esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cluster Head Selection
### Self-Organized Schemes
#### 2.1.2.4 Regional Selectivity
While setting a limit on the maximum number of allowable hops is proposed by the paper to address this issue, adding an elementary cluster formation stage and applying the proposed scheme into cluster boundaries is another solution, which also improves the scalability of the algorithm. In [33], Cluster-based Energy-efficient Scheme (CES) is presented for electing a cluster-head in 2-hop neighbourhood region. The paper introduces a parameter called 2-density of a node. The 2-density of a node represents the ratio between the number of links in its 2-hop neighbourhood (links between the node and its neighbours and links between two 2-hop neighbours of the node) and the 2-hop degree of the node. In CES, each sensor calculates its weight based on 2-density, its residual energy, and its mobility and broadcasts it to its 2-hop neighbourhood and the sensor node having the greatest weight in its 2-hop neighbourhood is chosen as the cluster-head for the current round. The scheme assumes that sensors have 2-hop knowledge and operate asynchronously without a centralized controller. The CLUBS [34] is another algorithm that forms clusters through local broadcast and converge in a time proportional to the local density of the nodes. To select CHs enough far from each other, the algorithm restart the CH selection process for the clusters with CHs within 1-hop range of each other. While local selection of CHs through broadcasting messages is easy to implement and scalable, it delays the convergence of clustering process and may not guarantee well distribution of the CHs throughout the network.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 4 Application-Specific Comparison
The work in [65] highlights the ability of RL to learn complex control strategies, particularly in situations where modeling robot dynamics is difficult. In [73], the authors developed an open-source framework for RL-based control robots in simulated indoor environments, specifically for 2D and staircase navigation challenges. Lastly, in [61], the authors tackle the difficulties in simulated environments, especially multitasking and meta-learning, filling a gap in current ROS-based RL applications. Multitask learning involves training a single agent to solve multiple distinct tasks, while meta-learning focuses on training agents to learn new tasks quickly by leveraging prior knowledge. The ROS and RL have become important tools for creating and using advanced learning and testing strategies. The modularity and wide range of tools make it easy to combine different algorithms, simulators, and hardware parts, which is very helpful for progressing the field of robotics. Even though benchmarking has fairly advanced with psychics engine tools to evaluate the robot performance, benchmarking in real-world scenarios still remains an open challenge.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 2 Technologies for LV Smart Metering--Background Information
### Wireless Technologies
#### 2.2.2 Cellular Technologies--GSM/GPRS-3G-LTE  
Usually, cellular technologies on smart metering applications are preferred for the telecommunication link between the data concentrator and the utility data center. One of these cellular technologies can be the GSM or GPRS. The frequency bands used in Europe are the 900 and the 1800 MHz bands. GSM has also been considered for implementation for smart grid monitoring  
Figure 2: Smart meters forming a mesh network for data transmission.  
and controlling. The main advantage of GSM is that the network already exists, thus making it the simplest solution for utilities [6; 26]. On the other hand, the GSM network was not originally designed to carry smart metering data, but voice data, so the handling of the increased data traffic from smart meters along with existing voice data services could become an issue. In addition, the GSM/GPRS bands have been mostly allocated for existing mobile services; therefore, it is hard to get the available spectrum for developing a dedicated network for smart metering applications [26].  
The 3G is the third generation of the mobile telecommunications technology. The data rate provided should be at least 0.2 Mbps. The two main standards considered to belong to the 3G category are the UMTS and the CDMA2000. Two more standards are the EV-DO and the EDGE. The frequency band occupied is between 800 MHZ and 1900 MHz for CDMA and EVDO. In addition, the 450 MHz band can be used for CDMA applications [26; 30]. Referring to the smart metering applications, the 3G can be used as the communication link between the data concentrator and the utility data center. The advantage of this technology is that it is already deployed in a large scale, so it can be considered as mature and stable [30].  
