esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Network Aided Assembly Line Monitoring According to Expectations of Industry 4.0
## 3 Results
### Test Results and the Comparison of the Result Accuracy in On-Line Mode
#### 3.3.1 Quality of the Localization Algorithms  
To be able to evaluate the assembly system, i.e., to compare the results obtained, the cumulative distribution function (CDF) was used. This function presents the probability of obtaining the certain localization error [32].  
Firstly, the effectiveness of the localization is compared, for test samples from the RSSI values database (these are the points with \"known\" RSSI value). For this case, the following localization situations are compared:  
* Localization at the industrial laboratory using neural networks (the number of neurons at the hidden layer is 200; the neurons are trained with the median database). The result is presented in Figure 9--curve IndusLab NN.
* Localization at the industrial laboratory using WkNN algorithms (the number of neighboring points is 3). The result is presented in Figure 9--curve IndusLab WkNN.  
Following that, the quality of the localization algorithm was valued for test points, which had no RSSI values in the RSSI map (test points with unknown RSSI values). For this case, the following localization situations were compared:  
* Localization at the industrial laboratory using neural networks (the number of neurons at the hidden layer is 200; the neurons are trained with the median database). The result is presented in Figure 10--curve IndusLab NN.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Anchor** & **Mean Value** & **Median** & **Min RSSI** & **Max RSSI** \\ \hline \(1\) & 8.2764 & 8 & 0 & 20 \\ \(2\) & 9.6335 & 9 & 0 & 21 \\ \(3\) & 9.1894 & 9 & 0.5 & 20 \\ \(4\) & 9.4317 & 9 & 1 & 22 \\ \(5\) & 11.3882 & 12 & 3.4 & 17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristic RSSI values in the case of industrial laboratory.  
Figure 9: Comparison of localization accuracy for points with known RSSI values.  
Localization at the industrial laboratory using WkNN algorithms (the number of neighboring points is 3). The result is presented in Figure 10--curve IndusLab WkNN.
## 5 Conclusions  
In this paper, we analyze the QoS of priority-based mPC deployment for coexisting PS-LTE and LTE-R networks. The procedure for deploying an mPC is presented by applying it to practical conditions of the integrated public safety network in Korea. The proposed scheme advances the user-association issue by using PS-LTE user priority. The scheduling priority step is applied using the LTE priority gates model, giving a higher priority to PS-LTE UE. The important performance metrics indexes, such as average UE throughput, UE-received SINR, and UE Rx interference, are considered in order to analyze the proposed mPC deployment. At 50% of the CDF, QoS from the proposed priority-based mPC deployment scheme is better than the mPC average UE throughput at around 1.26 Mbps, the UE Rx interference difference is around 9.91 dBm, and the UE outage probability difference is around 7.68 dB, compared to PS-LTE UE throughput, the PS-LTE UE Rx interference, and the PS-LTE UE outage probability, respectively. Hence, the QoS_mPCD scheme is appropriate for application in coexisting scenarios of next-generation public safety networks. Although the problem of co-channel interference is critical, because of the moving and unplanned deployment of an mPC in the complex coexisting environment of two LTE networks, the proposed scheme efficiently utilized the RBs and reduced the interference as well. So, we concluded that the proposed QoS_mPCD scheme can improve spectrum efficiency for next-generation, overlapping public safety networks.  
[PERSON] added the abstract, introduction, system methodology, simulations, and part of the conclusion. He corrected the sequence of sections, and added important details for the proposed scenario. He conducted the simulations and corrected the English mistakes in the overall manuscript. In addition, he corrected the technical issues related to the manuscript and the proposed schemes. [PERSON] drew the system model figure and added the priority gates model, channel model, and part of the conclusion, along with the necessary details. He wrote the equations with their descriptions, and added the tables, along with the simulation parameters. He helped write the discussion of simulation results. Moreover, he helped in the proposed schemes. [PERSON] was the technical leader for this manuscript. He suggested all the technical issues for the proposed QoS_mPCD scheme for aspects of the simulation. In addition, he corrected the simulation methodology for this manuscript, and corrected mistakes in the simulation environment as well as in the structure of the overall manuscript. All authors have read and agreed to the published version of the manuscript  
This work was supported by an Institute for Information and communications Technology Promotion (IITP) grant funded by the Korean government Ministry of Science, ICT & Future Planning (MSIP) through the Development of Fundamental Technologies for the Next Generation Public Safety Communications under Grant 2017-0-00316.  
The authors declare they have no conflict of interest.
