esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient LDPC Encoder Design for IoT-Type Devices
## 4 Richardson-Urbanke Encoding Algorithm
The second reason is that \(\mathbf{\Phi}^{-1}\) is not sparse in general, and the complexity of the multiplication is quadratic on \(N\). This is (for large block sizes) the most computationally demanding part of the encoder. However, it was shown that for QC-LDPC codes with a dual diagonal structure, \(\mathbf{\Phi}\) can be reduced to an identity matrix, eliminating the need for multiplication by \(\mathbf{\Phi}^{-1}\)[45]. The additional constraint on the \(\mathbf{T}\) and \(\mathbf{E}\) submatrices is that only nonzero elements (submatrices in QC-LDPC) are organized in a dual diagonal in \(\mathbf{T}\)[45]. In the article [45], it was shown how circular shifts \(s_{i,j}\) in matrices \(\mathbf{B}\) and \(\mathbf{D}\) of the dual-diagonal QC-LDPC \(\mathbf{H}\) could be calculated to make \(\mathbf{\Phi}\) an identity matrix. Dual-diagonal codes are used in several industrial standards, e.g., WiMAX [37] and Wi-Fi [38].
# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 3 Mine Intelligent Ventilation Wind Speed Sensor Cleaning Model and Evaluation Metrics
### Evaluation Metrics  
The introduction of evaluation metrics helps quantify the model's performance and prediction accuracy. Therefore, the mean absolute error (MAE) and root mean squared error (RMSE) are introduced to measure the model's accuracy. These metrics can be directly computed using functions from the sklearn library in Python [26].  
The mean absolute error (MAE) calculates the absolute value of the prediction error for each data point and then takes the average of these absolute errors. A smaller MAE value indicates that the model's predictions are closer to the actual values, reflecting higher accuracy. The formula is as follows:  
\[\text{MAE}=\frac{1}{T}\sum_{t=1}^{T}\left|Y_{t}-\hat{Y}_{t}\right| \tag{16}\]  
The root mean square error (RMSE) is a metric used to assess the accuracy of a model by calculating the mean of the squared prediction errors and then taking the square root. A smaller RMSE value indicates higher prediction accuracy of the model. The formula is as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{T}\sum_{t=1}^{T}\left(Y_{t}-\hat{Y}_{t}\right)^{2}} \tag{17}\]  
In the above formula, T represents the number of samples; t denotes a single data sample; \(Y_{\text{t}}\) stands for the true value; and \(\hat{Y}_{t}\) indicates the predicted value.
# Model Mediation to Overcome Light Limitations--Toward a Secure Tactile Internet System
###### Abstract  
Enabling haptic communication as well as voice and data over the future 5G cellular system has become a demand. Tactile Internet is one of the main use cases of the 5G system that will allow the transfer of haptic communications in real time. Latency, availability, reliability, and security are the main design challenges of the tactile Internet system and haptic based bilateral teleoperation systems. An end-to-end latency of 1 ms remains the main challenge toward tactile Internet system realization, not only for the processing and coding delays but mainly for the limitations of light. In this work, we analyze the key solutions to overcome the light limitations and enable the tactile Internet over any distances with the required latency. Building a virtual model or model mediated for the remote environment at the edge cloud unit near to the end user is the main solution. By means of AI, the virtual model can predict the behavior of the remote environment and thus, the end user can interact with the virtual environment with a high system experience. This literature review covers the existing work of the model mediated bilateral teleoperated systems and discusses its availability for the tactile Internet system. Furthermore, the security issues of tactile Internet system and the effect of model mediated system on the required security level of tactile Internet applications are discussed. Finally, a structure for the tactile Internet system, with the deployment of model mediation, is suggested.  
t pacs: tactile Internet; model mediation; latency; security; haptic communication; teleoperation
###### Abstract  
Traditional route planning methods usually plan the \"fastest\" or \"lowest cost\" travel route for users with the goal of finding the shortest path or the lowest cost, but this method cannot meet the needs of tourism users for personalized and multifunctional travel routes. Given this phenomenon, this paper proposes a personalized route planning model based on urgency. First, the model uses the visitor's historical tourism data and public road network data to extract their preferences, POI (point of interest) relationships, edge scenic values and other information. Then, the planned route function is determined according to the urgency value, which provides users with travel routes that accommodate their interest preferences and urgency. Finally, the improved genetic algorithm based on gene replacement and gene splicing operators is used to carry out numerical experiments on the Xi'an and Wuhan road network datasets. The experimental results show that the proposed algorithm is not only capable of planning routes with different functions for diverse users but also performs personalized route planning according to their preferences.  
