esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 4 Conclusions and Future Work  
Early detection for asset monitoring is critical for efficient maintenance scheduling to save cost and preserve the quality of industrial process and outcome. In this study, a Blind Source Separation method utilising Spectral Kurtosis based Weiner filtering was implemented. Results indicate that the proposed approach can be reliable and effective in detecting bearing faults for early fault diagnosis. Given the promising result of the proposed technique, the authors will explore using the extracted feature as input to machine learning model such as SVM and neural network to develop an automated asset monitoring application. Indeed, machine learning techniques are very powerful in automating the thresholding based on labelled data which will allow achieving both early and automated detection of defects.  
Figure 11: SK features vector after application of change detection algorithm.  
Figure 10: SK feature for all signals plotted against the number of days of testing.  
**Author Contributions:** Conceptualization, S.K.H. and H.C.N.; investigation and methodology, S.K.H. and H.C.N.; validation, S.K.H. and H.C.N.; data analysis, S.K.H. and H.C.N.; writing--original draft preparation, S.K.H.; writing--review and editing, S.K.H., H.C.N., W.B., J.K. and T.-H.G.; visualization, S.K.H.; supervision, W.B. and T.-H.G.; project administration, H.C.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the UK's innovation agency, Innovate UK under grant agreement number 104505.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://i.iarc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing](https://i.iarc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing)]([https://i.iarc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing](https://i.iarc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#bearing)), accessed on 1 February 2021].  
**Acknowledgments:** The research has been undertaken as a part of the project entitled \"Artificial intelligence-based digital prescriptive maintenance of ships (DiMOS)\". The DiMOS project is a collaboration between the following organisations: VibteK Ltd., CMS services UK Ltd., TWI Ltd., and Brunel University London.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Application of a New Visible Light-Curing 3D Printing Platform
## 1 Introduction  
As modern weaponry and explosive technologies advance, traditional energetic material charging technologies are revealing their limitations. These traditional methods encompass the casting charging method, the pressure charging method, and the plastic charging method. Over time, the inefficiencies and shortcomings of these methods have become evident, especially during production.  
Many scholars try to apply 3D printing technology in the field of energetic material charge or solid propellant [1] from two aspects. On the one hand, they try to change the 3D printing position and improve the resin performance, including printing energetic material [2] at a specific position on the surface of thermal resin; using inkjet printing, attaching energetic molecules to the resin surface [3]; or improving the curing time of UV resin [4]. On the other hand, they try to improve the formulation of energetic slurry, which is improved in the manufacturing process of MEMS pyrotechnic products [5]: CL-20/EDF-11 energetic slurry [6; 7]; RDX/acetonitrile/methanol/tetrahydrofuran energetic slurry [8]; nanoscale RDX energetic slurry [9]; CAB/RDX energetic slurry [10]; CL-20/Plexiglass composite material [11]; and polyvinylidene fluoride/hydroxypypy methylcellulose/thertherrite energetic slurry [12]. One promising solution is visible light-curing charging technology, which is inspired by digital light processing 3D printing (DLP). This approach addresses issues such as high refractive index, limited penetration, and UV-related environmental issues by replacing UV with visible light. This light provides a higher level of safety; it has no harmful radiation and produces no ozone during irradiation, minimizing health risks [13]. Combining the above two methods, a new 3D printing platform using visible light curing was innovatively designed. This encompasses a visible light-curing lamp, a structural design for the machine, and a leveling device. The visible light-curing 3D printing platform is composed of the control module, conveying module, leveling module, and light source module. The forming diagram of the energetic drug column is shown in Figure 1.  
In trials, this machine was used to 3D print an energetic slurry formula made up of components like RDX, ammonium perchlorate (AP), aluminum powder (AI), and visible light-curing slurry. The formula (mass fraction) of visible light-curing slurry is 60.46% epoxy acrylate (EA)/polyurethane acrylate (PUA), 37.21% pentaerythitol triacrylate (PETA)/1.6-Hexaneditol diacrylate (HDDA)/butyl acrylate (BA), 0.47% camphorquinon (CQ), 0.93% dimethylryptamine (DMT), and 0.93% additive. The energetic powder column formulation (mass fraction) is 9.57% RDX, 57.45% AP, 11.7% Al, 21.28% visible light-curing slurry. Further studies were conducted to analyze the surface structure, compatibility, burn rate, and combustion consistency of the photocurable energetic powder column, aiming to understand its comprehensive properties better.
