esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a UQPSK-based ICRS in the Master/Slave mode for distributed spacecraft networking is proposed, and the non-interfering ranging and communication are realized simultaneously. In our scheme, a high-precision ranging GNSS-like signal is adopted in the I branches, while a high-rate communication signal is utilized in the Q branches. By selecting a suitable PWDF, the ranging performance and communication performance are balanced. The simulation results imply that both performances attain an acceptable situation under the condition of the PWDF ranging from 10 to 20 dB, which is in accordance with the factor in the TDRSS selected as 10 dB. When applied to a realistic distributed spacecraft network, UQPSK-based ICRS can directly attach the communication function to the existing navigation system, rather than constructing a new system, which is a promising approach in celestial and terrestrial networks.  
Conceptualization, N.Z. and Q.C.; methodology, N.Z. and H.W.; software, N.Z. and Z.Z.; validation, Q.C., N.Z., H.W. and Z.Z.; formal analysis, N.Z. and H.W.; investigation, N.Z.; resources, Q.C.; data curation, Z.Z.; writing--original draft preparation, N.Z.; writing--review and editing, N.Z.; visualization, N.Z.; supervision, Q.C.; project administration, H.W.; funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 61471021).  
The author would like to thank the editor and anonymous reviewers for their thoughtful comments and suggestions.  
The author declares no conflict of interest.  
Figure 12: Clock offset between the two satellites.
# Combined Improved CEEMDAN and Wavelet Transform Sea Wave Interference Suppression
## 1 Introduction
This paper focused on a novel method of sea wave interference suppression based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform (WT). This novel method is based on frequency, and it can separate interference signals of different frequencies from communication signals. According to the radar echo model of superimposed sea wave interference, it is proved that the WSAW and the sea wave interference can be extracted by the phase signal so as to separate the WSAW signal. The performance of this novel method based on improved CEEMDAN and WT is compared with the reference filtering method. We attempted to verify the preferable performance of this novel method by means of theoretical derivation and experiment. The remainder of this paper is organized as follows. In Section 2, the radar echo model of superimposed sea wave interference is demonstrated. Novel sea wave interference suppression based on improved CEEMDAN and WT is proposed in Section 3. The interference suppression results and communication performance of the novel method and reference method are presented in Section 4. Section 5 concludes this paper.
## 3 Simulations and Measurements
### Objective Evaluation of the Algorithm Using Two Intelligibility Models
The intelligibility score of R1, because of lower \(T_{60}\), is higher than others and therefore this score may reach near to its maximum possible value such that more improvement is not possible specifically for high SNRs near 0 dB (third-row panel in Figure 5a). For room R4 with a long reverberation tail, the model, because of high amount of time smearing, does not show much improved scores, specifically for more noisy conditions with SNR = \(-\)10 and \(-\)15 dB. The same evaluation using mr-GPSM is shown in Figure 5b, now using PN. The model shows lower scores for the PN in comparison to SSN and also lower improvements caused by the preprocessing algorithm. Generally, the model predictions show less than 1 dB improvement for OE and about 2 dB improvement using the proposed method. In spite of lower values of improvements, the curves of Figure 5b show a consistent increase of speech intelligibility using the OE and the proposed approaches. The intelligibility prediction models are also applied to the robustness evaluation scenarios. In Figure 6, the STOI scores similar to Figure 4 show an improvement of intelligibility for the proposed method in comparison to the unprocessed and OE-enhanced signals. Figure 5: (**a**) The predictions of mr-GPSM in the presence of SSN for three SNR values. The intelligibility scores (SNR-mr-GPSM) show up to 5 dB for OE and up to 15 dB improvement in comparison to the unprocessed signal in low SNR = \(-\)15 dB (first-raw panel). The improvement is seen for other SNRs, specifically in rooms R2 and R3. The model shows lower improvement for preprocessed signals in rooms R1 and R4. ; (**b**) The predictions of mr-GPSM in the presence of pink noise (PN) for three SNR values. The intelligibility scores (SNR-mr-GPSM) show less than 1 dB improvement for OE and about 2 dB improvement using the proposed approach. In Figure 7, the SNR-mr-GPSM scores for evaluation of robustness of the proposed algorithm are compared with the unprocessed and OE-enhanced signals. The improvements are in the range obtained for the main scenarios in Figure 5.  