user preference; scenic route planning; POI relationship modelling; urgency; improved genetic algorithm
# Prediction of Cardiorespiratory Fitness Level of Young Healthy Women Using Non-Exercise Variables
## 3 Results  
### Predictive Model Based on Anthropometric Parameters  
First, the predictive models were determined, which only included somatic features and the subjects' body composition (Table 2). The analysis showed that the classic OLS model enabled VO2 max prediction with a relatively large error of 11.51 (mL.kg\({}^{-1}\cdot\)min\({}^{-1}\)). The shrinkage regression models have much higher accuracy than the OLS model. The prediction error for the Ridge model is equal to 6.67 (mL.kg\({}^{-1}\cdot\)min\({}^{-1}\)), while for the LASSO model, it is equal to 6.71 (mL.kg\({}^{-1}\cdot\)min\({}^{-1}\)). The LASSO model rejected the following predictors: BW, WC, and FFM. The most accurate model estimating VO2 max from anthropometric parameters is Ridge regression for a subset of predictors calculated using stepwise regression. This model generates an error of 6.58 (mL.kg\({}^{-1}\cdot\)min\({}^{-1}\)) and calculates predictions using four predictors (Age, Fat, TBW, and WHtR).
# Smart Grid Security: A PUF-Based Authentication and Key Agreement Protocol
## 5 Security Analysis of PPSG  
In this section, we embark on a comprehensive security evaluation of the proposed authentication and key agreement protocol for smart grid PPSG. The primary objective of this evaluation is to thoroughly assess the security aspects of PPSG from multiple perspectives.  
To begin with, we employ a heuristic evaluation approach to scrutinize the security of PPSG. This method involves a systematic examination of the protocol's components, algorithms, and implementation details to identify any potential vulnerabilities or weaknesses. Through this heuristic analysis, we leverage our expertise and knowledge in the field to identify possible security risks and provide valuable insights into the overall security posture of PPSG.  
Furthermore, we conduct a formal security evaluation of PPSG within the real-or-random model. By adopting this formal model, we can rigorously assess the security guarantees provided by the protocol.  
In order to further validate and reinforce the security claims of PPSG, we employ an automated security protocol verification tool named Scyther [38]. This tool plays a crucial role in validating the security properties of the protocol by subjecting it to rigorous analysis. By utilizing Scyther's advanced algorithms and formal methods, we can exhaustively examine PPSG for any potential security flaws, design vulnerabilities, or weaknesses. The  
Figure 2: Mutual authentication and key agreement phase of PPSG.  
utilization of Scyther ensures a comprehensive assessment of the security of PPSG and offers additional confidence in its effectiveness.  
By combining these three evaluation approaches--heuristic evaluation, formal analysis in the real-or-random model, and security validation using Scyther--we aim to provide a robust and multi-dimensional assessment of the security of the proposed PUF-based authentication and key agreement protocol for smart grid PPSG. This comprehensive evaluation approach enhances the reliability of our findings and strengthens the confidence in the security claims made for PPSG.
###### Abstract  
The rapid advancement of communication technologies underscores the urgent need for robust and adaptable emergency communication systems (ECSs), particularly crucial during crises and natural disasters. Although network-based ECSs have been extensively studied, integrating open-source technologies, such as software-defined wide area networks (SD-WAN) with private long-term evolution (LTE) base stations, is a relatively unexplored domain. This study endeavors to fill this gap by introducing an experimental ECS platform that utilizes a hybrid network, incorporating a VoIP network to enhance open-source and on-premises communications in targeted areas. Our hypothesis posits that a hybrid network architecture, combining SD-WAN and private LTE, can substantially improve the reliability and efficiency of ECSs. Our findings, supported by the open-source OMNeT++ simulator, illuminate the enhanced communication reliability of the network. Moreover, the proposed platform, characterized by autonomous wireless 4G/LTE base stations and an Asterisk VoIP server, demonstrates improved quality of service (QoS) and quality of experience (QoE), with minimal data loss. This research not only has immediate practical applications but also bears significant implications for the development of cost-effective, open-source communication networks, optimized for emergencies, critical infrastructure, and remote areas.  