## 3 Results and Discussion
### Contamination Status Evolution  
Figures 6 and 7 show more relevant maps for the reconstruction of the contamination status of groundwater (PCE and TCE). Tetrachloroethylene (PCE) and Trichoroethylene (TCE) were certainly used in the past as degreasing solvents and constitute the primary contaminants. Each figure shows the values of concentrations at three time instants. The contour maps illustrate some of the phases of construction of the train station. These are compatible with the phases of aquifer response to possible sources of contamination. Concentration values are compared to the Italian threshold limits (CSC): the limits between each class correspond to multiples of the CSC value for each contaminant.  
CSC excesses are concentrated during working activities in two phases: preliminary excavation and excavation. Afterwards, the contamination levels decrease in terms of both areal extension and concentration. The general evolution shows how station processing activities clearly influenced the quality of groundwater. In fact, as soon as the preliminary excavation phase ends, there is a constant amelioration of groundwater quality. Moreover, during the postoperative period, there is an improved qualitative state and it is possible to identify the areas affected by significant residual contamination [55]. The increased concentration of chlorinated solvents during the excavation phases could be related to the remobilization of contaminants adsorbed on the solid soil matrix or partly present as trapped nonaqueous phase [56].  
Figure 6: Contour maps representing values of tetrachloroethylene (PCE) concentration in the (**a–c**) shallow and (**d–f**) intermediate aquifers at three time instants.  
Figure 7: Contour maps representing values of trichloroethylene (TCE) concentration in (**a–c**) shallow and (**d–f**) intermediate aquifers at three time instants.
## 3 Double-Layer Corridor
### Model Predictive Priority Contouring Control
#### 4.3.3 Cost Function  
The design of the cost function is particularly important to ensure the smooth operation of multi-robot systems. The cost function constructed in this paper mainly includes the deviation term and the input smoothing term.  
\[J=l_{1}J_{d}+l_{2}J_{s,p}+l_{3}J_{s,w} \tag{32}\]In Equation (32), \(l_{1}\), \(l_{2}\), and \(l_{3}\) are weight factors. \(J_{d}\) a is the cost of deviating from the reference paths. \(J_{s\_{=}v}\) ensures smooth velocity changes and \(J_{s\_{=}w}\) ensures smooth angular velocity changes.  
Referring to [27], the cost \(J_{d}\) is defined as the sum of contour error \(e^{c}_{m,k}\) and lag error \(e^{l}_{m,k}\). The contour error and the lag error are shown in Figure 6.  
\[J_{d}=\sum_{m=1}^{N}\left(\sum_{k=1}^{K}\|e^{c}_{m,k}\|^{2}+\|e^{l}_{m,k}\|^{2}\right) \tag{33a}\] \[e^{c}_{m,k}=\sin(\varphi_{m,k})\left(x_{m,k}-x^{initial}_{m,k} \right)-\cos(\varphi_{m,k})\left(y_{m,k}-y^{initial}_{m,k}\right)\] (33b) \[e^{l}_{m,k}=-\cos(\varphi_{m,k})\left(x_{m,k}-x^{initial}_{m,k} \right)-\sin(\varphi_{m,k})\left(y_{m,k}-y^{initial}_{m,k}\right) \tag{33c}\]  
In Equation (33), \(\left(x_{m,k},y_{m,k}\right)\) represents the \(k\)th trajectory point of robot \(m\). \(N\) is the number of robots and \(K\) is the number of reference path points.  
The input smoothing term \(J_{s\_{=}v}\) and \(J_{s\_{=}w}\) are given as follows.  
\[J_{s\_{=}v}=\sum_{m=1}^{N}\left(\sum_{k=1}^{K-1}\left(\left(v_{m,k+1}-v_{m,k} \right)/\Delta t\right)^{2}\right) \tag{34}\]  
\[J_{s\_{=}w}=\sum_{m=1}^{N}\left(\sum_{k=1}^{K-1}\left(\left(w_{m,k+1}-w_{m,k} \right)/\Delta t\right)^{2}\right) \tag{35}\]  
In Equation (34), \(v_{m,k}\) is the velocity of robot \(m\) at time \(k\) and \(\Delta t\) represents the time interval between two control signals. In Equation (35), \(w_{m,k}\) is the angular velocity of robot \(m\) at time \(k\).