The Long Term Evolution (LTE) technology is a standard for high speed mobile communications. It is the evolution of the GSM/UMTS standards. With regard to the 3G architecture, it shows reduced latency. However, its wireless interface is incompatible with 2G and 3G networks. In addition, different bands are used in different countries meaning that devices compatible with one country's regulations can be incompatible with another one's [31]. Referring to smart grid applications it can be used, like the other cellular technologies, for supporting the Advanced Metering Infrastructure backhaul network and in particular the communication link between the data concentrator and the utility control center. The main advantages of LTE technology is the low latency it offers, its high capacity and its low power consumption. On the contrary, the main problem is that the equipment cost remains high [30].
# Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing +
###### Abstract  
In the past years, nonlinear frequency division multiplexing (NFDM) has been investigated as a potentially revolutionary technique for nonlinear optical fiber communication. However, while NFDM is able to exploit the Kerr nonlinearity, its performance lags behind that of conventional systems. In this work, we first highlight that current implementations of NFDM are strongly suboptimal, and, consequently, oversensitive to noise: the modulation does not ensure a large minimum distance between waveforms, while the detection is not tailored to the statistics of noise. Next, we discuss improved detections strategies and modulation techniques, proposing some effective approaches able to improve NFDM. Different flavors of NFDM are compared through simulations, showing that (i) the NFDM performance can be significantly improved by employing more effective detection strategies, with a 5.6 dB gain in Q-factor obtained with the best strategy compared to the standard strategy; (ii) an additional gain of 2.7 dB is obtained by means of a simple power-tilt modulation strategy, bringing the total gain with respect to standard NFDM to 8.3 dB; and (iii) under some parameters range (rate efficiency \(\eta\leq 30\%\)), the combination of improved modulation and detection allows NFDM to outperform conventional systems using electronic dispersion compensation.  
nonlinear Fourier transform; nonlinear frequency division multiplexing inverse scattering transform; Kerr nonlinearity; coherent communication +
Footnote †: journal: Applied Sciences
# An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data
## 3 Methodology
### K-Means Clustering  
Clustering is a traditional unsupervised learning method, which divides data samples into subgroups, thus enabling us to uncover and explain the reasons for different types of adverse events. Among the various clustering methods, K-means clustering is most widely used due to its simplicity and high-efficiency characteristics, though it highly relies on the parameter \(k\) and cannot handle data with varying densities. According to the characteristics of the QAR data and following our previous work [4], we use the Euclidean distance metric-based K-means clustering algorithm. Its basic idea is to divide the sample set into \(k\) clusters according to the distance among samples, so that points within the same cluster are closely connected while the distances between different clusters are as far as possible. Currently, K-means clustering algorithm has been widely used in a variety of applications [34; 38], such as urban hotspots detection [33], earthquake analysis [36], COVID-19 disease analysis [37], etc.  
Let the QAR data be represented by \(X\) with dimensions \((N,d)\), where \(N\) and \(d\) denote the number of flight records and features, respectively. Let record \(i\) be expressed as  
Figure 3: The transformation from wind speed and direction parameters to head and cross winds.  
\(x_{i}\in\mathbb{R}^{d},i=1,2,\cdots,N\). Firstly, the algorithm randomly generates \(k\) cluster centers, and the \(j\)-th center is represented as \(u_{j}(0\leq j\leq k)\), which represents one category. For each \(x_{i}\), we calculate the class that \(x_{i}\) should belong to based on the Euclidean distance between \(x_{i}\) and the cluster centers. Then, \(x_{i}\) is assigned to the closest cluster \(c_{i}\), i.e.,  
\[c_{i}=\arg_{j}\min||x_{i}-u_{j}||^{2} \tag{5}\]  
Then, for each class \(j\), the cluster center is updated in the following way:  
\[u_{j}=\frac{\sum_{i=1}^{N}1\{c_{i}=j\}x_{i}}{\sum_{i=1}^{N}1\{c_{i}=j\}},j=1,2,\cdots,k \tag{6}\]  
Repeat the above process until the following objective reaches the minimum.  
\[J=\sum_{j=1}^{k}\sum_{i=1}^{N}||x_{i}-u_{j}||^{2} \tag{7}\]  
where \(J\) represents sum of squared distances of the clusters.  
