esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Rule Templates for the First-Class Entities
#### 3.1.2 Design of Rule Templates  
After word segmentation, stop word removal, and parts-of-speech tagging, the relationships between words and parts of speech may be identified. Entities with specific characteristics can be identified based on word construction, context, and part-of-speech information. Figure 3 depicts the overall process of rule templates identifying entities.  
The experiment included nine different types of entity categories. Date and time in a chemical accident report are often noted as a specific day, month, year, or day of a certain month. The most frequent units of time expression are hours, minutes, and precise times. Date and time may generally be recognized by establishing rule templates because they all adhere to a set of preset building criteria.  
Part rules satisfied by date and time are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Format** & **Regularization Rule** \\ \hline YYYY-MM-DD & \(\wedge\)(?:(?0000)[0-9]4 ([-/.]year?) (?:(?0?[1-9]]1[0-2/1(?:0?[1-9]][10-9]]2[0-8]) \\ YYYY-M-D & \(|\)(?:0?[13-9]1[0-2])/1(?:29[30] (?:0?[13578] \\ YYYY year MM month DD day & \(|\)(!102)\()/1(?:31) )(?:(?09[2]:(?04[8]][2468][048][13579][26])\(|\)) \\ YYYY/M/D & (?:0[48][2468][048][13579][26])00) (!-/month?) 072/(?:29)(day)?\$ \\ HH:MM:SS & \(\wedge\)([(!0-1)[?0-9])([2][0-3])):([0-5][0-9])(!-(0-5][0-9])?\$ \\ MM:SS & \(\wedge\)(([1-9]1)([0-1])([0-9])([1-2][0-3])):([0-5][0-9])\$ \\ \hline \end{tabular}
\end{table}
Table 1: Some rule templates for dates and events.  
Figure 3: Named entity recognition process based on rule templates.  
The other two first-class entities can be identified by matching geographic information database and hazardous chemicals database. To date, all the entities of the first-class have been recognized.
## 3 Results and Discussion
### Assessment of Contamination Parameters  
Because of its impact on ecosystems and human health, TE pollution of the environment is a serious concern. To assess this contamination, the contamination parameters CF, Igoo, EF, and CF were estimated. The results are illustrated in Table 6 (EF), Table 7 (Ei and RI), and Figure 2 (CF and Igoo). The CF average values are below 1 for the elements Ba, Mn, Zn, and Y indicating low soil contamination from anthropogenic sources for these elements. Moderate contamination is observed for the elements Fe, Co, Cu, Ga, Ni, V, and Pb, with CF mean values of 1 \(\leq\) CF \(\leq\) 3. For the elements Cr, Nb, Ti, and Zr, the CF mean values are over 3, indicating that our study area is heavily contaminated by these elements. The works carried out by [PERSON] [61] and [PERSON] et al. [62] show that high CF values indicate an anthropogenic origin of the pollutants and relatively low average values could be attributed to a geogenic origin of the elements. The results presented in Table 6 show that the average Enrichment Factor values for the elements considered in this study are <2, with the exception of the average value for Zr (=7.05), Pb (=2.05), Br (=2.06), Ti (=4.97) and Nb (=7.77) indicating a significant enrichment of these elements. Values <2 indicate minimal soil enrichment in this area. Maximum EF values for Cr and Co are 7.16 and 6.96, respectively, i.e., 5 \(<\) EF \(<\) 20, indicating a significant enrichment of some Cr and Co measurement sites. These results show that Zr, Pb, Ti, Nb, Cr, and Co are the main elements of the anthropogenic load in the soil sampled at these sites [63]. The study area bears rare earth deposits that have been explored but not yet mined; future mining activity may increase the enrichment of the trace elements in topsoil. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pollution**} & \multicolumn{2}{c}{**Descrip**} & \multicolumn{10}{c}{**TE**} \\ \cline{2-13}  & **Tive** & **Bas** & **Cr** & **Mn** & **Fe** & **Co** & **Ni** & **Cu** & **Zn** & **Ga** & **Pb** & **Zr** & **Nb** & **Y** & **Ti** & **V** & **Br** & **Sn** \\ \hline \multirow{4}{*}{EF} & Mean & 0.18 & 1.81 & 0.43 & 1.00 & 1.50 & 0.96 & 0.59 & 0.86 & 1.61 & 2.05 \({}^{*}\) & 7.05 \({}^{*}\) & 7.77 \({}^{*}\) & 1.01 & 4.97 \({}^{*}\) & 0.64 & 2.06 \({}^{*}\) & 1.15 \\  & Min & 0.08 & 0.80 & 0.17 & 1.00 & 0.35 & 0.31 & 0.21 & 0.41 & 0.59 & 1.06 & 1.80 & 2.36 & 0.26 & 1.36 & 0.45 & 0.14 & 0.44 \\  & Max & 0.34 & 7.16 \({}^{*}\) & 1.01 & 1.00 & 6.96 \({}^{*}\) & 2.39 & 1.27 & 2.49 & 5.20 & 5.85 \({}^{*}\) & 34.68 \({}^{*}\) & 36.51 \({}^{*}\) & 4.14 & 25.42 \({}^{*}\) & 0.96 & 4.38 & 4.59 \\  & SD & 0.06 & 1.81 & 0.20 & 0.00 & 1.80 & 0.52 & 0.19 & 0.99 & 1.26 & 1.42 & 9.26 & 8.39 & 1.15 & 6.71 & 0.11 & 1.17 & 0.96 \\ \hline \hline \end{tabular}  
* significant enrichment.  
