor node. Sensing range is also normalized to \(R_{s}=1\). In such a case, the final coverage of the whole wireless sensor network relies on the real sensing range of the given sensors. Communication range \(R_{c}\) is also normalized based on the \(R_{s}\). In order to calculate spring forces from the closest neighbor nodes only, we set \(R_{c}=3R_{s}=3\) as shielding length. We then set spring coefficient \(\kappa=15\) and \(F_{centri}=0.005\), which correspond to the equilibrium distance \(D_{m}=\sqrt{3}\), \(\gamma=\sqrt{4\kappa m}=7.75\).  
Moreover, based on these parameters, the theoretical external force, which always points to the center, is 2.873 (see Figure 6 in the reference [13]). This force can break the distribution balance during the deployment process. In our previous paper, external force \(F_{extern}=3.5\) was used to successfully eliminate coverage holes without affecting network formation.  
The detailed information of simulation parameters and algorithm process can be found in our recent paper [13]. In this section, in order to further improve our VFA-SF and VFA-SF-OPT algorithms for the real field test in WSN applications, the parameter effects on these two algorithms are carefully discussed. Table 1 gives the details of the initial parameters in the following experiments.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 5 Experimental Results and Discussion
### Congestion Control Under Dynamic Bandwidth
#### 5.2.1 Flow Completion Time  
We evaluate the completion time of flows with different priorities. The priority of Flow\({}_{j}\), requested by Consumer\({}_{j}\), is denoted as \(p_{j}\). The relative priorities are set such that \(p_{1}=p_{2}<p_{3}<p_{4}\). Consumer 2 sends 3000 Interest packets, while other consumers each send 5000 Interest packets. Each consumer sends Interests at a rate of 1000 pkts/s. Each Interest brings back a Data packet with a total length of 1069 bytes. In this scenario, Router 3 and Router 2 process Data packets exceeding the bandwidth of their forwarding links. We compare the performance of CCLF with PCON, and the results are shown in Figure 15.  
The results indicate that, when using the PCON method, the flow completion time is only related to the content size. The FCT of Flow 1, Flow 3, and Flow 4, which all transmit the same amount of data, exceeds 21 s each. However, using the CCLF method, the FCT of Flow 3 and Flow 4 with high priority is reduced to 10.18 s and 5.08 s, respectively. Notably, using the PCON method, the average FCT of the four flows is 20.67 s, while using the CCLF method, it is reduced to 12.06 s, representing a 41% reduction compared to PCON.  
This is because the CCLF method prioritizes the forwarding of high-priority Data packets. However, PCON does not distinguish data priority, and the limited bandwidth is shared among all flows. By employing the CCLF method, Data packets are scheduled for  
Figure 14: The simulated network with dynamic bandwidth.  
Figure 15: The flow completion time in the network with dynamic bandwidth.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Time Interval** & **Bandwidth of Link 1** & **Bandwidth of Link 2** \\ \hline
0–6 s & 10 Mbps & 10 Mbps \\
6–10 s & 6 Mbps & 14 Mbps \\
10–14 s & 14 Mbps & 6 Mbps \\
14–25 s & 6 Mbps & 14 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters for dynamic bandwidth settings.  
forwarding while maintaining full bandwidth occupancy on congested links. This reduces the average flow completion time.
## 5 Optimization and Scheduling Model of the Complementary System
### Power Generation and Environmental Benefits
#### 5.2.1 System Construction Cost  
Since the wind turbines, PV panels, and cascade small hydropower stations have already been constructed, the construction cost of the system includes only the cost of pumped storage retrofitting and operation and maintenance expenses. The total life-cycle cost of the complementary system is calculated as follows:  
\[C_{lcc}=C_{inv}+C_{om}\frac{(1+r)^{T}-1}{(1+r)^{T}r} \tag{15}\]  
where \(C_{lcc}\) is the total life-cycle cost of the system, CNY; \(C_{inv}\) is the cost of the pumped storage retrofitting, CNY; \(C_{om}\) is the operation and maintenance cost of the system, CNY; \(r\) is the discount rate; \(T\) is the system life cycle, year.