Moreover, we use the silhouette coefficient [40] to choose the most suitable value of \(k\) and evaluate the algorithm. The silhouette coefficient combines the cohesion and separation characteristics of the clusters. Let \(d_{i}\) denote the average distance from record \(i\) to other records in the same cluster, \(d_{ij}\) be the average distance from record \(i\) to all records of other cluster \(j\), then the silhouette coefficient \(s_{i}\) of record \(i\) is defined as:  
\[s_{i}=\frac{d_{ij}-d_{i}}{\max(d_{i},d_{ij})}, \tag{8}\]  
Finally, the silhouette coefficients of all records are averaged to obtain the total silhouette coefficient of the clustering result, which is represented as \(S=\frac{\sum_{i=1}^{N}s_{i}}{N}\), and it satisfies \(-1\leq S\leq 1\). Higher silhouette coefficient means better performance.
# A Comparison of Machine Learning Approaches for Predicting Employee Attrition
###### Abstract  
Employee attrition is a major problem that causes many companies to incur in significant costs to find and hire new personnel. The use of machine learning and artificial intelligence methods to predict the likelihood of resignation of an employee, and the quitting causes, can provide HR departments with a valuable decision support system and, as a result, prevent a large waste of time and resources. In this paper, we propose a preliminary exploratory analysis of the application of machine learning methodologies for employee attrition prediction. We compared several classification models with the goal of finding the one that not only performs best, but is also well interpretable, in order to provide companies with the possibility of improving those aspects that have been shown to produce the quitting of their employees. Among the proposed methods, Logistic Regression performs the best, with an accuracy of 88% and an AUC-ROC of 85%.  
machine learning; employee attrition; decision support systems; business applications; business intelligence +
Footnote †: journal:  
Bureau of Labor Statistics and IBM. In this way the authors identify a number of statistically significant factors that relate to the decision of an employee to quit. A variety of machine learning approaches are used for this purpose, starting with Principal Component Analysis (PCA), \(k\)-NN, Random Forests (RF), and Logistic Regression (LR). Eventually in [7; 8] the authors propose an analysis of the same dataset we examine in our paper, i.e., of the IBM HR Analysis Employee Attrition dataset [9]. Our work differs from the latter as it also includes an analysis of the interpretability of the selected models, and introduces the use of an ensemble model. We argue that it is of utmost importance to consider not only the performance of a machine learning model, but also its interpretability properties. Indeed, especially in a critical context such as human resources, it is critical to be able to understand the reasons why employees quit and to make appropriate decisions based on those reasons. Our contributions are the following: (_i_) we perform an exploratory analysis of the dataset and highlighted the possible causes of employee attrition within a company; (_ii_) we employ a set of different machine learning models for predicting employee attrition and report the results of our evaluation in terms of performance and interpretability. The paper is structured as follows: in Section 2 we describe the ML approaches used in our analysis; in Section 3 we present data exploration analysis and the results of our experiments; in Section 5 we summarize findings and propose future perspectives of our work.
## 1 Introduction
[PERSON] et al. [50] calculated MSE with scales from 1 to 20 and then utilized SVM, KNN, and random forest classifiers for discrimination, achieving testing accuracies of less than 85%. To the best of our knowledge, no study has reported a testing accuracy greater than 86% using MSE alone. Certain studies directly applied several HRV indices for discrimination between healthy and CHF participants. However, excessive HRV indices in machine learning may lead to overtraining, which results in high training accuracy but low testing accuracy. To avoid overtraining, feature selection, a pre-process to reduce trivial dimensions, is executed before classification. The simplest feature selection is an exhaustive search that can also determine the optimal combination of dimensions. However, in high numbers of dimensions, the exhaustive search method becomes time-consuming and inaccessible. For example, conducting an exhaustive search for the optimal six dimensions (6D) among 20 leads to 38,760 dimensional combinations, meaning machine learning processing must be executed 38,760 times. As a result, the exhaustive search method is usually ignored, although an exhaustive search on just a few dimensions may be informative. This study investigated MSE analysis accuracy with an exhaustive search of two, three, four, and five dimensions in differentiating younger (<55 years) and older (>55 years) CHF patients from healthy ones in their respective age groups. LDA, SVM, and KNN classifiers were adopted for comparison with previous studies.