\end{table} 
Table 6: EF in the study area.
## 2 Materials and Methods
### Theoretical Model  
The natural frequency of a uniform rectangular cantilever plate is:  
\[f_{n}=\frac{C}{a^{2}}\Bigg{(}\frac{D}{\rho}\Bigg{)}^{1/2} \tag{2}\]  
where \(C\) is the modal constant (0.56 [1]), \(a\) is the length of the plate (130 mm), \(D\) is the plate stiffness factor for FR4 (5110 kg\(\cdot\)m), and \(\rho\) is the mass per unit area (5.64 \(\times\) 10\({}^{-5}\) kg\(\cdot\)s\({}^{2}\)\(\cdot\)m\({}^{-3}\)).  
Since the Young Modulus (\(E\)) is the key element in the vibration response of any structure, the estimation of \(E\) was done both theoretically and experimentally. The estimated Young modulus of a rectangular PCB (\(E_{PCB}\)) was calculated to be 20.6 GPa from the stiffness of the two composing materials [21] and their material fraction according to Equation (3).  
\[E_{PCB}=E_{FR4}\Bigg{(}\frac{V_{PCB}-V_{Cu}}{V_{PCB}}\Bigg{)}+E_{Cu}\Bigg{(} \frac{V_{Cu}}{V_{PCB}}\Bigg{)} \tag{3}\]  
where \(E_{FR4}\) is the Young modulus of FR4 (18.60 GPa), \(V_{PCB}\) is the volume of the PCB (5.6 \(\times\) 10\({}^{-6}\) m\({}^{3}\)), \(V_{Cu}\) is the volume of the copper layer (1.26 \(\times\) 10\({}^{-3}\) m\({}^{3}\)), and \(E_{Cu}\) is the Young modulus of copper (108 GPa). Figure 3 shows the stress-strain curve and the broken specimen corresponding to specimen 3 of Group 2). The experimental value of \(E_{PCB}\) was found to be 21.59 GPa. The parameters for the tension test were taken from a previous work [22].  
The measured first four modes of vibration, obtained from experimental modal analysis of all the specimens, were then compared by analytical modal analysis using FEM with ANSYS 13.0 with Mechanical APDL solver using a fixed end (cantilever) as boundary condition. Figure 4 shows typical mesh models used for samples with (a) and without (b) holes. The shell element SOLSH190 (size 2 mm) was used and element numbers varied from 1698 to 3721, depending on sample dimensions. To analyze the measured and predicted natural frequencies, an experimental value was plotted against the predicted one for each of the modes included in the comparison. This plot provides a set of correlated mode pairs (CMPs) [5] used to check the degree of correlation between the two sets of results.  
Figure 3: Experimental estimation of \(E_{PCB}\). (**a**) Specim under test (sample 3 of Group 2), (**b**) stress–strain curve of the specimen depicted in (**a**).
# A Fusion Framework for Confusion Analysis in Learning Based on EEG Signals
## 4 Experiments and Discussions
### Analysis of Confusion Matrix  
We provide the confusion matrices of our approach for binary and four-class classification tasks for the subject-dependent and subject-independent experiments, as shown in Figures 7 and 8, respectively.  
