esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 5 Conclusions  
In order to improve the SNR for low frequency electromagnetic TTE communication, the ANC-TDE algorithm based on MCC is proposed and implemented on FPGA in this paper. As shown in the simulation and experiment results, the proposed algorithm can adaptively compensate the varied time delay between the primary input and the reference input. With the time delay compensated, the proposed algorithm can reduce the complexity of the ANC filter and improve the noise cancellation performance in the impulsive noise environment.  
In the future work, we plan to conduct noise cancellation experiments in the real TTE environments and optimize parameters of proposed algorithm according to noise characteristics in different applications.  
This work is supported by National Key R&D Program of China (No.2016 YFB0700503), CERNET Innovation Project(No.NGII20160304), and the National Natural Science Foundation of China under Grant (No.61273071 and No.61302065). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
Figure 17: The signals received by TMR (Tunneling MagnetoResistance) sensor.  
Figure 18: Adaptive noise cancellation performance.
## 2 Digital Twin-Assisted Gearbox Monitoring
### Gear Faults
#### 2.2.1 Model-Based Development
[7] integrated CAD and FEM tools to optimize gearbox design by reducing weight and stress while improving reliability. FEM simulations were used to identify high-stress zones, facilitating better structural decisions and enabling predictive maintenance. [PERSON] et al. [66] utilized FEM (via HyperMesh and ABAQUS) to simulate gear faults under realistic operating conditions. Combined with deep learning models, the method achieved high accuracy in detecting various faults such as wear, pitting, and broken teeth, thereby supporting real-time fault monitoring. [PERSON] et al. [67], though focused on ship structures, demonstrated that inverse FEM can reconstruct real-time deformations from sensor data. This approach is adaptable to real-time structural health monitoring in gearboxes. [PERSON] et al. [68] combined FEM with dynamic modeling to simulate gearbox vibrations under both healthy and faulty conditions. Time-varying mesh stiffness (TVMS), extracted using FEM, was integrated into the dynamic model to enhance vibration-based fault detection.
## 3 DTN Solutions for IoT
### Taxonomy of DTN Solutions for IoT
#### 3.2.3 X-Dtn  
These solutions exploits and combines DTN with a second technology, taking advantage of two technologies helps to tackle DTN problems while enabling new technologies for IoT environments. Some examples of X-DTN solutions we can find in the literature are the combination of DTN with Opportunistic networking [28], with Information-Centric Networking (ICN) [29], with Machine to Machine (M2M) [30], and with Fog Computing [31].
## 3 Results
### Web Platform Results  
The web platform was developed using plain PHP, hypertext preprocessor, Hypertext Markup Language, JavaScript (JS), and Cascade Style Sheets (CSS), as discussed in its respective subsection. It comprises various modules or sections, such as current data, historical data, export data, configuration, management, and the API utilized by the WSN. The web platform consists of approximately 7600 lines of code, encompassing HTML, CSS, JS, and PHP files. Figure 10 showcases two screenshots of the user interface, (a) displays the main menu featuring buttons for each section. Clicking on a button redirects users to their respective sections. (b) depicts a plot that retrieves captured data by querying a specific date and a selected stevia node, in this case it shows the progression of all seven soil parameters, each with a different color, through time range.  
Figure 10: (**a**) Main menu of the web platform. (**b**) Historical data section, including a plot showing all soil parameters with different colors.  
Figure 11 showcases another screenshot of the web platform, specifically the export data section. This section provides users with the functionality to export data from the measurements table. Users have the flexibility to create queries based on date range and sensor node, allowing for customized data retrieval. Additionally, the export data section offers three quick buttons for relative timestamps, enabling users to easily access measurements from today, the last seven days, or the last 30 days.  
The second image demonstrates the exported data in comma-separated values (CSV) format. This exemplifies the platform's capability to interact with external software, facilitating data analysis and further processing outside of the web platform itself.
## 4 Discussion
### PSO-MLP Model-Building Analysis  
The PSO-MLP model proposed in this study firstly used FR analysis to build the correlations between recorded landslides and related predisposing factors. FR values are commonly used to obtain the input variables of prediction models and have been acknowledged as an efficient tool. Then, the correlation analysis was used to eliminate the collinear predisposing factors. As a result, the avoidance of redundant information contained in the input variables effectively guaranteed the validity of the prediction models.  