# Optimization of Heat Pump Systems in Buildings by Minimizing Costs and CO\({}_{2}\) Emissions
## 3 Case Study
### Assumptions for Heat Pumps and Electric Heaters
#### 3.2.3 Capacity of Heat Pumps and Electric Heaters  
The maximum power of a heat pump is not constant and is based on the primary source temperature and the temperature of the heating water. In this analysis, it was assumed that the temperature of the heating water was constant at 55 \({}^{\circ}\)C. The primary heat source for GSHPs is the earth's ground, while air-to-water HPs use outdoor air, and hybrid HPs are a combination of a gas boiler and a heat pump, with the primary source being either the earth's ground or outdoor air. The majority of the HPs analyzed were ground source. Thus, the temperature of the earth's ground was required to calculate the change in power of these pumps. This temperature is influenced by factors such as the depth of the borehole, the temperature of the outdoor air, and the operating mode of the heat pump.  
In Lithuania, the temperature of the ground deeper than 7-10 m is relatively constant and reaches 7-9 \({}^{\circ}\)C, while the surface ground temperature without the effect of a heat pump collector can drop to 0 \({}^{\circ}\)C during the coldest months [43]. Due to limited land area in urban areas near apartment buildings, this study examined a vertical collector with a radial borehole consisting of 10 radial boreholes of 75 m each, with a total borehole length of 750 m. As a result, it was assumed that the temperature of the earth's ground depends solely on the operation of the heat pump. The temperatures of the earth's ground in different months, along with the average outdoor air temperatures for each month, are illustrated in Figure 5.  
The analysis of various types of heat pumps was conducted to evaluate their maximum heating capacity. Factors such as the average monthly temperatures and the heat pump's specifications [44; 45; 46; 47] for heating capacity dependency on the heating water temperature and primary source temperature were taken into account. For GSHPs, the maximum heating power was evaluated by using the average monthly ground temperature and assuming a constant heating water temperature of 55 \({}^{\circ}\)C. This enabled the determination of varying powers of the HPs for each month. In the case of air-to-water HPs, the maximum power change was determined by using data on average monthly outdoor air temperatures and specifications provided by the manufacture [44] for the heat pump's heating power dependency on the primary source temperature (outdoor air temperature) and the set indoor temperature (heating water temperature). This enabled the evaluation of varying powers of the HPs for each month, depending on the outdoor air temperature, in order to ensure a temperature of 20 \({}^{\circ}\)C in the buildings. On the other hand, the power of the analyzed hybrid HPs was found to be constant. The amount of heating energy produced by the HPs per month was calculated by multiplying the heating power of the heat pump by its working hours during the analyzed month.
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 5 Results and Discussion
### Gain  
The simulated 3D polar gain plot of the designed SWB antenna at the notch resonance frequencies of 2 GHz, 6 GHz, and 10 GHz is presented in Figure 12. Three sharp reductions  
Figure 11: Simulated and measured VSWR.  
Figure 10: Fabricated antenna showing (**a**) E-shaped stub, split elliptical slot, and C-shaped resonator on Rogers’s board (front view), and (**b**) elliptical ground plane (rear view).  
in the gain at about \(-9.9\) dB, \(-3.6\) dB, and \(-1.8\) dB in the notched bands at 2, 6, and 10 GHz, respectively, were perceived, which evidently proved the signal rejecting ability of the presented antenna.  
The gain obtained by simulating the model were proved to be in a positive agreement with measured results achieved from the fabricated prototype of the antenna. Figure 13 exhibits the variation of simulated and measured peak gain with frequency for the proposed antenna over the SWB frequency range (1.67-47.5 GHz). Abrupt gain reductions in the notched band could be anticipated because of elevated VSWR values at these frequencies, which resulted in low gain. At frequencies other than notched bands, the gain of the antenna was enhanced with frequency and increased from 1.7 dB to 10 dB. At notched  
Figure 12: Three-dimensional polar gain plot for the designed antenna at notch frequency (**a**) 2 GHz, (**b**) 6 GHz, and (**c**) 10 GHz.  
frequencies, the simulated gain was \(-9.9\) dB, \(-3.6\) dB, and \(-1.8\) dB, whereas the measured gain was \(-10.38\) dB, \(-4.45\) dB, and \(-2.08\) dB at 2 GHz, 6 GHz, and 10 GHz, respectively. (Note: The empirical results were recorded only up to 20 GHz because of the higher frequency constraint of the available VNA).