# Image Dehazing and Enhancement Using Principal Component Analysis and Modified Haze Features
## 1 Introduction
However, since this method selects the image patch including the candidate pixel of atmospheric light at every pixel, it provides the resulting image at a high-computational cost. [PERSON] et al. extracted the sky region and estimated transmission using color correction in the HSV color space [12]. [PERSON] et al. improved surveillance monitoring images using a DCP-based maximum filter to remove the haze components [13]. [PERSON] et al. also performed haze removal using the disparity map which was estimated between a set of stereo images [14]. To solve this problem, this paper presents a novel estimation method for the optimal atmospheric light and the transmission. The contribution of the proposed method is two-fold: (i) estimation of atmospheric light vectors using the reduced pixels based on the PCA, and (ii) estimation of the transmission component using random forest via modified haze-relevant features. Since the atmospheric light is estimated by constraining the searching region and clustering the similar pixels to reduce the candidate pixels, the computational complexity is significantly reduced. In addition, the proposed method estimates the transmission using single-scale haze-relevant features to simplify the random forest. In conclusion, the proposed method can provide similar results at a low computational cost. This paper is organized as follows. Section 2 describes the modified haze removal model as the theoretical background. Section 3 presents the proposed haze removal method using optimal atmospheric light and transmission in detail. Experimental results are shown in Section 4, and Section 5 concludes the paper.
## 3 Modeling the Temporary Facility Layout Problem
### ANP-Based Modeling for Layout Planning  
As a comprehensive decision-making approach, ANP is capable to integrate all relevant factors and criteria in decision problems [58; 59]. ANP, which is an extension of AHP, allows for more complex interdependent relationships between elements involved in the decision problem. In AHP models, it is assumed that the elements of different decision levels along the hierarchy have unidirectional  
Figure 1: Overview flowchart depicting model development.  
relationships between them. Within each cluster and between different clusters, the elements are not correlated with each other. Therefore, in the mathematical problems in which elements are correlated, AHP is not able to determine the weight of each criterion.  
ANP provides planners and decision-makers with a more generalized model considering the interdependencies between higher-level and lower-level elements. In temporary facility layout planning, ANP is suitable to select the most important criteria for construction projects. Firstly, in layout planning problems, some criteria, such as safety criterion and the ability to dynamically change temporary facilities, are difficult to be quantified. In the ANP approach, the factors, which are incorporated into the evaluation model, can be either quantifiable or nonquantifiable. Secondly, when comparing the criteria for weight assessment, the decision-makers are able to check the consistency by calculating the consistency ratio, which is critical in decision problems involving opposing influences on the objective. Furthermore, pairwise comparisons are conducted in ANP by engineers, decision-makers and planners to utilize their knowledge and experiences in construction layout planning. This can be practical to generate the optimal layout planning solution for a construction project. Therefore, according to the research objective and the features of the ANP method, it is found that ANP is a suitable approach to prioritizing among the temporary facility layout planning criteria.  
The basic research step of the ANP-based decision-making process are as follows.  
1. Development of ANP network, which defines the influential relationship between the clusters and elements.
2. Pairwise comparisons by the experts and decision-makers based on the previous step. The data input process is completed during this step, which formulates the pairwise comparison matrix.
3. Consistency check. Through the consistency check, the consistency ratio (CR) is calculated.
4. Formation of unweighted supermatrix, weighted supermatrix and the limit matrix.
5. Based on the ANP decision-making process, the detailed model development and calculation process is explained in the following sections [59; 60; 61].
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 3 Results
### Selenium Removal  
Selenium removal proved to be relatively straightforward, with generally high efficiencies across all the experiments (56-100%). The lower efficiencies were typically associated with shorter operation times, lower current strengths, or the absence of pH adjustment. On the other hand, complete Se removal was achieved with higher current strengths and longer treatment times. In the obtained model, four out of the five tested factors proved to be significant, with \(p\)-values less than 0.0001. The only insignificant factor was the number of electrodes. Significant interaction terms also emerged in the model, particularly those  
Figure 4: (**a**) A comparison of predicted and observed removal efficiencies for Se removal. (**b**) A comparison of predicted and actual values of Sr removal efficiencies.  
that involved the applied current and treatment time, applied current and electrode material, and treatment time and electrode material, all with \(p\)-values less than 0.0001. These interactions highlight the complex dynamics between these variables in influencing the efficiency of selenium removal. Figure 5 includes the three-dimensional response surface plots for each electrode material, illustrating the effects of the applied current and treatment time when the number of electrodes and pH ranges were averaged over.  