Next, the mini-batch gradient descent algorithm was applied to overcome the drawbacks of the conventional gradient descent and stochastic gradient descent algorithms in terms of the non-global optimum, low training speed, reduced accuracy, and lack of parallel computing. In addition, the PSO algorithm was introduced to appropriately screen the optimal structural parameters of MLP, including the learning rate, learning decay rate, learning momentum, and the number of neurons in the hidden layer. The comparative results showed that the proposed PSO-MLP model had significantly better prediction performance than the conventional MLP-only model, suggesting that the PSO-MLP model does address the drawbacks of the MLP model of local optimum and efficient parameter selection.  
Figure 7: ROC curves of the four models for LSP.  
Moreover, it also obtained a more reasonable landslide susceptibility map than the conventional ANN model (i.e., BPNN) and the statistical model (i.e., IV), indicating that the PSO-MLP model is an excellent alternative model and can be extended to other areas for LSP.
## 2 Proposed Coverage Enhancement Modeling
### Enhanced Coverage and Protocol Modeling
This study proposes a DBR routing protocol based on location clustering. Considering the distribution of node positions, the monitoring area is divided into four equal small areas based on clustering. First, in each local small area, the routing protocol is used to transmit data packets to a relay node, and the remaining energy of each node is calculated at the end of the network operation. The data packets received by the relay nodes in each local small area are then sent to the sink node. Finally, the average remaining energy of the nodes is calculated. The average residual energy of nodes can be calculated as  
\[E_{average}=\frac{\sum(E_{initial}-E_{consume})}{N} \tag{26}\]  
where \(E_{average}\) is the average remaining energy; \(E_{initial}\) is the initial energy; \(E_{consume}\) is the energy lost by the node; and \(N\) is the number of nodes.
# Influence of Welding Parameters on the Weld Pool Dimensions and Shape in a TIG Configuration
## 3 Results  
We are interested in studying the influence of four parameters: the gas nature, the value of the current intensity, the distance between the workpiece and the cathode and the value of the cathode tip angle. For each parameter two values are considered:  
1. Intensity: I = 100 A and I = 150 A
2. Distance: distances between the cathode tip and the workpiece d = 3 mm and d = 5 mm. 3. Tip angle: \(\Theta\) = 30\({}^{\circ}\) and \(\Theta\) = 60\({}^{\circ}\)
4. Gas: Helium and Arcal.37. The two gases are commercial products from Air Liquid Company. Arcal.37 designates a mixture of 30% of argon and 70% of helium. Many combinations of these parameters are possible. Sixteen cases should be considered in the experimental plan. We have chosen to replicate each experimental case three times to test the reproducibility. We would also like to analyse the weld pool shape versus time considering six durations. Due to the important number of cases to consider we decide to limit the experimental plan. The study mainly focuses on the first three parameters with two additional cases with Helium gas. The cases considered are presented Table 1 and conducted from 1 to 20 s in 4-s steps. Table 2 details and provides the associated numerical values of the weld pool dimensions for each case. The given values thus correspond to mean values. Method 2 is based on the analysis of one picture at the final time, so to obtain the width dimensions for each given time, six experiments are necessary when only one experiment is necessary for method 1. In order not to multiply the number of experiments only method 1 is used for cases 5, 6 and 7. However at the end of the experiment  
the material is analyzed using method 3 and the widths and depths determined by metallographic investigations. So, some data is available for methods 2 and 3 from cases 5, 6 and 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Name**} & **Shielding Gas** & **Welding Current** & **Arc Length** & **Tip Angle** \\ \hline Case n\({}^{\circ}\)1 & Arcal.37 & 100 A & 5 mm & 60\({}^{\circ}\) \\ Case n\({}^{\circ}\)2 & Arcal.37 & 150 A & 5 mm & 60\({}^{\circ}\) \\ Case n\({}^{\circ}\)3 & Arcal.37 & 100 A & 3 mm & 60\({}^{\circ}\) \\ Case n\({}^{\circ}\)4 & Helium & 100 A & 3 mm & 60\({}^{\circ}\) \\ Case n\({}^{\circ}\)5 & Arcal.37 & 100 A & 5 mm & 30\({}^{\circ}\) \\ Case n\({}^{\circ}\)6 & Arcal.37 & 100 A & 3 mm & 30\({}^{\circ}\) \\ Case n\({}^{\circ}\)7 & Helium & 100 A & 3 mm & 30\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of welding cases.