## 2 Materials and Methods
### Calibration of the Four-Point Probe  
The calibration of the four-point probe for EIS-based conductivity measurements, that is, determining the cell constant for transforming impedance spectra to conductivity, was performed using the HI70031 conductivity standard solution (Hanna Instruments Inc., Woonsocket, RI 02895, USA).  
After each set of experiments, the cell constant was determined by performing a calibration EIS scan in 40 mL of the standard solution which had been allowed to equilibrate to room temperature before. The temperature was measured using a Brymen BM235 Multicenter (Brymen Technology Corporation, Taipei, Taiwan) paired with a K type temperature probe, which has an accuracy of \(\pm 1\)\({}^{\circ}\)C. The conductivity of the HI70031 standard solution closest to the measured solution temperature, (\(\sigma_{Solution}\)), was read from the provided conductivity chart. At 21 and 22 \({}^{\circ}\)C, the provided conductivity values of the solution (\(\sigma_{Solution}\)) are \(0.1305\pm 0.0005\) and \(0.1332\pm 0.0005\) S/m, respectively. The frequency-dependent cell constant (\(K_{Cell}(f)\)) for the four-point probe was calculated according to  
\[K_{Cell}(f)=\sigma_{Solution}(f)\ \div\ G_{Solution}(f) \tag{4}\]  
Here, \"\(f\)\" designates the frequency. Subsequently, \(K_{Cell}(f)\) was utilized to calculate the conductivity of biological samples (\(\sigma_{Sample}(f)\)) [65] as  
\[\sigma_{Sample}(f)=K_{Cell}(f)\cdot G_{Sample}(f) \tag{5}\]  
To compare the relative conductivity change (\(\Delta\sigma_{rel}(f)\)) following PEF treatment with respect to an untreated control region with conductivity (\(\sigma_{Ctrl}(f)\)), a relative conductivity change (in percent) was calculated as follows:  
\[\Delta\sigma_{rel}(f)=\frac{\sigma_{Sample}(f)-\sigma_{Ctrl}(f)}{\sigma_{Ctrl}( f)}\times 100 \tag{6}\]Similarly, the permittivity (\(\epsilon_{Sample}(f)\)) and relative permittivity change (\(\Delta\epsilon_{rel}(f)\)) of biological samples were calculated as follows:  
\[\epsilon_{Sample}(f)=B_{Sample}(f)\cdot K_{Cell}(f)\ \div(2\pi f\cdot\epsilon_{0}) \tag{7}\]  
\[\Delta\epsilon_{rel}(f)=\frac{\epsilon_{Sample}(f)-\epsilon_{Ctrl}(f)}{\epsilon _{Ctrl}(f)}\times 100 \tag{8}\]
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 3 Mathematical Model
### Extension of the Objective Function for Fairness  
The objective function given in Equation (5) does not consider fairness but is only targeted at maximizing the aggregate throughput. In this section, the objective function is extended for the different fairness methods BSF, BFF and NFF. The formulations for the constraints specific for the different allocation methods are given in the Appendix A.2.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Comparative Experiment Results and Analysis
#### 3.3.1 Analysis of Massachusetts Roads Dataset Results
The superior IoU score for PCCAU-Net suggests its proficiency in delivering more accurate road segmentation, a critical aspect for applications demanding high precision. In summary, PCCAU-Net manifests consistent superiority or near-top-tier performance across three key metrics, thereby substantiating its multifunctionality and robustness in high-resolution satellite imagery for road extraction. This exemplary performance validates the effectiveness of the attention mechanisms integrated within the DCA module, establishing PCCAU-Net as the preferred model for sophisticated road extraction tasks.