4G; LTE; SDWAN; VoIP; VoLTE; Asterisk; OMNeT++; critical infrastructures; emergency communication systems +
Footnote †: journal: _etlecom_
## 3 Results  
### Expression of QSOX1 in the Non-Pregnant and Early Pregnant Uterus  
We analyzed QSOX1 expression in the mouse uterus during the estrous cycle by Western blotting. Two forms (about 80 and 65 kDa bands) of QSOX1 were detected from thousands of protein components in the uterine tissue extract collected from the cycling uterus, especially prominent in proestrus and estrus uteri. The 65 kDa band was the primary QSOX1 form (Figures 1a and S1).  
Immunohistochemical staining was conducted using anti-QSOX1 antiserum to reveal the cellular localization of the QSOX1 protein in the pregnant uterus. The QSOX1 protein was immunocalized in luminal and glandular epithelial cells and was more weakly expressed in stromal cells, circular, and longitudinal smooth muscle cells in the non-pregnant uterus (Figure 1b, right). When slides were stained without anti-QSOX1 antiserum, nearly no signal was detected (Figure 1b, left; Figure S2). QSOX1 levels were remarkably elevated  
Figure 1: Expression of QSOX1 in the mouse uterus. (**a**) Western blotting detected two forms of QSOX1 in the uterus homogenates, with the \(\sim\)65 kDa protein as the primary form. P, proestrus; E, estrus; M, metestrus; D, diestrus. Original blots are provided in the Supplementary Materials, Figure S1. Immunohistochemical staining of QSOX1 in the mouse uterus at non-pregnancy (**b**) or gestational days 0.5 dpc (**c**) and 6.5 dpc (**d**). The control section without treating with anti-QSOX1 antiserum showed nearly no staining signal (**b**). (**e**–**g**) show partial enlarged images of the uterus at 6.5 dpc (**d**). Red colors show QSOX1 staining, and blue shows the counterstain of hematoxylin in the nucleus. Scale bar = 200 \(\mu\)m. Abbreviations: amd, anti-mesometrial decidua; bv, blood vessel; cm, circular muscle; dec, decidua; e, embryo; epc, ectoplacental cone; gc, giant cells; ic, implantation crypt; lm, longitudinal muscle; md, mesometrial decidua; s, stroma; ug, uterine glands; ul, uterine lumen.  
in uterine lumen and glands at 0.5 dpc, left still weaker signal in stromal cells, circular, and longitudinal smooth muscle cells (Figure 1c). Later, QSOX1 was highly expressed in 6.5 dpc pregnant uterus, including the decidua at mesometrial and anti-mesometrial regions, lateral vascular sinuses, uterine glands, ectol placental cone, and embryo; however, the trophoblast giant cells around the embryo, circular, and longitudinal smooth muscle cells showed relatively weaker staining (Figure 1d-g).
# Numerically Optimized Fourier Transform-Based Beamforming Accelerated by Neural Networks
## 5 Replacing Optimization Algorithms by Trained ANNs
### Performance Validation of the CTRLNN  
The CTRLNN was trained on the same computer as the BFNN. The typical training time is less than 10 min. After training, the CTRLNN model produces a result in a fraction of second. The ratio of 70%/15%/15% was selected for the training/validating/testing datasets, respectively. To validate the performance of the trained network, the mean square error (MSE) as a function of the epoch number was calculated and plotted in Figure 15a. It was found that the control voltage MSE is about \(5.54\times 10^{-5}\) V\({}^{2}\) at epoch 655. In total, 1000 epochs were used as the maximum number of epochs in the training process.  
Figure 15b plots the reflection phase versus the varactor bias voltage as obtained by simulations in Agilent ADS and predicted by the trained CTRLNN. As one can see, the results predicted by the CTRLNN match the simulated results very well.  