Our method performs well in identifying cognitive emotions during the subject-dependent experiment in binary and four-class setups. In the binary task, the model exhibits excellent discrimination between confused and non-confused emotions. In the four-class task, the recognition effect of confused emotions is slightly worse than that of the other three types of emotions. A total of 13% of the confused sample identified as think-right, suggesting that similar EEG patterns may be generated when subjects are confused or think-right. However, the identification performance is relatively poor in the subject-independent experiment due to the variations among different subjects. It is evident that in the case of binary classification, the model demonstrates good recognition of confused emotions among different subjects, but it struggles with identifying non-confused emotions. The same pattern can be observed in the four-class scenario, where the recognition performance is relatively better for confused emotions. This suggests that there is a certain degree of similarity in the EEG of different individuals when they are confused during learning, while there is a significant difference in the EEG of emotions other than confusion. These findings illustrate that addressing the differences among subjects poses a significant challenge.  
Figure 6: Training time for convergence of different methods.  
Figure 7: Confusion matrix of our method for the subject-dependent experiments.
# Crowded Space: A Review on Radar Measurements for Space Debris Monitoring and Tracking
## 6 Conclusions and Future Perspectives
From a hardware point of view, the clear tendency is to move towards the use of phased arrays or sparse arrays rather than large parabolic reflectors, thus favoring higher frequencies and wider bandwidths thanks to the fast data processing and digital beamforming provided by the new generation of CPUs, GPUs, and FPGAs [33; 48; 74]. This opens up to the possibility for radar data digitization in situ rather than depending on a late postprocessing. On the software side, the way to go is the use of new algorithms for the processing of space debris data and images, improving the S/N ratio and decreasing the RCS of detectable objects [75; 76]. These assumptions are reflected in the future coming of the new US Space Fence, the second-generation of the space surveillance systems. The new AN/FSY-3 Space Fence, located on Kwajalein Atoll with a second site expected in western Australia, is a S-band phased array with unprecedented detection capability, coverage, and tracking accuracy [77; 78]. It will be able to provide both survey and tracking, increasing the number of trackable objects by ten times with respect to existing or recently retired US assets. **Author Contributions:** Bibliographic research, data collection and analysis: [PERSON]. ([PERSON]), G.M. ([PERSON]), T.P., P.A., and G.V. ; writing, review and editing: [PERSON]. ([PERSON]); supervision: G.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate
### Optimization of Power Allocation Factor for Cluster Drones
(2022) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Neural Network Based Channel Estimation for Massive MIMO-OFDM Systems with Imperfect Channel State Information. _IEEE Syst. J._**2022**, _16_, 4675-4685. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Discrete-Rate Adaptive Modulation Schemes for Uplink Massive MIMO Systems with Imperfect CSI. _IEEE Trans. Veh. Technol._**2024**, _73_, 5426-5442. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON] Joint User Scheduling and Precoding for XL-MIMO Systems with Imperfect CSI. _IEEE Wirel. Commun. Lett._**2023**, _12_, 1657-1661. [CrossRef]
* [PERSON] and [PERSON] (2024) [PERSON]; [PERSON] Performance Analysis of Partial NOMA-Based Layered D2D Communications. _J Wirel. Commun. Netw._**2024**, _2024_, 45. [CrossRef]
* [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Towards Reinforcement Learning in UAV Relay for Anti-Jamming Maritime Communications. _Digit. Commun. Netw._**2023**, \(9\), 1477-1485. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] Throughput Maximization for Mobile Relaying Systems 2016. In Proceedings of the 2016 IEEE Globecom Workshops, Washington, DC, USA, 4-8 December 2016. * [PERSON] et al. (1999) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation. _IEEE J. Select. Areas Commun._**1999**, _17_, 1747-1758. [CrossRef]
* [PERSON] (2019) [PERSON] Soft Actor-Critic for Discrete Action Settings. _arXiv_**2019**, arXiv:1910.07207. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Deep Gaussian Process for Multi-Objective Bayesian Optimization. _Optim. Eng._**2023**, _24_, 1809-1848. [CrossRef]
* [PERSON] et al. (2022) [PERSON]; [PERSON]; [PERSON]; [PERSON] Joint Optimization of Trajectory and Resource Allocation in Secure UAV Relaying Communications for Internet of Things. _IEEE Internet Things J._**2022**, \(9\), 16284-16296. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Fractional Programming for Communication Systems--Part I: Power Control and Beamforming. _IEEE Trans. Signal Process._**2018**, _66_, 2616-2630. [CrossRef]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Energy consumption optimization based on optimal SIC order in UAV-assisted MEC systems. _J. Commun._**2024**, _45_, 18-30. **Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
## 3 Results  
### Hardware Implementation  
The hardware design of the P/R device is described in Figure 11. This hardware design consisted of four boards: the mainboard, the trigger board, the amplifier board and the high-voltage board. This design was implemented in Altimum Designer v.19. The software in which the trigger board, amplifier board and high-voltage board were designed had only two layers. The mainboard was designed with eight layers on this board, with applied SI and impedance matching techniques to generate pulser signals with appropriate duty.