# Time Efficiency Improvement in Quadruped Walking with Supervised Training Joint Model
## 2 Robot Configuration  
### Robot Kinematic  
The quadruped robot shown in Figure 1 is of a sprawling type with 12 degrees of freedom (DoF). The Denavit-Hartenberg (DH) parameters of the robot leg that referred to the first joint attachment with the robot body, \(O_{i}\), are listed in Table 1.  
Notation \(i\) represents the leg number, and \(j\) represents the number of joints. The lengths of the links for each leg's component were \(L_{1}=0.114\) m, \(L_{2}=0.1389\) m, and \(L_{3}=0.2484\) m.  
The robot kinematics are described by [14]  
\[\begin{split}{}^{0}A_{i3}&={}^{0}A_{i1}^{1}A_{2}^{2} A_{i3}\\ &=\left[\begin{smallmatrix}\epsilon_{01}\epsilon_{023}-\epsilon_ {013}\epsilon_{023}-\epsilon_{013}\epsilon_{023}-\epsilon_{01}\epsilon_{01}+L _{12}\epsilon_{01}\epsilon_{02}+L_{3}\epsilon_{01}\epsilon_{023}\\ \epsilon_{01}\epsilon_{023}-\epsilon_{013}\epsilon_{023})& \epsilon_{01}& L_{1}\epsilon_{01}+L_{2}\epsilon_{01}\epsilon_{02}+L_{ 3}\epsilon_{01}\epsilon_{023}\\ -\epsilon_{023}&-\epsilon_{023}&-L_{2}\epsilon_{02 3}-L_{3}\epsilon_{023}\\ 0& 0& 0& 1\end{smallmatrix}\right]\end{split} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(j\) & \(\theta_{ij}\) & \(\alpha_{ij}\) & \(a_{ij}\) & \(d_{i}\) \\ \hline
1 & \(\theta_{i1}\) & \(-90^{\circ}\) & \(L_{1}\) & 0 \\
2 & \(\theta_{i2}\) & 0 & \(L_{2}\) & 0 \\
3 & \(\theta_{i3}\) & 0 & \(L_{3}\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DH parameters of each leg.  
Figure 1: Quadruped robot configuration.  
With the center of mass as the reference point, the position of the endpoint of a leg could be calculated as  
\[{}^{B}\mathbf{T}_{i3} ={}^{B}A_{i0}^{0}A_{i1}^{1}A_{i2}^{2}A_{i3} \tag{2}\] \[=\mathbf{T}_{rans}(X_{i},Y_{i},Z_{i})R_{z}(\theta_{Zi})^{0}A_{i1}^ {1}A_{i2}^{2}A_{i3}\]  
where the definitions for \(X_{i}Y_{i},Z_{i}\), and \(\theta_{Zi}\) are listed in Table 2. With the global coordinate as a reference, the position of the endpoint of a leg could be calculated as follows.  
\[{}^{G}\mathbf{T}_{i3} = \mathbf{T}_{rans}(x_{com},y_{com},z_{com})R_{z}(\theta_{yaw})R_{y }(\theta_{pitch})R_{x}(\theta_{roll})^{B}\mathbf{T}_{i3} \tag{3}\]  
With the global coordinate as a reference, the configuration of each leg's first attachment point,\(O_{i}\), to the robot body could be calculated as  
\[{}^{G}\mathbf{T}_{io} = \mathbf{T}_{rans}(x_{com},y_{com},z_{com})R_{z}(\theta_{yaw})R_{ y}(\theta_{pitch})R_{x}(\theta_{roll})^{B}A_{i0} \tag{4}\]  
where \(x_{com}\), \(y_{com}\), and \(z_{com}\) are the translation distances of the robot's center of mass with respect to the global coordinate system along the \(x\), \(y\), and \(z\) axes, respectively. Variables \(\theta_{yaw}\), \(\theta_{pitch}\), and \(\theta_{roll}\) denote the orientation information of the robot's center of mass with respect to the yaw, pitch, and roll axes of the global system, respectively.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 5 Performance Evaluation
### End-to-End Validation Test  
The End-to-End validation aims to check whether there has been a successful connection between the UE and MEC-App, with traffic passing through the UPF and the MTS-API. The focal point for the analysis was the exchange of messages between the UPF and the API. To this end, during communication, the packets exchanged were captured, and it was possible to validate the path taken by each request.  
The analysis also revealed an average response time for requests of 31 ms, with a standard deviation of 1.71. This standard deviation indicates that the response time in the 30 runs showed slight variation and was close to the average. When verifying the service's existence and receiving a token from the UE, the response time is 26 ms, with a higher standard deviation of 5.21, indicating a more significant variation in response times. When considering the whole process, the average time is 57 ms, with a standard deviation of 5.63 due to the standard deviation of the service existence check.  