Figure 14: _Cont._  
Figure 13: Correlation between the optimized and BFNN-estimated phase distributions for a \(10\times 3\) PMS in a single-beam scenario. (**a**) Correlation of predicted vs. optimized phases from the training dataset. (**b**) Correlation of predicted vs. optimized phases from the validation dataset.
## 2 Materials and Methods
### Focus Error Signal Technique  
A laser beam, generated by the DVD pick-up head, was introduced to propagate onto a surface of the disk and then, reflected to a portion of 4 quadrant sensors of a photodiode detector for a precise focus [12]. However, the focal range of reflection light of the DVD pick-up head covers more than one quadrant of the photodiode detector and the focus error signal (FES) technique was proposed [13].  
A CD pick-up head with different wavelength is firstly, in this paper, combined with the DVD pickup head for not only making a comparison but also, increasing the ranges of wavelength or scratch details. In order to overcome the misalignment problem between focal distances of the reflection light, the technique of focus error signals (FES) is then developed as shown in Figure 2 and following Equations (2) and (3) for CD/DVD modes. The symbolic modes of the CD/DVD are sets of A, B, C, D and of a, b, c, d, respectively.  
\[FES_{CD}=(V_{A}+V_{C})-(V_{B}+V_{D}) \tag{2}\]  
\[FES_{DVD}=(V_{a}+V_{c})-(V_{b}+V_{d}) \tag{3}\]  
Figure 1: Measurement of scratch width and position using developed CD/DVD pick-up head.  
Figure 2: Developed focus error signal of the CD/DVD pick-up head.
## 3 Methodology
### Preprocessing of Real-Time Data  
To a considerable degree, the accuracy of the data determines the results of the prediction. This indicates that preprocessing data plays a vital function in the model [39]. In this analysis, we picked some of the necessary methods to refine the initial dataset. Firstly, there are some incomplete and inaccurate dataset values due to mistakes or deregulation. These pointless values contributed to several deceptive experimental results, such as the diastolic blood pressure, systolic blood pressure, and body mass index values may not have been 0 in the initial dataset, implying that the true value was absent. We used the mean from the training data to replace all missing values and reduce irrelevant values shown in Table 2.  
Figure 1: Representation of input parameter.  
Second, elimination of outliers: any attribute that does not conform to the usual boundary is referred to as an outlier, as seen in Figure 2, and can be removed by using Equation (3):  
\[Q_{1}=a.quantile(0.25) \tag{1}\]  
\[Q_{3}\ =a.quantile(0.75) \tag{2}\]  
\[IQR=Q_{3}-Q_{1} \tag{3}\]  
where Equations (1) and (2) are the first and third quantile [38], respectively. _IQR_ stands for interquartile range, and its values are shown in Table 3. All values that lie beyond this threshold (_IQR_) are termed outliers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.no** & **Attributes** & **Missing Values** \\ \hline
1. & Pregnancies & 0 \\
2. & Glucose & 5 \\
3. & Blood-pressure & 35 \\
4. & Skin Thickness & 227 \\
5. & Insulin & 374 \\
6. & BMI & 11 \\
7. & Diabetes-Pedigree & 0 \\
8. & Age & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Missing values in PIMA dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.no** & **Attributes** & **IQR Threshold Values** \\ \hline
1. & Pregnancies & 5 \\
2. & Glucose & 41.25 \\
3. & Blood-pressure & 18 \\
4. & Skin Thickness & 32 \\
5. & Insulin & 27.25 \\
6. & BMI & 9.3 \\
7. & Diabetes-Pedigree & 0.38 \\
8. & Age & 17.0 \\
9. & Outcome & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: IQR values for PIMA Indian dataset.  
Figure 2: Box plot representation of data along with outliers.  