# Sentiment Analysis of Students' Feedback with NLP and Deep Learning: A Systematic Mapping Study
## 2 Sentiment Analysis and Related Work
### Related Work  
Referring to past literature, we found that one study [18] on sentiment analysis (SA) in the education domain focused on detecting the approaches and resources used in SA and identifying the main benefits of using SA on education data. Our study is an extended  
Figure 2: The architecture of a generic sentiment analysis system.  
form of this article; thus a great deal of information is presented from different dimensions including bibliographical sources, research trends and patterns, and the latest tools used to perform SA. Instead of listing the data sources, we present the four categories of education-based data sources that are mostly used for SA. Furthermore, to increase convenience for researchers in this domain, we present groups of studies based on the learning approaches, most frequently used techniques, and most widely used education related lexicons for sentiment analysis.  
Another review study [19] provided an overview of sentiment analysis techniques for education. The authors of this study provided a sentiment discovery and analysis (SDA) framework for multimodal fusions. Rather than the text, audio, and visual signals focused in [19], our review article aims to present all aspects related to the sentiment analysis of educational information with a focus on textual information only in a systematic way. Furthermore, we also provide a long list of current approaches employed for sentiment discoveries and the results obtained by them. Similarly, [20] aimed to review the scientific literature of SA on education data and revealed future research prospects in this direction. The authors of [20] focused on the area in more depth, including the design of sentiment analysis systems, the investigation of topics of concern for learners, the evaluation of teachers' teaching performance, etc., from almost 41 relevant research articles. In contrast, to conduct our scientific literature review study, we initially filtered 612 research articles from different journals and conferences. At the final stage of filtering, we finalized and included 92 of the most related and high-quality scientific articles published between 2015 to 2020 in this work. The main aim of this paper is to provide most of the available information regarding the sentiment analysis of educational information in a systematic way in a single place.  
Review studies of this kind are greatly helpful for readers in this domain. This review study will assist researchers, academicians, practitioners, and educators who are interested in sentiment analysis with a classification of the approaches to the sentiment analysis of education data, different data sources, experimental results from different studies, etc.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
###### Abstract  
Wireless sensor networks (WSN) are being increasingly used for data acquisition and control of remote devices. However, they present some constraints in critical and large-scale scenarios. The main limitations come from the nature of their components, such as lossy links, and devices with power supply limitations, poor processing power and limited memory. The main feature of software-defined networks (SDN) is the separation between the control plane and the data plane, making available a logically unified view of the topology in the controllers. In this way, it is possible to build network applications that take into account this unified view, which makes the SDN an alternative approach to solve the mentioned limitations. This paper presents the SD6 WSN (software-defined 6 LoWPAN wireless sensor network) architecture, developed to control the behavior of the data traffic in 6 LoWPAN according to the SDN approach. It takes into account the specific characteristics of WSN devices, such as low data transfer rate, high latency, packet loss and low processing power, and takes advantage of the flexibility provided by flow-based forwarding, allowing the development of specific networking applications based on a unified view. We provide a detailed description of how we have implemented SD6 WSN in the Contiki operating system. The new architecture is assessed in two experiments. The first considers a typical advanced metering infrastructure (AMI) network and measures the overhead of SD6 WSN control messages in configurations involving different path lengths. The results indicate that the overhead introduced is not excessive, given the advantages that the SDN approach can bring. The second considers a grid-topology to evaluate the average latency of the peer-to-peer communication. It was observed that the average latency in the SD6 WSN is considerably lower than that obtained with standard 6 LoWPAN, showing the potential of the proposed approach.  