This result indicates that the solution is viable for e-health use cases. According to [40], which presents KPIs for healthcare scenarios, this solution would be suitable for applications such as paramedic support and critical events (not specified in the paper).
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 2 Precoding Schemes
### Non-Linear Precoding Schemes
#### 2.2.1 THP Precoding  
The concept of THP is derived from the Decision Feedback Equalizer (DFE) technique. There, the interference resulting from the previously-estimated received symbols was reconstructed and subtracted from the actual received symbol. THP is represented in Figure 2. The power growth resulting from the successive cancellation step is limited by using the modulo function.  
\[\text{mod}\ (x)=x-2\sqrt{M}\left\lfloor\frac{s+\sqrt{M}+j\sqrt{M}}{2\sqrt{M}} \right\rfloor, \tag{9}\]  
where:  
\[\mathbf{x}_{p}=[x_{pk}]=\mod\ \left(x_{k}-\sum_{l=1}^{K-1}b_{kl}x_{pl}\right), \quad\forall k\in 1,...,K \tag{10}\]  
and the feedback matrix \(\mathbf{B}=[b_{kl}]_{k,l=1,2\cdots K}\) is a unitary lower diagonal matrix, obtained through a QR decomposition of \(\mathbf{H}\), such that:\[\mathbf{B}=\begin{bmatrix}1/l_{11}&0&\cdots&0\\ 0&1/l_{22}&\cdots&0\\ \cdots&\cdots&\cdots&\cdots\\ 0&0&\cdots&1/l_{KK}\end{bmatrix}\begin{bmatrix}l_{11}&0&\cdots&0\\ l_{21}&l_{22}&\cdots&0\\ \cdots&\cdots&\cdots&\cdots\\ l_{k1}&l_{k2}&\cdots&l_{KK}\end{bmatrix} \tag{11}\]  
and \(\mathbf{x}_{THP}=\mathbf{Q}^{H}\mathbf{x}\).  
At the receiver, the user's \(k\) signal is firstly divided by \(g_{k}=l_{kk}\), then passed through the modulo operation to be then sent to the Quadrature Amplitude Modulation (QAM) demodulator.
## 1 Introduction
Wavelet-based feature extraction and CNN application have been studied in machine fault classification. However, in the PHM field, it has become increasingly important to predict the RUL accurately for predictive maintenance, rather than simply diagnosing and classifying faults in a machine or component [24; 28; 29]. To the best of our knowledge, no research on RUL prediction using deep learning with 2-D image features is found in the literature. Therefore, we propose the image feature-based RUL prediction method. The proposed method can be used for prognosis as well as diagnosis. Our major contribution is to improve the accuracy of RUL prediction using the HI combined with CWT and CNN (i.e., CWT- and CNN-based HI (CWTCNN-HI)). First, CWT is used to convert a 1-D vibration signal into a 2-D image feature using wavelet power spectrum. The raw signal contains only time domain information, but the transformed image feature is effective for visualizing both time and frequency domain information of the vibration signals. In addition, CWT can effectively isolate non-stationary and transient data such as vibration signals and can detect weak defect signal, even in strong noises [26]. Once the raw signal is converted into an image feature, the image is directly entered into the 2-D CNN architecture. CNN is used as a regression model to estimate the CWTCNN-HI based on training images. The HI quantifies the condition of the machine or component. Since the RUL is predicted by the estimated HI, the accuracy of the prediction model becomes critical. Although CNN requires a large amount of training data and tuning of hyper-parameters, previous studies have shown its superior performance in image analysis [28; 29; 30]. In addition, CNN automatically discovers useful features through multi-layer convolutional filters. Therefore, the process of extracting, selecting, and fusing multi-domain features can be compressed with a single algorithm. With more training data, prediction performance of the proposed method can be improved. In this study, we use the vibration dataset of bearings provided by PRONOSTIA for RUL prediction [31]. The experimental results demonstrate the superiority of the proposed method compared to related works on the same dataset. The rest of the paper is organized as follows. Section 2 presents the proposed CWTCNN-HI with brief introductions of CWT and CNN. Section 3 showcases the validation of the proposed method using the PRONOSTIA dataset, and the experimental results are compared to other related research. Finally, in Section 4, we conclude with a discussion and mention future research topics.
## 4 Machine Learning Algorithms for Seismic Elephant Rumble Detection  
A number of ML algorithms together with three spectral feature sets described in Section 3 were tested for seismic elephant rumble detection. To this end, we employed the open-source LazyPredict machine learning platform using the scikit learn 1.3.2 [21] Python library. This ML platform allows the training and testing of various ML algorithms quite efficiently. The MFCC feature set performed well with the following machine learning algorithms:  
* Ridge classifier;
* Support vector machine (SVM)--linear;
* Logistic regression.  