Both electrode materials demonstrated better efficiencies when subjected to higher currents and/or extended treatment times, irrespective of the pH conditions. A notable difference in the performance between the two materials was observed when the treatment time was short; under these conditions, the Fe electrodes were more effective at lower current strengths compared with the Al electrodes. However, as illustrated in Figure 6, with the longest treatment, both materials behaved similarly regardless of the applied current.  
Figure 5: RSM plots showing interactions of factors A (applied current) and C (treatment time) for Se for (**a**) Al electrodes and (**b**) Fe electrodes.  
Figure 6: Interaction of factors A (applied current) and D (electrode material) for Se when the treatment time was (**a**) 5 min and (**b**) 25 min. The red line represents Al electrodes, green line Fe electrodes.  
While the influence of pH was less pronounced in the removal of selenium compared with strontium, adjustments to the pH still had a noticeable impact, particularly at the maximum pH range. This was especially true for Al electrodes during shorter treatment times, where an increase in the pH range notably improved the performance. Figure 7 depicts that with longer operating times, the pH range had a minimal effect on the removal efficiency.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 4 Simulation and Numerical Results  
In this section, time delay and joint decision are simulated for signals with modulation parameters \(K=5,N=60\) and \(f_{c}=455\) KHZ. Table 1 gives the estimated delays for using the LMSTDE scheme with a delay of 1 ms. Table 2 gives an estimate of the multipath delay by using an autocorrelation scheme. The comparison results of bandwidth between modulation parameter schemes and spread spectrum schemes are given in Table 3. In addition, the normalized symbol decision method is compared with the the current mainstream method by taking the signal-to-noise ratio and the sub-path gain as variables.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 5 Results
### Estimating the Position of Pedestrians  
In this section of the paper, we present the testing of the proposed algorithm for the estimation of the pedestrians' coordinates. The input data used in this scenario are sample real-world data from the campus of the Technical University of Kosice. The position of a pedestrian in the image was \(x=627\) and \(y=828\), both in pixels, see Figure 8.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **True Positive** & **False Positive** & **False Negative** & **Processing Time** \\ \hline YOLO 2496 \(\times\) 2496 & 293 & 0 & 30 & 29.45 s \\ \hline YOLO 1248 \(\times\) 1248 & 261 & 0 & 62 & 8.73 s \\ \hline YOLO 608 \(\times\) 608 & 139 & 1 & 184 & 2.68 s \\ \hline YOLO 416 \(\times\) 416 & 71 & 2 & 252 & 1.66 s \\ \hline Mask R-CNN & 115 & 1 & 208 & 5.68 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pedestrian detection from a low distance. Resolution 1920 \(\times\) 1080. Number of pedestrians 323.  
Figure 8: Estimating the position of a pedestrian—highlighted in the yellow square.  
Figure 7: Sample of pedestrian detection from a low distance (**left**) and high distance (**right**).  
Thus, \(dx\) offset to the center of the image is:  
\[dx=x-\frac{h}{2}=-333 px \tag{11}\]  
where \(h\) is the height of the image, in this case, 1920 pixels. Using Formulas (2) and (3), we get:  
\[O_{hor}=-15.60937^{\circ} \tag{12}\]  
\[O_{ver}=-15.73330^{\circ} \tag{13}\]  
Results of \(G_{hor}\) (Equation (4)) and \(G_{ver}\) (Equation (5)) are then:  
\[G_{hor}=O_{hor}+C_{hor}=59.39062^{\circ} \tag{14}\]  
\[G_{ver}=O_{ver}+C_{ver}=-63.43133^{\circ} \tag{15}\]  
where \(C_{hor}\) is the horizontal degree of \(75^{\circ}\) and \(C_{ver}\) is the vertical degree of \(-47.698^{\circ}\), both as received from UAV.  
Calculation of the flat distance between the UAV and a detected person follows the Formula (6):  
\[flatDist=2\times|alt\times tan(rad(90-G_{ver}))|=29.00458\;\mathrm{m} \tag{16}\]  
where \(alt\) is the altitude from UAV and is equal to 29 m. Note that this is not the result of \(flatDist\). Finally, we can obtain geographic coordinates following Formula (7):  
\[geoDist=\frac{flatDist}{kpd\times 1000}=0.00026^{\circ} \tag{17}\]  
The geographic directional vector \(P\) (Equations (9) and (10)) is then:  
\[Px=0.00013 \tag{18}\]  
\[Py=0.00022 \tag{19}\]  
Knowing the \(Px\) and the geographic distance in latitude and longitude we obtain:  
\[Lat=48.73124+Px=48.73138^{\circ} \tag{20}\]  
\[Lon=21.24482+Py=21.24504^{\circ} \tag{21}\]  
The input values of latitude and longitude are provided by the UAV.  