software-defined wireless sensor network; 6 LoWPAN; smart grid; advanced metering infrastructure; low power and lossy networks; neighborhood area network +
Footnote †: journal: Sensors
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 2 System Model and Acquisition Method  
### System Model  
The positioning part in CNFS is a DSSS-CDMA system whose navigation message is modulated onto the PRN code by employing BPSK modulation. The signal transmitter of CNFS is called fusion signal base station (FSBS) which can be realized by adding a positioning signal generator in the communication base station as shown in Figure 1. The communication and positioning signal are transmitted in the same frequency band and allocate different power levels as described above. Therefore, the interference of positioning signal to communication signal can be avoided by keeping the positioning signal transmitted with lower power. The power difference is at least \(-18\) dB based on a large number of experiments. When the duplexing mode of communication system is frequency division duplexing (FDD), the superposed CNFS positioning signal can be transmitted continuously in the downlink frequency band of communication signal. The fusion signal transmitted by FSBS can be expressed as Equations (1) and (2) below  
\[\mathrm{s}^{\mathrm{(i)}}(\mathrm{t})=\mathrm{s}^{\mathrm{(i)}}_{\mathrm{OFDM}}( \mathrm{t})+\mathrm{s}^{\mathrm{(i)}}_{\mathrm{P}}(\mathrm{t}) \tag{1}\]  
\[\mathrm{s}^{\mathrm{(i)}}_{\mathrm{P}}(\mathrm{t})=\mathrm{d}^{\mathrm{(i)}}( \mathrm{t})\mathrm{c}^{\mathrm{(i)}}(\mathrm{t})\cos(2\pi\mathrm{f}_{\mathrm{c }}\mathrm{t}+\varphi) \tag{2}\]  
where \(\mathrm{s}^{\mathrm{(i)}}(\mathrm{t})\) indicates the downlink fusion signal, \(\mathrm{s}^{\mathrm{(i)}}_{\mathrm{OFDM}}(\mathrm{t})\) indicates the downlink communication signal and \(\mathrm{s}^{\mathrm{(i)}}_{\mathrm{P}}(\mathrm{t})\) indicates the CNFS positioning signal. \(\mathrm{d}^{\mathrm{(i)}}(\mathrm{t})\) represents the navigation message, \(\mathrm{c}^{\mathrm{(i)}}(\mathrm{t})\) denotes the PRN code, \(\mathrm{f}_{\mathrm{c}}\) is the carrier frequency and \(\varphi\) is initial carrier phase. Interference cancellation (IC) technique is used to reduce the interference of communication signal to the positioning signal. Therefore, we focus on the CNFS positioning signal \(\mathrm{s}^{\mathrm{(i)}}_{\mathrm{P}}(\mathrm{t})\) and ignore the impact of communication signal in the following analysis.  
The characteristics of the FSBS mean that the positioning system share the same network topology as the communication system. The typical cellular network is as shown in Figure 2 CNFS positioning signal can realize multiple coverage in this network based on several weak signal processing techniques [31; 32]. Besides this typical cellular network, different topology is applied according to different environments. The position of signal transmitters remains constant and the distance between two transmitters is small compared with the distance between satellites in GNSS system. These characteristics give the opportunity to realize fast acquisition in CNFS positioning receiver.
## 3 Results and Discussion
### Analysis of Tar Absorption Process
#### 3.2.2 Effect of Bed Height and Solvent Volumetric Flow Rate  
Figure 10 shows the effect of solvent volumetric flow on the removal tar efficiency. It is evident that an increase in the solvent volumetric flow rate has a significant effect on the removal efficiency. The removal efficiency is dramatically enhanced when the solvent volumetric flow rate is increased. The reason is that by increasing the solvent volumetric flow rate, the mass transfer rates of tar  
compounds increase, resulting in higher tar removal efficiencies [19]. The trend of this effect is similar for all the tar components, but the change rates (increasing rates) of removal efficiencies by increasing the solvent volumetric flow rate are different from component to component. The change rates for benzene are higher than toluene and ethylbenzene.  
Furthermore, increasing the bed height enhances the removal efficiency because of the mass transfer increasing between gas and liquid [19]. This trend of effect is similar for all tar components, but the change rates (increasing rates) of removal efficiency are different from components to another. Here, the change rate for benzene is higher than toluene and ethylbenzene.