In Figure 7a, the maximum improvement predicted by the mr-GPSM in the presence of SSN for the OE-preprocessed signal is 3 dB and for the proposed approach it is about 9 dB. For the PN, a similar tendency can be seen in the three panels of Figure 7b. The data show an overall improvement of 3 to 4 dB for the proposed method depending on the scenario. This improvement is sometimes better than the main scenario for SSN and PN. This underlines that the proposed algorithm is very robust against changes in listener position and that detailed knowledge of the IRs is not essential. The intelligibility score is more dependent on the listening scenario and was sometimes better than the main scenario for which the weights were calculated because of more binaural advantage caused by more azimuthal separation of the target and noise sources.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Biodegradable Graphene ECG Electrodes
#### 2.3.1 Ultra-High-Skin-Conforming and Biodegradable Graphene ECG Electrode  
Polyvinyl alcohol (PVA) is a water-soluble and biodegradable organic material very suitable for flexible and environmentally friendly wearable electronics [55]. Since it does not conduct electricity, it is usually used as a substrate for conducting materials. [PERSON] et al. proposed a PVA/graphene flexible ECG electrode fabricated via double transfer [56] (Figure 2e). Compared with previous techniques that require heating and etching, the new process is efficient and low-cost. The resulting graphene electrode has high flexibility ([PERSON]'s modulus of 8.598 MPa) and can detect ECG signals with a high signal-to-noise ratio (60 dB). The ECG electrode can simply be delaminated by water in 150 s, which offers a good solution for biodegradable wearable electronics.
## 3 Results
### Characterization of the Coated Scaffolds
#### 3.1.3 Microstructure by ESEM  
For the detection of the collagen coating of the PCL scaffolds, they were cryo-broken before the ESEM investigations. Thus, cross-bracing from the center of the scaffold could be investigated. The collagen coating of the PCL scaffolds was made visible by the ESEM investigations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Length [mm]** & **Width [mm]** & **Height [mm]** & **Weight [mg]** & **Strand Width [\(\upmu\)m]** & **Strand Spacing [\(\upmu\)m]** \\ \hline
8.53 \(\pm\) 0.03 & 8.55 \(\pm\) 0.03 & 2.08 \(\pm\) 0.02 & 74.27 \(\pm\) 3.39 & 234.7 \(\pm\) 13.91 & 258.75 \(\pm\) 9.62 \\ \hline \end{tabular}
\end{table}
Table 2: Size, weight and strand width of the 3D printed PCL scaffolds (\(n=15\)).  
Figure 1: Overview of the 3D printed Scaffolds; (**a**): CAD model; (**b**): two layers of the 3D printed scaffold, image taken directly while printing from the Envisiontec 3D Bioplotter; (**c**): 3D Laserscan Image of the scaffolds; (**d**): 3D reconstruction of the 3D lasaerscanning microscopy data.  
The second goal of the ESEM investigations was to visualize the different cell types on the surface of the scaffolds.  
Figure 2(c) shows the MG-63 cells on the surface of the PCL scaffold. It can be seen that the cells arrange themselves relatively regularly. Whereas the MLO-Y4 cells show a different behavior (especially on the right side of Figure 2(d)). The distance between the cells is larger and the cells partly migrate under the collagen coating. The MSCs also adhere well to the surface and even interact with the collagen coating (see Figure 2(e) white arrow). Using ESEM and image analysis with ImageJ (FIJI modification), a thickness of \(2.088+0.39\)\(\upmu\)m was determined for the Collagen I coating. In addition, it was determined that the Collagen I coating has a uniform thickness over the entire scaffold.  
Figure 3: _Cont._  
Figure 2: Comparison of surface roughness of (**a**): uncoated (**b**): collagen coated PCL scaffolds; images taken with KEYENCE VK-X210 3D Laserscanning microscope.
## 2 State of the Art
### The Problem Tackled  
In the original conceptual idea, detectability aimed to quantify the ability to detect a particular failure mode and did not explicitly measure the ability to mitigate that failure mode once it was detected. Therefore, it was traditionally assumed that existing mitigation capabilities were sufficient to minimize or eliminate a failure mode. However, in a given risk scenario, mitigation capabilities may be insufficient or may change due to unexpected events.  