# A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion
## 5 Algorithm Verification and Results Analysis
### Algorithm Tested in SBU-Interaction Dataset
#### 5.2.1 Experimental Results and Analysis  
Taking into account the large number of videos in the dataset, we randomly selected a certain amount of test data from the dataset, and selected the other data as training data. Two experiments were tested in this dataset:  
(1) Interaction recognition by weighted fusion of probability of the RGB and depth videos: In this experiment, piecewise segmentation of the interactive behavior was not performed. The HOG features were extracted in RGB and depth image respectively. The parameters in feature extraction were chosen to be the same as in the above experiment. Only two HMMs were trained to model one human interaction with features extracted in RGB video and features extracted in depth video. The final recognition result of human interaction was obtained by weighted fusion of the probabilities of the two datasets. The weighted parameters of RGB video and depth video probability were 40% and 60%. The results are shown in Table 4.  
(2) Interaction recognition by weighted fusion of probability of the RGB and depth videos and three stages: In this experiment, piecewise segmentation of interactive behavior was performed. The HOG features were extracted in RGB and depth image respectively. The parameters in feature extraction and weight parameters were chosen to be the same as in the above experiment. Ten HMMs were trained to model one human interaction with features extracted in RGB video and features extracted in depth video and three stages. The final recognition result of human interaction was obtained by weighted fusion of the probabilities of all probabilities. The results are shown in Table 4.  
As can be seen from Table 4, the recognition accuracy of the weighted fusion of two-source information is much better than that of single-source information. Furthermore, compared with the results without piece fusion, using piece fusion can greatly improve the accuracy of two-person interactive action recognition.
# An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection
## 2 Video Deepfake Detection with CNN-LSTM
### Long Short-Term Memory (LSTM)  
LSTM is a type of Recursive Neural Network (RNN) that effectively mitigates the vanishing gradient problem by storing information over long periods [24]. It outperforms standard feed-forward networks and RNNs due to its memory blocks and recurrent connections [25]. Each LSTM cell features three gates: the input gate, which controls information added to memory using the sigmoid function; the forget gate, which determines what to remove; and the output gate, which generates a filtered output vector using the tanh activation function [13]. Following the information flow through the cell gates, the output \(h_{t}\) is calculated based on the updated cell state and output gate activation. This is depicted in Figure 2.  
Bidirectional RNNs enhance standard RNNs by utilising separate forward and backward states for neurons, allowing outputs to be processed independently [26]. When LSTM memory blocks replace the hidden states in a bidirectional RNN, it forms a bidirectional LSTM [27]. This architecture can be trained like a standard LSTM but requires two steps for the forward pass, processing input data through both states before activating output neurons. The backward pass calculates derivatives for the output and both states. Consequently, bidirectional LSTMs utilise both past and future contexts, improving performance in sequential data processing. Figure 3 illustrates these forward and backward passes.  
Figure 1: The three-layer architecture of CNN.
## 3 Results and Discussion
### Model Validation  
[PERSON] et al. [24] conducted an experimental study on regenerators within a given size and operating parameters. The comparison between the results calculated by our model and that of the experiment is carried out with the structural and operating parameters of the regenerator in the experiment. The proposed model is also compared with Regen, a program specialized in regenerator simulation [25]. The pressure variations of the warm end and the cold end in one cycle are compared as shown in Figure 6. In general, the simulation results are compatible with the experimental results, more so than Regen. The deviation of Regen may be caused by the limitations in the setting of boundary conditions, namely that both pressure and mass flow rate must be specified at the cold end, but that the input value of the phase angle between the two may be deviated from the actual value. Another reason might be because of the friction term determined from the experimental data of steady flow, which may not be accurate for oscillating flow in the regenerator. It can also be seen from the figure that the deviations in amplitudes and phase angles between the pressure curves predicted by our model, and the experimental results of both the warm end and the cold end, are relatively small. This deviation may be caused by the assumptions in the model, as well as the difference in boundary conditions at the cold end as denoted by the blue curves in Figure 6.  
