esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Speaker Identification Related Work
### Classifiers  
Algorithms such as Gaussian Mixture Models (GMM) are generally employed for speaker recognition [34] and Support Vector Machines (SVM) are also widely applied [35; 36]. Other studiessuggest the use of Deep Neural Networks (DNN) for speaker recognition [37; 38]. However in this research, we aim to keep a balance between computational complexity and accuracy due to the hardware constraints of the device, where the battery consumption is critical and the coarse amount of training data originally available. After some preliminary tests to compare GMM, SVM and Multi-Layer Perceptron (MLP) classifiers for our task, the later--a precursor of Deep Neutral Networks with only one hidden layer--was chosen due to its simplicity, speed and better performance.
## 2 Literature Review  
A number of studies shown that feature selection improves the generalization capabilities of the classification models [6; 8; 9; 16; 18; 20]. Feature selection is similar to dimensionality reduction whereas the objective of the former is to retain the semantics present in the original dataset while the latter transforms the data in such a manner as the overall dimensions of the data are reduced [21]. Feature selection techniques are generally grouped into three broad categories, i.e., filter-techniques, wrapper-techniques, and embedded-techniques, as mentioned in the preceding section. Filter techniques score features based on the general characteristics of the dataset [18]. In this regard, most of the filter approaches are based on evaluating the correlation between features and the class label. Hence, features having a high correlation with the target concept are regarded as useful features. Feature ranking approaches are generally based on filter methods, but ranking can also be produced by employing a classification model which in turn evaluates a subset of features [16]. Wrapper approaches involve classification algorithms in the process of evaluating a subset of features. In this regard, a feature subset generation step is followed by an evaluation step [19]. The main objective of the wrapper approach is to find a subset of features that are neither irrelevant nor redundant. Filter methods are generally employed when the number of features is very large as these methods are computationally fast and do not get bogged down in a pairwise comparison of the candidate feature sets. Wrapper methods generally produce results that are relatively more optimized and accurate than that of the filter methods whereas the latter produces the result in relatively less time [18]. Embedded methods select a subset of features as an integral part of the process of building a classifier such as a decision tree algorithm that selects the most appropriate feature as it grows the tree [30]. A high-level summary (adapted from [31]) regarding the merits and demerits of feature selection techniques is given in Table 1.  
This research focuses on the application feature selection and classification for the CKD diagnosis problem. In this section, we discuss some of the representative works in which feature selection techniques are used for the CKD diagnosis. [PERSON] and [PERSON] [11] proposed a wrapper based feature selection approach which reduces the overfitting of the Random Forest (RF) classification model on the CKD dataset. The reported resultant F1-measure of the model on top 5 features is 99.80%. Furthermore, the authors also reported promising results in terms of reduced cost. [PERSON] et al.
###### Abstract  
(1) Background: The aim of this study was to investigate the validity and reliability of surface electromyography (EMG) for automatic detection of the aerobic and anaerobic thresholds during an incremental continuous cycling test using 1 min exercise periods in elite cyclists. (2) Methods: Sixteen well-trained cyclists completed an incremental exercise test (25 \(W/1\) min) to exhaustion. Surface bipolar EMG signals were recorded from the vastus lateralis, vastus medialis, biceps femoris, and gluteus maximus, and the root mean square (RMS) were assessed. The multi-segment linear regression method was used to calculate the first and second EMG thresholds (EMG\({}_{\text{T1}}\) and EMG\({}_{\text{T2}}\)). During the test, gas exchange data were collected to determine the first and second ventilatory thresholds (VT\({}_{\text{1}}\) and VT\({}_{\text{2}}\)). (3) Results: Two breakpoints (thresholds) were identified in the RMS EMG vs. time curve for all muscles in 75% of participants. The two breakpoints, EMG\({}_{\text{T1}}\) and EMG\({}_{\text{T2}}\), were detected at around 70%-80% and 90%-95% of VO\({}_{\text{2 MAX}}\), respectively. No significant differences were found between the means of VT\({}_{\text{1}}\) and EMG\({}_{\text{T1}}\) for the vastii and biceps femoris muscles (\(p>0.05\)). There were no significant differences between means of EMG\({}_{\text{T2}}\) and VT\({}_{\text{2}}\) (\(p>0.05\)). (4) Conclusions: It is concluded that the multi-segment linear regression algorithm is a valid non-invasive method for analyzing the aerobic-anaerobic transition during incremental tests with 1 min stage durations.  