# Predictive Maintenance for Remanufacturing Based on Hybrid-Driven Remaining Useful Life Prediction
## 2 Proposed Methodology
### Prognostic Approach: Hybrid-Driven Remaining Useful Life Prediction
#### 2.2.2 Model Parameter Identification  
The proposed Weibull distribution model is considered as measuring the reliability of the equipment. Once the two parameters \(\eta\) and \(\beta\) are determined, we can obtain the whole degradation process in terms of the period. A nonlinear least-square method [46] was selected in the proposed predictive maintenance framework to learn the reliability model parameters using both historical data and the current state information obtained from the condition monitoring module. The mathematical formulation for optimizing the reliability model parameters is expressed as follows:  
\[\arg\min_{\eta,\beta}\sum_{t=0}^{T}\left(y_{t}-e^{-(\frac{t}{\eta})^{\beta}} \right)^{2} \tag{7}\]  
where \(y_{t}\) is the historical reliability at \(t\), \(T\) is the current time.
# Multifunction RF Systems for Naval Platforms
## 4 MFRF System Prototype Development Programs
### InTop
#### 4.2.2 Mfew ADM  
The MFEW ADM antenna utilized 20 dual-polarization sinuous receive elements arranged in an interferometer configuration. Sinuous elements are planar with a circular shape [49]. They feature a low RCS, a bandwidth as high as 9:1, a large element beamwidth, and a phase centre that is stable with frequency, all of which are desirable for interferometer applications. (However, sinuous elements have a relatively large diameter, making them unsuitable for use in AESAs, where half-wavelength inter-element spacing is required to avoid grating lobes when beamforming. For example, the Randtron Antenna Systems (Menlo Park, CA, USA) Model 53640 sinuous antenna element [50] covers a wide frequency range of 2-18 GHz, but has a physical diameter of 6 cm. In an AESA, this would imply a minimum inter-element spacing of 6 cm, which would result in potential beamformer grating lobes for all frequencies above 2.7 GHz).  
Each antenna element had an associated tuner and digital receiver which captured a signal bandwidth of 400 MHz. The digitized signals from each receiver were then filtered with a bank of 32 MHz digital filters before detection processing to maximize sensitivity while minimizing the effects of external interference. The tuners were employed in a scanning architecture to cover all frequency bands of interest. The scanning process utilized _a priori_ information about emitter parameters and frequency concentration to optimize overall response time of the ES function.  
Determination of AoA was done with 14 of the antenna elements which formed an L-shaped pattern. This arrangement essentially provided two orthogonal interferometers to allow computation of both azimuth and elevation of an emitter signal. A RAM dynamically allocated these antenna elements to either frequency scanning or AoA determination tasks as required.  
As discussed in Section 2.2, implementation of the ES function without an AESA and the associated advantages of beamforming generally limited the system to detection of stronger emitter signals.
# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 3 Proposed Methodology
### Intelligent Prediction of Stuck Pipe Probability Based on LSTM  
#### 3.2.1 Establishment of Stuck Sample  
Firstly, it is necessary to determine the stuck date and time according to the mudlog data report, and then mark the normal sample and the stuck sample.  
Figure 5 shows the changes in hook tension and torque throughout the day on 2 May It can be seen that the time of stuck pipe is around 20:48:08 from the figure.  
As shown in Figure 6, it is the change of hook tension and torque from 20:11:28 to 21:21:28. During 20:50:28-20:51:28, the torque suddenly increases from 14.2 to 19.6 kN, which is consistent with the mudlog data report. Therefore, the accurate time of stuck pipe can be determined as 20:51:28.  
Figure 5: Hook tension and torque changes in the whole day on 2 May.  
Figure 6: Hook tension and torque changes from 20:11:28 to 21:21:28.  