The final estimated position is depicted in Figure 9, on the left, and the real position of the person is on the right. The overall deviation to real value is up to 2 m. Analogous deviations were reported for another testing of the algorithm. Deviation in the final results is due to inaccurate data from the UAV (angle of camera and altitude).  
Figure 9: The estimation position of the pedestrian (**left**). The real position of the pedestrian (**right**).
## 2 Materials and Methods
### Sensory-Based Attributes
#### 2.10.2 GC-MS Analysis of Volatile Organic Compounds (VOCs)  
VOC analysis was performed by Headspace Solid-Phase Microextraction (HS SPME) of the oils and analysis by gas chromatography-mass spectrometry (GC-MS) as described by [PERSON] et al. [26]. Briefly, 2 mL of oil sample was put into a 15 mL glass vial, closed with a PTFE/silicone septum, and left in a PAL SHIMADZU autosampler unit (AOC 6000, CTC Analytics, Zwingen, Switzerland) at 55 \(\pm\) 0.1 \({}^{\circ}\)C for 15 min, with agitation at 250 rpm. After their equilibrium, a PAL SPME fiber (DVB/PDMS/CAR WR/PDMS divinylbenzene/polymethylsiloxane/carbon wide-range/polymethylsiloxane Dual-Phase 80 \(\upmu\)m, 50/30 thickness, Sigma-Aldrich, St. Louis, MO, USA) was introduced to the headspace for 50 min at 55 \({}^{\circ}\)C to absorb the volatiles. The fiber was pre- and post-conditioned for 10 min at 260 \({}^{\circ}\)C, according to the manufacturer's instructions.  
VOCs were analyzed using a Shimadzu GC-MSQP2020 (Shimadzu, Kyoto, Japan) instrument with a MEGA-5 MS capillary column (30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m; Legano, Italy). Injection was operated in split-mode (1:2) at 260 \({}^{\circ}\)C for 4 min, He was the carrier gas at a flow rate of 1.2 mL/min, the column was held for 2.5 min at 40 \({}^{\circ}\)C, then programmed to 230 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and held for 5 min. The mass detector was operated in the electron impact mode and scanned at m/z 35-450 amu range.  
Compound identification was carried out by comparing the mass spectra of eluting compounds with those of the commercial library NIST17, considering the compounds with a score of \(\geq\)80% in similarity checks. The semi-quantitative results are expressed as the following: relative peak area (%) = (peak area of individual VOC/total peak area of identified VOCs) \(\times\) 100.
## 3 Case Study: Structural Factor Analysis for Public Transportation Bus  
This section presents the case study of analyzing the structural factors that cause the failure in the public transportation bus domain. A public transportation bus is an integrated complex structure that includes five major systems, i.e., powertrain system, chassis system, power system, electrical system, and body system [55]. Each major system contains numerous sub-systems and components. Any safety risk occurring in any system may cause the entire bus unit to malfunction. To ensure the analysis results are realistic and practical, this study utilizes maintenance records collected from depots and seeks consultation from professional staffs at the bus enterprise. These maintenance records and expert insights serves as valuable information sources, contributing to the credibility and relevance of the analysis.  
Based on the combination of data exploration, domain knowledge, and expert confirmation, this study successfully extracts valuable information from the maintenance records and defines an initial set of failure-causing factors. Due to the initial factor set being relatively large and unsuitable for further calculation, FTA and IVPF-AHP are utilized to assess and rank the factors. The top thirty factors are considered the more critical ones. IVPF-DEMATEL constructs the causal diagram to represent the cause-effect relationships between the critical factors. The results of the analysis can contribute to a better understanding of the relationships between safety factors and provide suggestions for optimizing maintenance managements.  
To ensure the efficiency of data processing and analysis, this study utilizes a MacBook equipped with a 2 GHz quad-core Intel Core i5 processor, 32 GB of unified memory, and a 1 TB SSD (Apple, Cupertino, CA, USA). All data-processing and analysis scripts are implemented in a Python 3.10.4 environment.