cycling; neuromuscular fatigue; surface electromyography; Incremental cycling ergometer test
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 4 Proposed GCRN-Based Surround AEC
### Model Architecture  
In this paper, the GCRN model was used to recover the near-end speech, which is mainly comprised of three components, namely one convolutional encoder, LSTM modules, and two decoders for both real and imaginary part reconstruction. The encoder consists of five convolutional-gated linear units (Conv-GLUs) layers [27], which extract the high-level dimension feature patterns from the inputs, while the decoder serves as the mirror version of the encoder to gradually recover the original size [28]. Each decoder has five deconvolutional-gated linear units (Deconv-GLUs). Each convolution or deconvolution layer is successively followed by a batch normalization (BN) [29] and an exponential linear unit (ELU) [30] activation function. Between the encoder and the decoder, two LSTMs are stacked to effectively establish the sequential correlation between adjacent frames. Additionally, the skip connection [31] was also adopted to concatenate the output of each encoder layer to the input of the corresponding decoder layer, which effectively mitigates the information loss. One linear layer is stacked in the end of each decoder to obtain the real or imaginary estimation.  
The detailed parameter setup of the GCRN architecture is presented in Table 1. The \(featureMaps\times timeSteps\times frequencyChannels\) format was used to specify the input size and output size of each layer. The (kernelSize, strides, outChannels) format was adopted to represent the hyperparameters of each layer. The number of input feature maps in each decoder layer is doubled because of the skip connections.
## 3 Results
### Reflectance Values at a Wavelength of 590-720 nm  
Table 7 presents selected descriptive statistics of the reflectance at a wavelength of 590-720 nm. In the assessment of differences in reflectance values at subsequent time points (before oil application, after 20 min, and after 60 min), there was no statistically significant difference between particular time points for the reflectance at a wavelength of 590-720 nm after the application of Oil A (\(p\) = 0.6269), Oil B (\(p\) = 0.2495), Oil C (\(p\) = 0.6654), or Oil D (\(p\) = 0.4068). The results of multiple tests (Table 8) confirm the absence of differences at all the time points of reflectance assessment at a wavelength of 590-720 nm after the application of each tested oil. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Oil A: Conover–Iman post hoc test; \(p\) =**} \\ \hline
**Intertemporal comparison** & **A\_0** & **A\_20\_min** & **A\_60\_min** & **A\_120\_min** \\ \hline
**A\_0** & & 0.1078 & 0.8024 & 0.7075 \\ \hline
**A\_20\_min** & 0.1078 & 0.0645 & 0.0511 \\ \hline
**A\_60\_min** & 0.8024 & 0.0645 & 0.9004 \\ \hline
**A\_120\_min** & 0.7075 & 0.0511 & 0.9004 \\ \hline
**Oil B: Conover–Iman post hoc test; \(p\) =** & & & \\ \hline
**Intertemporal comparison** & **B\_0** & **B\_20\_min** & **B\_60\_min** & **B\_120\_min** \\ \hline
**B\_0** & & 0.0432 & 0.5957 & 0.1305 \\ \hline
**B\_20\_min** & 0.0432 & 0.0118 & 0.0007 \\ \hline
**B\_60\_min** & 0.5957 & 0.0118 & 0.3213 \\ \hline
**B\_120\_min** & 0.1305 & 0.0007 & 0.3213 \\ \hline
**Oil C: Conover–Iman post hoc test; \(p\) =** & & & \\ \hline
**Intertemporal comparison** & **C\_0** & **C\_20\_min** & **C\_60\_min** & **C\_120\_min** \\ \hline
**C\_0** & & 0.4519 & 0.6155 & 0.0825 \\ \hline
**C\_20\_min** & 0.4519 & 0.2119 & 0.0144 \\ \hline
**C\_60\_min** & 0.6155 & 0.2119 & 0.2119 \\ \hline
**C\_120\_min** & 0.0825 & 0.0144 & 0.2119 \\ \hline \hline \multicolumn{4}{c}{**Oil D: Conover–Iman post hoc test; \(p\) =**} \\ \hline
**Intertemporal comparison** & **D\_0** & **D\_20\_min** & **D\_60\_min** & **D\_120\_min** \\ \hline
**D\_0** & & 0.7162 & 0.5450 & 0.1494 \\ \hline
**D\_20\_min** & 0.7162 & 0.8085 & 0.2777 \\ \hline
**D\_60\_min** & 0.5450 & 0.8085 & 0.3976 \\ \hline
**D\_120\_min** & 0.1494 & 0.2777 & 0.3976 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of post hoc tests of the reflectance at a wavelength of 480–600 nm after the application of Oils A, B, C, and D.