Figure 5: Predictions of cold end velocity for different initial parcel sizes: (**a**) 1 mm; (**b**) 0.5 mm; (**c**) 0.333 mm. A cycle is divided into 200 time steps for all the simulations.  
Figure 6: Comparison of the predicted pressure variations with the experimental results [24] and the calculated results by Regen [25].
## Abbreviations  
* 2 pol dual-circularly-polarized CMC Code-Minus-Carrier CN0 carrier-to-noise ratio GNSS Global Navigation Satellite System GSA European GNSS Agency IMU Inertial Measurement Unit INS Inertial Navigation System LHCP Left-Hand Circular Polarization LOS Line-of-Sight NLOS Non-Line-of-Sight PVT Position, Velocity and Time RHCP Right-Hand Circular Polarization RLR RHCP to LHCP Ratio RMS Root Mean Square RTK Real-Time Kinematics SSN Septentrio SW SoftWare UAB Universitat Autonoma de Barcelona WE Weighting and Exclusion
## 3 Diversity-Controlled MUST Technique
### Receiver Design at an MUST-Enhanced UE
 \tag{12}\]  
By summing Equation (12) over \(N\) sub-carriers, the final received signal for the MUST-enhanced UE can be obtained as follows:  
\[y_{comb}=\sum_{i=1}^{N}\tilde{y}_{E}(i). \tag{13}\]  
If the SIC is successfully executed in Equation (12), then \(\tilde{y}_{E}(i)\) is given by  
\[\tilde{y}_{E}(i)=\left\|\mathbf{h}_{E}(i)\right\|^{2}\sqrt{\mathcal{E}}\sqrt {\alpha}x_{E}+\mathbf{h}_{E}(i)^{H}\mathbf{w}_{E}(i), \tag{14}\]  
where \(x_{E}\) is the QPSK symbol mapped from the information bit pair \((b_{E,1},b_{E,2})\). In addition, with the perfect SIC, the final received signal for the MUST-enhanced UE can be obtained as follows:  
\[y_{comb}=\sum_{i=1}^{N}\tilde{y}_{E}(i)=\text{Tr}\left(\mathbf{H}_{E}^{H} \mathbf{H}_{E}\right)\sqrt{\mathcal{E}}\sqrt{\alpha}x_{E}+\sum_{i=1}^{N} \mathbf{h}_{E}(i)^{H}\mathbf{w}_{E}(i), \tag{15}\]  
where \(\text{Tr}\left(\cdot\right)\) denotes the trace operator of the matrix. Let \(W_{comb}\triangleq\sum_{i=1}^{N}\mathbf{h}_{E}(i)^{H}\mathbf{w}_{E}(i)\) and then \(W_{comb}\) can be written as:  
\[W_{comb}\sim\mathcal{CN}\left(0,N\cdot N_{0}\right). \tag{16}\]  
The information bit pair \((b_{E,1},b_{E,2})\) can be decoded by the sign of \(y_{comb}\) as follows:  
\[\left(\hat{b}_{E,1},\hat{b}_{E,2}\right)=\mathcal{M}^{-1}\left(Sgn(\Re(y_{ comb})),Sgn(\Im(y_{comb}))\right), \tag{17}\]  
where \(\hat{b}_{E,1}\) and \(\hat{b}_{E,2}\) denote the estimates of \(b_{E,1}\) and \(b_{E,2}\), respectively. Figure 4: Receiver structure at the MUST-enhanced UE with the diversity-controlled MUST technique. Table 3 summarizes the computation complexity of the proposed receiver at the MUST-enhanced UE in terms of the complex number of operations. Basically, the computational complexity at the MUST-enhanced UE is larger than that at the MUST-based UE because of the symbol-level SIC operation and the signal transmission over multiple sub-carriers.