## 5 Discussion
### Water-Rock Interaction and Mixing Model
Groundwater in the PNA evolved from site 10 (initial water-1) to site 84 (final water), and then from site 84 (initial water-1) to site 90 (final water). The model converges via the dissolution of calcite, gypsum halite, and silica; de-dolomitization; precipitation of albite and anorthite; some cation exchange; and an evaporation factor ranging from 1.05 to 1.22. To simulate the recharge and mixing from Nubian groundwater toward the other two aquifers, eleven model scenarios were converged for the Quaternary aquifer and three models for the Post-Nubian aquifer. To simulate upward leakage from the NSA toward the Quaternary groundwater aquifer located upstream, Nubian groundwater sites 8, 94, 95, 24, 103, and 101 were used as initial water-1, rainwater was used as initial water-2, and Quaternary groundwater sites 9, 17, 18, 19, 51, 53, and 54 were used as final waters in the NETPATH model. Model results for the shallow Quaternary groundwater aquifer located upstream of the Qena basin close to the confined NSA primarily indicated dissolution of calcite, gypsum, dolomite, halite, chlorite, and albite, and that clay minerals (illite, Ca-montmorillonite) and alunite were formed (Table 4). The estimated mixing percentages from the Nubian toward the Quaternary groundwater ranged from 21.4 % to 87.2 %; the recharge percentages from rainwater ranged from 78.6 % to 12.8%. The mixing NETPATH model results suggest that the downstream Quaternary alluvial groundwater aquifer located in the Wadi Qena delta area is mainly recharged from canal water (River Nile branches). The calculated mixing ratio ranged from 48% from original groundwater that comes from the upstream watershed represented by site 44 (final water at site 45) to 85.5% from Nile water (final water at site 28). The calculated mixing percentage from  
Figure 10: Schematic cross-section showing the geochemical processes controlling groundwater quality based on NETPATH geochemical model results reported in Table 4.  
Nubian to Post-Nubian groundwater ranged from 82 % to 93.5 % of Nubian groundwater, whereas the rainwater amount ranged from 6.5% to 18 %. Calculated mineral saturation indices (SI; Table 5) were consistent with changes in mineral phases, where the minerals that had negative saturation indices were dissolved and indicated by positive (+ve) mass transfer values in the NETPATH model, with the exception of de-dolomitization driving the dissolution of dolomite despite over-saturated SIs [60].
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 4 Through Metal Communication System  
Figure 14 shows the fully integrated communication system for a through metal application. Here, two distinct subsystems can be identified: outside, and inside subsystems. The outside subsystem will wirelessly transfer power to the inside subsystem through the metal plate to enable its operation. Bidirectional communication is possible using only the  
Figure 12: Relative harvested power through stainless steel plates to through a 1 mm-thick aluminum plate versus input power. The frequency is 2 kHz.  
Figure 13: Harvested power versus input power using aluminum plates of different thickness. The frequency is 2 kHz.  
harvested power from outside. There are no batteries used inside the metal barrier. Only one pair of miniature coils is used for both energy transfer and wireless communications.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 2 Methodology
### Weight Matrix  
The weight strategy is shown in Equation (5). The satellite observation adopts an elevation angle weighting strategy, that is, the weight of satellite carrier phase observation with an altitude angle greater than 30\({}^{\circ}\) is set to 1, and the satellites with an altitude angle less than 7\({}^{\circ}\) are deleted. The pseudorange weight is set to 1/10,000 of the carrier phase observation weight according to experience. Considering the ranging accuracy of 5G TOA, we set the weight of 5G observation value to 1/100 of the carrier phase weight, as shown in Equations (6)-(8).  
\[W=\frac{{\sigma_{0}}^{2}}{\sigma^{2}}, \tag{5}\]  
\[{\sigma_{\Phi}}^{2}=\left\{\begin{aligned} \sigma_{0}^{2},\theta \geq 30^{\circ}\\ \frac{{\sigma_{0}}^{2}}{(2\sin\theta)^{2}},\theta<30^{\circ}\, \end{aligned}\right. \tag{6}\]  
\[{\sigma_{P}}^{2}=10^{4}*{\sigma_{\Phi}}^{2}, \tag{7}\]  
\[{\sigma_{T}}^{2}=10^{2}*{\sigma_{0}}^{2}. \tag{8}\]  
where \(W\) represents the weight; \(\theta\) represents the elevation angle of satellite; \({\sigma_{0}}^{2}\) represents the variance of unit weight; \(\sigma^{2}\) represents the variance of the observations; the other symbols have the same meaning as before.