## 3 E2E Approach
### CNNs and LSTMs
The pooling layers summarize the most important features, the SE module enhances feature importance, and the inception module captures multi-scale data. The authors argue that these additions make their CNN architecture more discriminative, achieving an accuracy of 95.1%. In many other studies, CNNs have been employed for PD classification [3; 26; 30; 39; 40; 41; 42; 43; 44]. In [42], [PERSON] et al. report an accuracy of 91%. [PERSON] et al. [30] implement both a traditional and E2E pipeline approach, leading to an accuracy of 67.93% for the former and 68.56% for the latter. Although the E2E approach is able to outperform the more traditional approach, the performance is considerably lower than that seen in other studies. In [39], an Acoustic Deep Neural Network (ADNN), Acoustic Deep Recurrent Neural Network (ADRNN), and Acoustic Deep Convolutional Neural Network (ADCNN) are distinguished. The authors demonstrate that the CNN architecture, ADCNN, slightly outperforms the more regular DNN and RNN (99.92% versus 98.96% and 99.88%, respectively). In [40], the authors also compare a CNN and LSTM classifier using MFCCs and Gammatone Cepstral Coefficients (GTCCs) on several subsets of the PC-GITA [36] and the Parkinson's Disease Classification [45] datasets. The experiments demonstrate that the CNN significantly outperforms the LSTM classifier, and the GTCCs provide greater accuracy compared to the MFCC, reaching an accuracy of 100%, which implies none of the PWP and HC are misclassified. In [26], the authors use both CNN and Transformer (see Section 3.3 for more details) architectures. In the case of CNNs, they use MobileNetV2, DenseNet201, DenseNet169, DenseNet121, ResNet152, ResNet50, GoogleNet, VGG19, and VGG16. The lowest accuracies are reported for the MobileNetV2 and VGG19, which led to an accuracy of 95.61%, and the highest accuracy is reported for ResNet152, for which 98.08% accuracy is reported. In this study, the authors use the Synthetic vVowels of Speakers with PD and Parkinsonism dataset [46], about which more information can be found in Table 11.  
Finally, other studies on the performance of CNN-based auto-encoders have demonstrated varying levels of accuracy across different databases, privacy-aware methods, and noisy environments. [PERSON] et al. [43] report an accuracy of 61.49% on the MVDR-KCL database following a Federated Learning (FL) method; whereas [PERSON] et al. [44] observe a higher accuracy of 75.4% on the PC-GITA dataset [36] using speaker identity invariant representations and adversarial training. In [PERSON] et al. [41], it is pointed out that CNNs can still reach an accuracy ranging from 92% to 96% in noisy environments on a custom private small dataset of 27 participants. In [PERSON] et al. [3], the authors demonstrate that adding noise can even improve the accuracy with which PD can be classified on the PC-GITA dataset.