Intuitively, these two concepts have been combined in the traditional variable Detectability, which can lead to ambiguous results, i.e., overly conservative or non-conservative risk assessment estimates. As with Severity, which includes multiple sub-variables, Detectability ratings in traditional FMEA are also ambiguous because detection and mitigation are considered together without explicitly assessing mitigation capabilities for a given failure mode and respective risk scenario.  
This paper aims to improve the FMEA approach by splitting the traditional risk variable of Detectability in FMEA analysis into Detectability and Mitigation variables. The Mitigation variable aims to remove the ambiguity of traditional Detectability assessments by including mitigation capabilities in the risk assessment. In this way, it becomes possible to improve the effectiveness of the FMEA approach and ultimately improve risk analysis in innovation.  
In addition to this goal, a model should be developed that not only improves the prioritization of failure modes, but that also retains the simplicity and ease of use of the traditional FMEA approach to meet the risk assessment and risk management needs that arise in practice.
## 3 Results
### Comparative Study  
Figure 6 provides a comparison of the three methods, population-specific, category-specific, and patient-specific, investigated in this paper. The performance metrics employed to assess the obtained results are given in Table 3.  
The maximum MAE is observed for the population-specific method (0.7037), while the minimum is achieved by the patient-specific method (0.1004). The population-specific method reveals the maximum MSE (0.6939), while the minimum is attained by the patient-specific method (0.0256). The minimum \(R^{2}\) is observed for the population-specific method (0.376), and the maximum is achieved by the patient-specific method (0.872).  
These results indicate that building the LSTM architecture for each patient leads to better predictive performance, as evidenced by lower MAE and MSE, and higher \(R^{2}\) compared to the population-specific and category-specific methods.  
The highly significant \(p\)-value of \(7.16776\times 10^{-21}\) shows that the patient-specific method significantly improves \(R^{2}\) values, enhancing model estimation capability. Similarly,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Population-Specific** & **Category-Specific** & **Patient-Specific** \\ \hline MAE & 0.4139 \(\pm\) 0.05 & 0.38115 \(\pm\) 0.04 & 0.36325 \(\pm\) 0.03 \\ MSE & 0.332 \(\pm\) 0.04 & 0.280 \(\pm\) 0.03 & 0.222 \(\pm\) 0.02 \\ \(R^{2}\) & 0.594 \(\pm\) 0.07 & 0.606 \(\pm\) 0.06 & 0.722 \(\pm\) 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Median values for MAE, MSE, and \(R^{2}\) for different methods.  
Figure 6: Boxplot representation of the performance metrics for each method.  
the \(p\)-values for MSE (0.0125) and MAE (0.0012) also indicate statistically significant differences among the methods, with the patient-specific method resulting in better estimations. The result confirms that the patient-specific method provides the best estimation accuracy; however, due to its intensive nature, this method requires significant computational time. In Table 4, the computational time needed by each method to find the LSTM model architectures is presented.  
In the population-specific method, the selection of the LSTM architecture occurs within an hour, highlighting the efficiency of the method in minimizing time requirements. Conversely, the category-specific method requires 53 h through five runs of the algorithm, each corresponding to a distinct correlation category. The patient-specific method requires significantly higher computation time, i.e., 130 h. The need to iterate individually over 70 patients raises notable concerns, particularly in real-time applications.  
The results demonstrate that, while the population-specific method offers a swift convergence to an LSTM architecture (i.e., 1 h), it tends to show higher MAE and MSE values, along with a lower median \(R^{2}\). The patient-specific methods show the best estimation accuracy; however, the extended computational time needed limits the real-time application of this method. The category-specific method is an intermediary option, effectively balancing computational efficiency (53 h) and individualization.
## 3 State Evaluation and Fault Prediction of Protection System Based on Digital Twin Technology
### Fault Prediction of Protection System Based on Clustering Algorithm
#### 3.3.1 Common Fault Sets in Protection Systems
With the development of smart grids and the continuous upgrading of electrical equipment, the fault set in this research method is not limited to the above nine selected faults, which can be increased or decreased according to the actual situation. This method can also be applied to other electrical equipment, such as measurement and control devices, communication devices, and distribution automation devices. It is only necessary to combine the specific equipment types and organize the characteristic quantities and fault sets during their operation according to the method described in this paper.