Filtered values are seen in Figure 3. The next step is to normalize data, such as bringing data into the range of 0 and 1 by adding normalized filters and calculating z-score values by using Equation (4) [40]. Where \(\overline{u}\) is the mean or average value of the variable, \(a_{i}\) is input values, and \(s\) is the standard deviation of the variable. However, \(b_{i}\) is a new normal value. Table 4 shows mean, standard deviation, minimum, and maximum values of the PIAM dataset. This reduces the uncertainty of estimation and accelerates the process.  
\[b_{i}=(a_{i}-\overline{a})/s \tag{4}\]  
where  
\(b_{i}:\) normalised value  
\(a_{i}=\) input data  
\(\overline{a}:\) input data average  
\(s=\) input data standard deviation
## 5 Conclusions  
In this paper, a robust phase compensation technique was proposed to maintain carrier phase measurement accuracy while suppressing interference using an antenna array. The main contributions of this work are summarized as follows:  
1. The phase bias detection method was presented, which allows phase compensation techniques to be used in practice. It can locate the phase bias appearance time and trigger phase compensation techniques to start phase bias estimation and compensation.  
Figure 8: Dynamics of the GNSS signal. (**a**) Acc. (**b**) Sin.  
Figure 10: Carrier phase errors with and without phase compensation techniques under Sin dynamics. (**a**) 35 dB-Hz. (**b**) 40 dB-Hz. (**c**) 45 dB-Hz. (**d**) 50 dB-Hz.  
Figure 9: Carrier phase errors with and without phase compensation techniques under Acc dynamics. (**a**) 35 dB-Hz. (**b**) 40 dB-Hz. (**c**) 45 dB-Hz. (**d**) 50 dB-Hz.  
2. The phase bias estimation method was studied and the robust estimation was applied to the phase bias estimation, making the proposed technique outperform existing phase compensation techniques in terms of accuracy and robustness. There are still several limitations to this work, and the following future work is worthy of further investigation: 1. The window size in the proposed technique is fixed in this work; however, the selection of the window size requires more attention, and an adaptive window size may be more appropriate to balance the estimation accuracy and the dynamic performance. 2. The scale of the estimated phase bias is limited by the phase discriminator; if the phase bias is beyond the detection range of the discriminator, the phase bias cannot be accurately estimated. This issue should be addressed in future work.  
Conceptualization, S.L. and F.W.; methodology, S.L.; software, S.L. and H.L.; validation, H.L. and X.T.; formal analysis, S.L.; investigation, S.L.; resources, X.T. and S.N.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, X.T. and F.W.; supervision, F.W.; project administration, S.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (Grant No. U20A0193) and the Science and Technology Innovation Program of Hunan Province (Grant No. 2021 RC3073).  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
This study presents an ultrasonic non-destructive method with convolutional neural networks (CNN) used for the detection of interface defects in adhesively bonded dissimilar structures. Adhesive bonding, as the weakest part of such structures, is prone to defects, making their detection challenging due to various factors, including surface curvature, which causes amplitude variations. Conventional non-destructive methods and processing algorithms may be insufficient to enhance detectability, as some influential factors cannot be fully eliminated. Even after aligning signals reflected from the sample surface and interface, in some cases, due to non-parallel interfaces, persistent amplitude variations remain, significantly affecting defect detectability. To address this problem, a proposed method that integrates ultrasonic NDT and CNN, and which is able to recognize complex patterns and non-linear relationships, is developed in this work. Traditional ultrasonic pulse-echo testing was performed on adhesive structures to collect experimental data and generate C-scan images, covering the time gate from the first interface reflection to the time point where the reflections were attenuated. Two classes of datasets, representing defective and defect-free areas, were fed into the neural network. One subset of the dataset was used for model training, while another subset was used for model validation. Additionally, data collected from a different sample during an independent experiment were used to evaluate the generalization and performance of the neural network. The results demonstrated that the integration of a CNN enabled high prediction accuracy and automation of the analysis process, enhancing efficiency and reliability in detecting interface defects.  
convolutional neural network; detectability; automatization; adhesive joints; ultrasonic; dissimilar materials +
Footnote †: journal: applied sciences  
0024
# Extreme Learning Machines to Combat Phase Noise in RoF-OFDM Schemes
## 1 Introduction
Section 2 presents the transmitted and received signals of the RoF-OFDM scheme subject to a direct detection approach. Section 3 mathematically details the complex ELM algorithm. In Section 4, the phase noise suppression techniques based on ELMs in the real and complex planes are introduced. Section 5 shows the system performance as a function of the decorrelation degree between the fields after the photo detection process, which is discussed in Section 6 taking into account the overall practical design purposes. Finally, Section 7 gives concluding remarks.