# Review of Indoor Positioning: Radio Wave Technology
## 4 Positioning Algorithms
### Proximity Algorithm  
Proximity estimation is the simplest technique to implement for localization systems. In [50; 156], the proximity technique estimates the target position when the target is close to a known position, as shown in Figure 9. It is a detection or range-free-based method that does not calculate the exact position coordinates of the object. Therefore, proximity estimation is a coarse-grained technique [157]. The proximity method was used in global system for a mobile communication (GSM)-based localization system. It succeeds in achieving an accuracy between 50 and 200 m, which depends on the GSM cell size [158]. The method needs a compact deployment of BLE beacons to obtain the highest precision but does not need a calibration process for localization [159]. The proximity method has a high variance which sometimes might not satisfy the need for localization. Thus, this method is not as widely used as the previous works.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 2 Finite Element Model of a 5 MW Wind Turbine Blade  
### Blade Layup  
In this study, the geometry and material properties of the SNL 61.5-m model, representing a 5 MW wind turbine blade, were modeled based on the report by [PERSON] [26]. Table 1 presents the material properties of laminates used in the blade model. The geometry of the blade was created considering the airfoil type according to the blade span direction, chord length, torsion angle, and the location of the aerodynamic center. The cross-section of the blade comprises seven parts: the leading edge (LE), LE panel, spar cap, trailing edge (TE), TE reinforcement, TE panel, and shear web (SW). As listed in Table 2, the material and stacking sequence were modeled differently depending on each part and the location in the blade span direction. Figure 2 shows the thickness distributions of each panel along the blade span and the finite element model created based on them. Detailed and specific information on geometry and material properties may be referred to [PERSON] [26] and the authors' previous paper [15].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **Panel** & \(E_{\textbf{11}}\) (GPa) & \(E_{\textbf{22}}\) (GPa) & \(G_{\textbf{12}}\) (GPa) & \(\
u_{\textbf{12}}\) & \(\rho\) (kg/m\({}^{\textbf{3}}\))** \\ \hline Gelcoat & Gelcoat & 3.44 & 3.44 & 1.38 & 0.30 & 1235 \\ E-LT-5500 (UD) & UD glass TE & 41.80 & 14.00 & 2.63 & 0.28 & 1920 \\ SNL (Triax) & Triax skins/Triax root & 27.70 & 13.65 & 7.20 & 0.39 & 1850 \\ Saertex (DB) & SW facesheet & 13.60 & 13.30 & 11.80 & 0.49 & 1780 \\ Foam & TE foam/LE foam & 0.26 & 0.26 & 0.02 & 0.30 & 200 \\ Carbon (UD) & UD carbon & 114.50 & 8.39 & 5.99 & 0.27 & 1220 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of laminates [26].  
Figure 2: A finite element model and thickness distributions of each panel along the blade span (z: radial position, R: blade span).
## 1 Introduction
In this paper, we will be examining the diffraction problem of the H-polarized plane wave incidence using the Wiener-Hopf technique, and we will be utilizing a waveguide geometry that is identical to the one presented in [36]. The geometry considered in this paper serves as an important generalization to our previous paper that analyzes a simpler case of four-layer material loading [47]. Due to the existence of five material layers in the waveguide, the analysis procedure becomes much more complicated compared to the four-layer case [47], as the multiple reflection-refraction-diffraction effect inside the waveguide is rigorously taken into account in this paper. Applying the factorization and decomposition procedure, the Wiener-Hopf equations are solved exactly. The exact solution, however, is formal since it contains infinite series of terms with unknown coefficients. Employing the edge condition rigorously, we can find the asymptotic behavior of the unknowns leading to highly-accurate approximate solutions of the Wiener-Hopf equations. The results are shown to be uniformly valid for arbitrary waveguide dimensions. Taking the inverse Fourier transform of the solution in the transform domain and evaluating the resultant integrals, we can derive the scattered field representation real space. The scattered field inside the waveguide can be evaluated by computing the residue contributions from an infinite number of simple poles leading to the waveguide TM modes. The saddle point method is used to asymptotically estimate the scattering field outside the waveguide, yielding a far-field expression. Based on these analytical results, we will provide numerical examples of RCS for a range of physical parameters, along with a detailed analysis of the far-field scattering properties of the waveguide. We shall also make some comparisons with the E-polarized case [46]. Throughout this paper, we assume that the time factor is represented by \(e^{-i\omega t}\) and is subsequently omitted for the sake of brevity and clarity.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 3 Overview of Network Protocols
### Application Layer
#### 3.1.5 Advanced Message Queuing Protocol  
AMQP is an open-standard application layer protocol designed and tailored to support communication in a distributed environment [59]. AMQP supports various messaging models such as point-to-point, publish-and-subscribe, and request-reply, providing flexibility in designing distributed systems and enabling effective message-based communication. The core feature of AMQP is the guaranteed delivery of messages with its store-and-forward feature that ensures reliability [60]. This utilitarian feature is paramount in a V2I communication environment where real-time messages are important for the successful operation of the entire system. Refer to Figure 7 for AMQP architecture.