The stuck pipe is a sudden accident, that is, the time from the beginning of the stuck pipe to the complete stuck pipe is very short, and sufficient time cannot be left to adjust the drilling parameters in time to avoid the stuck pipe. Therefore, it is necessary to mark the period before the stuck pipe as stuck pipe samples in order to achieve the purpose of forecasting in advance. The data of 20:28:28-20:50:28 are labeled as stuck pipe samples, and 20:11:28-20:27:28 are labeled as normal samples.
## 2 Feature Enhancement Point Cloud Data Resampling
### Coordinate System Conversion Based on Principal Component Analysis  
The aerial component point cloud data are mainly collected through the scanner, and the projection direction of the maximum data volume of point cloud data cannot be obtained directly. Considering the problem of point cloud data noise, we first solve the solution direction of the maximum data volume of point cloud data by robust principal component analysis [23] (RPCA), i.e., we obtain the projection plane and projection direction with the least data loss after projection of 3D data according to the maximum variance theory.  
Given the point cloud matrix \(P\), conventional PCA finds the optimal \(r\) rank matrix \(L\), which can be solved by SVD decomposition of the eigenvalues. For noisy point cloud data, the solved result will be very inaccurate. RPCA decomposes \(P\) into two parts \(P=L+S\), where \(L\) is the low-rank part and \(S\) is the sparse part, and the optimization objective of RPCA can be expressed by the following equation:  
\[\begin{split}\min_{L,S}&\text{rank}(L)+\lambda \
orm{S}_{0}\\ s.t.& P=L+S\end{split} \tag{10}\]  
Since the above equation has nonconvex and nonsmoothed properties in optimization, it can be transformed into a relaxed convex optimization problem, and the optimization of RPCA can be performed by methods such as the augmented Lagrange multiplier method.  
\[\begin{split}\min_{L,S}&\
orm{L}_{*}+\lambda \
orm{S}_{1}\\ s.t.& P=L+S\end{split} \tag{11}\]
# Prediction of Belt Drive Faults in Case of Predictive Maintenance in Industry 4.0 Platform
## 2 Belt Drive Diagnostics Approach
### Description of Test Stand  
In order to carry out tests allowing to collect appropriate measurement data, the GUNT [19] test stand was used, which enables the analysis of a belt drive that is working properly as well as a damaged one.  
The belt drive that was tested was a double belt drive with a belt tensioner for both V-belts (Figure 2).  
Two types of transmission belt were used for the test: one undamaged and one in which the mounting tooth was damaged (Figure 2). Each belt was placed on a test stand where the analysis was performed for the undamaged pulley and for the damaged pulley. Accordingly, four situations were identified that can occur during belt drive operation in production equipment. Fault analysis was performed in the frequency domain, where belt frequency(\(f_{R}\)) was calculated as follow:  
\[f_{R}=\frac{n}{t}.\frac{U_{AR}}{L_{R}}, \tag{1}\]  
where \(n\) is drive shaft speed, \(L_{R}\) is V-belt length, \(U_{AR}\) is drive roller circumference, and \(t\) is min to sec conversion factor (60).  
Figure 1: An overview diagram of the process of learning the correct device by the algorithm and on-line monitoring. Numbers 1 and 2 mean consecutive steps and “\(>\)2” system operation after initial configuration.  
Figure 2: Setup for simulation of vibrations from belt drive. It consists of drive unit (1), elastic claw coupling (2), short shaft (3), small belt pulley for belt drive with V-belt (4), reflective mark on the V-belt (5), magnetic clamp with steel plate (6), reference sensor aligned with reflective mark on V-belt (7), setting rings for axial attachment (8), bearing block (9), acceleration sensor 1 and 2 (10/11), and brake and load unit (12).  
For the purpose of conducting the tests, the following characteristic values for a belt drive were assumed: belt tension \(F_{t}\) = 130 N at a constant drive speed \(n\) = 1800 rpm, the load was equal to \(F_{L}\) = 2.0 Nm. Assuming \(L_{R}\) = 912 (mm), \(U_{AR}\) = 197.9 mm the unfault belt frequency (\(f_{R}\)) was equal to 6.5 Hz.  
To determine values during the test stage, vertical and horizontal acceleration sensors (piezo-electric sensor, model 603C01 from IMI SENSORS) with a scale of 100 mV/g and resonance frequency equal 25 kHz were used.