## Appendix A Group Velocity Calculation Method of the AE Signal at the Dominant Frequency  
The group wave velocity \(\hat{\theta}_{s\alpha}\) and its variance \({}^{\psi_{g,\alpha}^{2}}\) can be calculated by determining the relationship between the probability distributions of the two random variables \(f\) and \(V_{g,\alpha}\) when they are related by \(V_{g,\alpha}=F(fd)\). In view of the complex resolution of the nonlinear system of equations, we propose in this work an unscented transformation, which calculates the statistics of a random variable that undergoes a nonlinear transformation [70]. Assuming the random variable \(``fd''\) with a mean value \(``\overline{f}d''\) and variance \(``\sigma_{fd}^{2}''\), three weighted sigma points are necessary to calculate the statistics of \(V_{g,A0}\). These points are given by:  
\[\left\{\begin{array}{c}\mathcal{X}0=\overline{f}d\\ \mathcal{X}1=\overline{f}d+\left(\sqrt{(1+k)\sigma_{fd}^{2}}\right)\\ \mathcal{X}2=\overline{f}d-\left(\sqrt{(1+k)\sigma_{fd}^{2}}\right)_{i},\\ \mathcal{W}_{0}=k/(1+k)\\ \mathcal{W}_{1}=1/2(1+k)\\ \mathcal{W}_{2}=1/2(1+k)\end{array}\right. \tag{10}\]  
where \(\mathcal{W}_{i}\) is the weight associated with the ith point and \(k\) is an arbitrary number. By setting \(k=0\), Equation (10) becomes:  
\[\left\{\begin{array}{c}\mathcal{X}0=\overline{f}d\\ \mathcal{X}1=\overline{f}d\ +\left|\sigma_{fd}\right|\\ \mathcal{X}2=\overline{f}d\ -\left|\sigma_{fd}\right|\\ \mathcal{W}_{0}=0\\ \mathcal{W}_{1}=1/2\\ \mathcal{W}_{2}=1/2\end{array}\right. \tag{11}\]  
Given the set of sigma points calculated by Equation (10), the transformation of the Gaussian probability density function of the random variable \"\(fd\)\" to the approximated Gaussian distribution of \(V_{g,A0}\) is summarized in the following steps.  
1. Calculate the group velocity corresponding to each sigma point according to the dispersion curves: \[\mathcal{Y}_{\mathrm{i}}=\mathrm{F}(\mathcal{X}\mathrm{i})\quad\mathrm{i}=0,1,2\] (12)
2. The mean of random variable \(\overline{\
abla}_{g,A0}\) can be calculated as: \[\overline{\
abla}_{g,A0}=\sum_{\mathrm{d}=0}^{2}\mathcal{W}_{\mathrm{i}} \mathcal{Y}_{\mathrm{i}}\] (13) which gives in our case: \[\hat{V}_{g,A0}=\overline{V}_{g,A0}=\sum_{\mathrm{i}=1}^{2}\frac{1}{2}\mathcal{ Y}_{\mathrm{i}}=\frac{1}{2}\left(\mathcal{Y}_{\mathrm{i}}+\mathcal{Y}_{2}\right)\] (14)
3. The variance of random variable \(V_{g_{0}}\) can be determined by: \[\alpha_{\
abla_{g,A0}}^{2}=\sum_{\mathrm{d}=0}^{2}\mathcal{W}_{\mathrm{i}} \left(\mathcal{Y}_{\mathrm{i}}-\
abla_{g,A0}\right)\left(\mathcal{Y}_{\mathrm{ i}}-\
abla_{g,A0}\right)^{\mathrm{T}}\] (15) In our case these parameters are scalar and \(\alpha_{\
abla_{g,A0}}^{2}\) rewritten as: \[\alpha_{\
abla_{g,A0}}^{2}=\frac{1}{2}\left(\mathcal{Y}_{1}-\hat{V}_{g,A0} \right)^{2}+\frac{1}{2}\left(\mathcal{Y}_{2}-\hat{V}_{g,A0}\right)^{2}\] (16)
## 4 Experiments
### Ablation Study
#### 4.6.3 Parameter Analysis  
In our UCDA method, the balance parameter of the final loss function (Equation (15)), denoted as \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\), jointly decide the model's sensitivity toward asymmetric consistency loss \(\mathcal{L}_{ac}\), uncertainty-guided adaptation loss \(\mathcal{L}_{ua}\), and the contrast loss \(\mathcal{L}_{c}\). Setting the balance parameters appropriately is essential as it assists in directing the learning toward our selected objective. During our experiments, these hyperparameters have undergone exhaustive tuning, which is shown in Figure 7.  
It is worth noting that although we have landed on effective configurations for \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) with regard to the PCam dataset, these set values reached optimal accuracies when \(\lambda_{1}=0.3\), \(\lambda_{2}=0.2\), and \(\lambda_{3}=0.5\). Theoretically, the ideal range and values for these parameters will vary according to the distinct characteristics of different data and should be ascertained each time via astute tuning tailored explicitly for specific tasks.  
Figure 7: The parameter analysis for asymmetric consistency loss \(\mathcal{L}_{ac}\), uncertainty-guided adaptation loss \(\mathcal{L}_{ua}\), and the contrast loss \(\mathcal{L}_{c}\) in Equation (15), which is conducted on PCam with 1% labeled data.