## 1 Introduction  
Marine mussels (_Mytilus_ spp.) have been successfully used over the past fifty years as sentinel organisms for monitoring metal contamination along marine coasts [1; 2; 3; 4; 5; 6; 7; 8; 9]. The reason for this is that this mollusk genus is geographically widespread in sub-boresian and temperate environments and it concentrates metals in its soft tissue in proportion with the concentration in surrounding waters [10; 11; 12]. Thus, trace metal concentrations in the soft tissue of the blue mussel reveal the contamination level of the waters of its environment. Applied to the monitoring of temporal and geographical trends of chemical contamination, this approach has been named \"Mussel Watch\", and has been adopted as one of several coastal environmental quality-monitoring strategies by United Nations programs [3; 13]. However, biological factors related to mollusk growth rate also control metal uptake and excretion and must be taken into account in order to optimize the use of mussels as sentinel organisms [4; 10; 14; 15; 16]. Several Mussel Watch programs have been carried out to monitor trace metal contamination along the eastern coasts of Canada and US [8; 17; 18; 19], and especially mercury (Hg) contamination in the Estuary and Gulf of St. Lawrence [1; 20; 21]. These programs have not been maintained over more than a few years and, consequently, temporal trends are not as well documented. Nevertheless, they constitute baselines against which future assessments can be compared [22].  
Here, we present the results of a Hg survey carried out in the summers of 2016, 2018, and 2019 in the St. Lawrence Estuary and Gulf, forty years after the first Mussel Watch was completed in this area. We used a normalization model to minimize the effects of biological factors on the Hg content of the mussel soft tissues. Our observations suggest that the amount of Hg bioavailable to marine mussels in the study area is similar to what it was 40 years ago. Sub-regional Hg distribution patterns indicate a gradual decrease of Hg content in mussels downstream from the main freshwater tributary (St. Lawrence River) to the St. Lawrence Estuary.
## 2 Materials and Methods
### ELISA for VEGF  
The effect of CHSP on VEGF protein secretion was analyzed by ELISA with a Quantikine Human VEGF Immunoassay kit (R&D Systems, Minneapolis, MN, USA), targeting VEGF165 in the cell culture supernatant. OVACAR-3 cells (6 \(\times\) 10\({}^{5}\)) were seeded in 60-mm cell culture dishes and grown for 16 h at 37 \({}^{\circ}\)C before treatment with various concentrations of CHSP (5-40 \(\mu\)g/mL) or DMSO (as vehicle) for 24 h. Culture supernatants were collected for the VEGF assay. The inhibition of VEGF protein secretion was expressed as a percentage of the control. Cell lysates were also assayed for total protein levels using a BCA protein assay kit (Pierce, Rockford, IL, USA) to adjust VEGF levels.
## 2 The Formation, Characteristics, and Composition of Landfill Leachate
### Composition of Landfill Leachate  
Landfill leachate varies widely in composition according to the age of the landfill as well as the type and the mixture of waste and the contents of these wastes. Usually these wastes contain dissolved, non-dissolved, and suspended materials [35]. Landfill leachate typically manifests as a deeply pigmented liquid exhibiting pronounced malodorous properties, comprising a complex mixture of elevated concentrations of both organic compounds and inorganic constituents [57]. Landfill leachate generally comprises four principal constituent groups [58]: (1) dissolved organic matter (DOM) (100-10,000 mg/L), encompassing volatile fatty acids (VFAs) and aquatic humic substances (AHSs) such as humic and fulvic acids; (2) heavy metal ions including Cu\({}^{2+}\), Mn\({}^{2+}\), Cd\({}^{2+}\), and Ni\({}^{2+}\) (\(<\)2 mg/L); (3) xenobiotic organic compounds comprising phenolic derivatives, aliphatic alcohols, aldehydes, pesticides, and related compounds (0.1-500 ug/L); and (4) macro-inorganic species such as NH\({}_{4}\)+, Na\({}^{+}\), Ca\({}^{2+}\), Fe\({}^{2+}\), and HCO\({}_{3}\)- (10-6000 mg/L). Notably, emerging contaminants of global concern (ECGCs) in leachate matrices have been increasingly identified through advanced analytical techniques, including endocrine-disrupting compounds (EDCs), polybrominated diphenyl ethers (PBDEs), and per- and polyfluoroalkyl substances (PFASs) (0.1-292 ug/L). These ECGCs exhibit distinct environmental persistence characteristics, demonstrating resistance to natural degradation through their inherent chemical stability, bioaccumulation potential, and high environmental mobility [59]. Table 2 shows the composition of landfill leachate.  