Furthermore, the Hjorth parameters performed well with the following machine learning algorithms:  
* Decision tree classifier;
* AdaBoost classifier;
* Random forest classifier.  
Finally, the spectral energy distribution performed well with the following machine learning algorithms:  
* Light gradient boosting machine classifier (LGBM)
* Gradient boosting classifier
* AdaBoost classifier.  
In our study, we used a dataset consisting of 7 rumbles and 60 non-rumble instances collected at Pinnawala Elephant Orphanage. Given the limited size of our dataset, we employed cross-validation to evaluate the performance of ML models, ensuring a more robust assessment of model generalization. As shown in Figure 6, the LazyPredict received the MFCC, Hjorth, or spectral energy distribution feature set and executed the corresponding ML algorithms. For each ML algorithm and feature set combination, the performance was recorded in terms of accuracy, balanced accuracy, and F1 score. It is worthwhile to note that here, we present the best performing ML algorithms for a given set of features, and the use of a set of features, e.g., MFCC, is not limited to the presented ML algorithms only. Furthermore, we may employ an early fusion of features (before the classification) or a late fusion of classification results (using an ensemble learning approach with a selected set of ML algorithms) to improve the classification performance. We intend to incorporate these approaches in future work.
## 4 Experimental Results and Analysis
### Quantitative Analyses
This shows that XGBoost, as an integrated learning component, further enhances the prediction ability and fitting effect of the model in the MDFFE model. From the perspective of feature engineering and model fusion, MAE, RMSE, and MAPE continue to decrease with the addition of FE and XGBoost, R-Squared continues to increase, and SSE continues to decrease, which clearly shows the gradual optimization of model performance. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CNN** & **FE** & **XGBoost** & **MAE\(\downarrow\)** & **RMSE\(\downarrow\)** & **MAPE\(\downarrow\)** & **R-Squared** & **SSE\(\downarrow\)** \\ \hline \(\surd\) & \(\times\) & \(\times\) & 0.0720 & 0.0838 & 0.0720 & 0.7019 & 150.8885 \\ \(\surd\) & \(\surd\) & \(\times\) & 0.0250 & 0.0376 & 0.0254 & 0.9353 & 44.6601 \\ \(\surd\) & \(\surd\) & \(\surd\) & **0.0068** & **0.0084** & **0.0071** & **0.9970** & **1.5090** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative analysis of ablation. The table illustrates the performance variations of different model configurations on key evaluation metrics and validates the effects of feature engineering and model fusion. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAE\(\downarrow\)** & **RMSE\(\downarrow\)** & **MAPE\(\downarrow\)** & **R-Squared** & **SSE\(\downarrow\)** \\ \hline BiLSTMModel & 0.0806 & 0.0980 & 0.0842 & 0.5916 & 206.7034 \\ CNN & 0.0720 & 0.0838 & 0.0720 & 0.7019 & 150.8885 \\ CNNLSTM & 0.1403 & 0.1957 & 0.1475 & \(-\)0.6273 & 823.6530 \\ LSTM & 0.2427 & 0.3332 & 0.2592 & \(-\)3.7164 & 2387.2202 \\ CNNBiLSTMGRU & 0.1314 & 0.1665 & 0.1464 & \(-\)0.1782 & 596.3677 \\ TransformerModel & 0.1395 & 0.1689 & 0.1519 & \(-\)0.2123 & 613.5961 \\ LSTMTransformer & 0.2504 & 0.2831 & 0.2813 & \(-\)2.4046 & 1723.2487 \\ CNNLSTMGRU & 0.1298 & 0.1751 & 0.1288 & \(-\)0.3020 & 659.0109 \\ MDFFE & **0.0068** & **0.0084** & **0.0071** & **0.9970** & **1.5090** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative analysis of comparative experiments (quantitative results of each model on key evaluation indicators are listed for evaluating prediction performance).