# Research on Communication Stability of Inter-Cannonball Network Based on OPNET
## 3 Routing Protocol Modelling Analysis
### AODV Routing Protocol  
The AODV protocol implements route discovery and route maintenance between nodes through four types of packets: RREQ, RREP, RERR, and active route detection (HELLO) packets. When data is forwarded between communication network nodes, the source node sends out the data packet, which is forwarded by the intermediate node, and the destination node receives the data packet and performs related processing [32].  
Incremental sequence numbers are set in AODV protocol messages to avoid routing loops, which are situations where the metric value of a route counts to infinity due to the formation of loops when data is forwarded between nodes [33]. The serial number of the packet lets the node know what the old and new status of the packet is. This makes it easy for the node to do routing maintenance. If there is a route between the source node and the destination node, the data forwarding process can start directly; otherwise, the source node will start the route discovery first.  
Figure 6 illustrates the process of AODV route establishment, where \(node\_1\) is the source node and \(node\_3\) is the destination node. The source node will first send RREQ messages in multicast form to other nodes within the communication range of the node, at which time the intermediate node receiving the RREQ packet will establish or update the reverse route to the source node [34]. If the RREQ message reaches the destination node, or if it reaches the existence of a sufficiently new, intermediate node with a routing table to the destination node, it will unicast the RREP message to the source node. Otherwise, the intermediate node will continue to forward RREQ messages for route finding to the destination.  
The AODV protocol periodically broadcasts the HELLO message to confirm the connectivity of the communication link. If a route has not been used within the \"active route timeout\", or if the next hop of a valid route is not reachable due to node movement or failure, the route is invalid. In this case, the RERR message is broadcast by the node, informing other nodes that the IP address of the node is not accessible and deleting the routing table associated with the unreachable node. Then, the source node restarts the route discovery process.  
Figure 5: Wireless LAN delay comparison of AODV and DSR routing protocols.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 4: Network Architecture
#### 4.3.3 Network Heterogeneity  
Heterogeneity refers to the diversity in devices' capabilities, such as differences in processing power, storage, and communication range, which directly impacts network diffusion algorithms.  
Strengths:  
* Optimization: Powerful devices can offload tasks from less capable devices, improving overall network performance.  
Weaknesses:
* Bottlenecks: Less capable devices may slow down diffusion processes if they cannot handle network demands, creating bottlenecks.
## 5 Numerical Study
### The CGA in the Inventory Policy Optimization  
In order to comment on the usefulness of the CGA regarding optimizing logistic network behavior, numerous models with topologies of different sizes and complexities were examined (number of nodes: 5-70; gamma, Poisson, normal distributions with different parameters; around 5000 problem instances overall). For illustrative purposes, two representative examples were selected and given detailed treatments in the latter part of this section. The first series of these simulations was conducted in the framework of a \"small\" network comprising five nodes (\(N=3\), \(M=2\)) and was compared with the exact solution obtained using the full-search approach. The second example corresponded to a much larger, fourteen-node system (\(N=10\), \(M=4\)), which was not amenable to the exhaustive-search method. The common parameters were set as follows:  
* Simulation interval \(T=50\) periods.
* External demand was imposed on all the controlled nodes, which was generated using a gamma distribution with the shape and scale coefficients equal to 5 and 10, respectively.
* The CGA population comprised 10 individuals.
* Mutation probability = 15%.
* Stop conditions: generation limit = \(10^{4}\), the number of generations without improvement = \(10^{3}\).  
In each case, the performance of the standard OUT vs. networked NOUT policy was assessed.