\begin{table}
\begin{tabular}{c c c} \hline
**Number** & **Parameters** & **Values (mg/L)** \\ \hline
1 & pH & 2.69–8.11 \\
2 & BOD\({}_{5}\) & 98–50,000 \\
3 & COD & 140–157,200 \\
4 & TOC & 3963–78,640 \\
5 & NH\({}_{3}\)-N & 400–6000 \\
6 & TN & 395–5332 \\
7 & Calcium (Ca) & 69–2330 \\
8 & Potassium (K) & 28–1700 \\
9 & Magnesium (Mg) & 4–780 \\
10 & Sodium (Na) & 85–3800 \\
11 & Lead (Pb) & 0.001–5 \\
12 & Arsenic (As) & 0.0001–0.578 \\
13 & Chlorine (Cl\({}^{-}\)) & 47–6000 \\
14 & Sulphate (SO\({}_{4}\)\({}^{2-}\)) & 0.01–3240 \\
15 & Volatile suspended solids & 800–11,320 \\
16 & Total Suspended Solids & 930–13,333 \\
17 & Volatile phenol & 1.3–800 \\
18 & Total hardness & 15–2241 \\ \hline \end{tabular}
\end{table}
Table 1: Typical characteristics of landfill leachate [49; 54; 55; 56].
## 4 Analysis Results
### Local Deformation of Adhesive at joints  
Deformation concentration at the edge of adhesive under weak-axis bending causes reflective cracks on the asphalt pavement [11]. In this study, the local deformation along the deck surfaces under design wheel loads is evaluated by FE analyses for SLS and ULS. Figures 11 and 12 show the distribution of the weak-axis tensile strains along the bottom and the top surfaces of the deck under LC1 and SLS for FX and SS, respectively. Figures 13 and 14 show the distribution of weak-axis tensile strains at the bottom and top surfaces of the deck under LC2 and FX and SS, respectively. The weak-axis tensile strains are much larger under ULS with similar trends to those in SLS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Loading Configuration**} \\ \cline{3-6}
**Serviceability** & & \multicolumn{2}{c}{**LC1**} & \multicolumn{2}{c}{**LC2**} \\ \cline{3-6}
**Limit State** & & \begin{tabular}{c} **Maximum** \\ **Deflection (mm)** \\ \end{tabular} & \begin{tabular}{c} **Span/Maximum** \\ **Deflection (L/\(\Delta\))** \\ \end{tabular} & \
\begin{tabular}{c} **Maximum** \\ **Deflection (mm)** \\ \end{tabular} &
\begin{tabular}{c} **Span/Maximum** \\ **Deflection (L/\(\Delta\))** \\ \end{tabular} \\ \hline Boundary & FX & 7.19 & 348 & 5.08 & 492 \\ condition & SS & 9.34 & 268 & 6.84 & 366 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Maximum deflections of the deck with different boundary conditions and loading configurations under SLS.  
Figure 10: Deflections along the strong axis.  
Figure 11: Weak-axis tensile strains along the (**a**) top surface and (**b**) bottom surface of the deck under FX and LC1 in SLS.  
Figure 12: Weak-axis tensile strains along the (**a**) top surface and (**b**) bottom surface of the deck under SS and LC1 in SLS.  
Figure 13: Weak-axis tensile strains along the (**a**) top surface and (**b**) bottom surface of the deck under FX and LC2 in SLS.  
As shown in Figures 11-14, the weak-axis tensile strains along the bottom and the top surfaces of the deck highly concentrate at the adhesives near the loading area, as described by [PERSON] et al. [11]. This is caused by the local deformation of the deck section and relatively low stiffness of the adhesive. The highly concentrated tensile strains at the adhesive cause adhesive tensile failures and reflective cracks on the asphalt pavement. To avoid these problems, it is necessary to reduce the strain in the adhesive by selecting adhesives with higher stiffnesses or optimizing the joint configuration.  
Table 12 summarizes the maximum weak-axis tensile strains at the adhesive calculated by FE analyses. Applying the partial safety factor (2.34) to the maximum tensile strain of the adhesive (7.0%) described in Table 9, the design limit of the tensile strain is determined as 3.0%. The maximum weak-axis tensile strains in the adhesive under SLS and ULS are lower than the design limit of tensile strain. The stress in the adhesive needs to be checked for safety, as described in Sections 4.3-4.5.