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 3 Star Identification Algorithm
### Matching Calculation
When the distance ratio constraint is satisfied, \(\omega_{i}\) and \(\mu_{j}\) can be considered successfully matched, i.e.,  
\[|\frac{\beta_{last\_i}}{\beta_{i}}-\frac{b_{last\_j}}{b_{j}}|\leq\epsilon_{R} \tag{12}\]  
In the process of calculating the feature match count, \(i\) and \(j\) start from 1, with the distances \(\beta_{last\_i}=\beta_{m}\) and \(b_{last\_i}=b_{n}\) saved from the previous match. If the features corresponding to \(i\) and \(j\) match under the cumulative angle and distance ratio constraints, the feature match count \(num_{match}\) between the reference star and the candidate star increases 1, and the current distances \(\beta_{i}\) and \(b_{j}\) replace the previously saved distances \(\beta_{last\_i}\) and \(b_{last\_j}\). Then, compare the next position. Otherwise, make the next judgment based on the value of \(\omega_{i}\) and \(\mu_{j}\) to proceed: if \(\omega_{i}<\mu_{j}\), then \(i=i+1\); if \(\omega_{i}>\mu_{j}\), then \(j=j+1\). This process of angle matching accumulation and distance ratio validation continues. Ultimately, when \(i\geq m\) or \(j\geq n\) is satisfied, the feature match count calculation ends. The resulting feature match count \(num_{match}\) at this point represents the number of successfully matched neighboring star feature pairs between the reference star \(R\) to be identified and the candidate star \(star_{id}\). Due to differences in the number of neighboring stars around each navigation star, using only the feature match count to measure the matching degree is not reasonable. Therefore, the ratio of the feature match count to the number of neighboring stars around the navigation star should be used as the measure of the matching degree. That is,  
\[S=\frac{num_{match}}{num_{neighbor}} \tag{13}\]  
Clearly, the matching score \(S\) is in the range of [0,1], where a higher value indicates a greater degree of similarity between the reference star and the current candidate star.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 5 Prototype Fabrication and Measurements
### Measured Impedance Characteristics  
In this section, the measured antenna impedance characteristics _with_ and _without_ a matching network are presented. The measurements were conducted by placing the antenna within the SpO2 sensor and placing the assembly on a IXB-063 phantom column produced by INDEXSAR [74]. The IXB-063 phantom column comprises a silicon loaded with carbon powder and has been used to emulate the human wrist tissue effect to the antenna performance [74]. The IXB-063 phantom column is characterized by a cylindrical shape of 200 mm height and 63 mm diameter.  
The Smith Chart of Figure 10(a) shows the measured antenna impedance on the phantom column with and without a matching network. With no matching network (solid red line), a large impedance mismatch can be observed at 915 MHz as shown at Point A. On the other hand, the addition of the \(\uppi\)-type matching network (dashed green line) enables wideband matching around 915 MHz as shown at point B. In addition, the measured antenna impedance with the \(\uppi\)-type matching network is shown (dotted blue) and shows only a small difference at 915 MHz (Point C) compared to on-body case, indicating a desirable resilience to detuning for the developed antenna.  
The measured antenna \(\,\mathrm{I}\,\mathrm{S}_{11}\,\mathrm{I}\,\) is shown in Figure 10(b) both for the free-space and on IXB-063 phantom column cases. It can be observed that the antenna exhibits minimal detuning with the antenna maintaining a \(-10\) dB impedance bandwidth of 55 MHz on the human arm and of 65 MHz in free-space. These figures meet the minimum 26 MHz bandwidth requirement for the 915 MHz band as defined in [12].  
Figure 10: Fabricated wrist-worn wireless SpO2 sensor prototype: (**a**) top view with no top enclosure; (**b**) bottom view; (**c**) antenna PCB bottom view.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 5 Feature Analysis and Pattern Recognition
### Feature Analysis  
Feature extraction is a fundamental step in signal processing and pattern recognition, aiming to distill the most informative features from raw sensor data for effective classification. In our study, data collected from sensors--specifically accelerometers and gyroscopes--were preprocessed through filtering and normalization to mitigate noise and eliminate redundant information. This rigorous preprocessing ensures that the subsequent feature extraction process yields high-quality inputs for analysis.  
We extracted a range of features from the preprocessed data, which were broadly categorized into time domain and frequency domain features. In the time domain, statistical measures such as the mean, variance, peak value, and root mean square were computed. These metrics capture the trends and fluctuations in the sensor data, providing clear distinctions between different movement states. For instance, the variance in accelerometer readings is typically higher during running compared to walking, reflecting more vigorous movement patterns.  
In the frequency domain, we converted the time domain signals using Fourier transformation to extract spectral characteristics such as power spectral density, skewness, and kurtosis. These features are vital for identifying periodic patterns and subtle variations in movement, particularly when distinguishing activities with different frequencies like walking versus running.  
Furthermore, our approach introduces combined features that integrate data from an Inertial Measurement Unit (IMU) including accelerometers and gyroscopes and merge both time-domain and frequency-domain characteristics. This innovative feature fusion significantly enriches the information content available for subsequent classification tasks, representing a key contribution and novelty of our work.