esa-sceva/satcom-chunk-collection huggingface dataset:
# New Generation of Electrochemical Sensors Based on Multi-Walled Carbon Nanotubes
###### Abstract  
Multi-walled carbon nanotubes (MWCNT) have provided unprecedented advances in the design of electrochemical sensors. They are composed by sp\({}^{2}\) carbon units oriented as multiple concentric tubes of rolled-up graphene, and present remarkable active surface area, chemical inertness, high strength, and low charge-transfer resistance in both aqueous and non-aqueous solutions. MWCNT are very versatile and have been boosting the development of a new generation of electrochemical sensors with application in medicine, pharmacology, food industry, forensic chemistry, and environmental fields. This work highlights the most important synthesis methods and relevant electrochemical properties of MWCNT for the construction of electrochemical sensors, and the numerous configurations and successful applications of these devices. Thousands of studies have been attesting to the exceptional electroanalytical performance of these devices, but there are still questions in MWCNT electrochemistry that deserve more investigation, aiming to provide new outlooks and advances in this field. Additionally, MWCNT-based sensors should be further explored for real industrial applications including for on-line quality control.  
Keywords:nanomaterials; multi-walled carbon nanotubes; synthesis methods; electrochemical properties; electrochemical sensors; electroanalysis; sensing applications +
Footnote †: journal: Applied Sciences
## 3 Results
In the first years (1994 to 1999), a similar percentage in MD and WD were found in players from Asia and Europe, with 40.4% and 44.9%, respectively, while in XD the percentage of European players was higher than that from other continents with 50.4% of the players. In the following years, from 2000 to 2009, the three modalities showed a comparable percentage of players from Asia and Europe with 44.6% and 42.7%. From 2010, the percentage of Asian players became higher in the different modalities, having more than 60% of the players in MD and WD and almost 60% of the players in XD, followed by the Europeans with -30%. American players represented only -7% and African and Oceania players only -3%. \begin{table}
\begin{tabular}{c c c c c} \hline  & & & **Decades** & \\ \hline
**Category** & **Age Brackets** & **1990s** & **2000s** & **2010s** \\ \hline \multirow{4}{*}{MD} & 14–20 years (\%) & 14.10 \(\dagger\) & 13.41 \(\dagger\) & 14.75 \(\dagger\) \\  & 21–25 years (\%) & 47.75 & 46.49 & 48.00 \\  & 26–30 years (\%) & 30.63 \(\dagger\) & 29.60 \(\dagger\) & 27.10 \(\dagger\) \\  & \textgreater{}30 years (\%) & 7.53 & 10.50 \(\dagger\)+ & 10.15 \(\ast\)+ \\ \hline \multirow{4}{*}{WD} & 14–20 years (\%) & 24.46 & 24.16 & 25.50 \\  & 21–25 years (\%) & 46.32 & 46.91 & 49.65 \(\ast\) \\  & 26–30 years (\%) & 22.83 & 23.58 & 20.64 \\  & \textgreater{}30 years (\%) & 6.39 & 5.35 & 4.21 \(\ast\) \\ \hline \multirow{4}{*}{XD} & 14–20 years (\%) & 17.48 \(\dagger\) & 16.26 \(\dagger\) & 14.89 \(\dagger\) \\  & 21–25 years (\%) & 45.87 & 48.83 & 51.73 \(\ast\) \\ \cline{1-1}  & 26–30 years (\%) & 28.31 & 26.97 & 25.96 \(\dagger\) \\ \cline{1-1}  & \textgreater{}30 years (\%) & 8.34 & 7.94 & 7.42 \(\dagger\) \\ \hline \end{tabular}
\end{table} 
Table 1: Frequency distribution of top 100 World Ranking badminton players for men’s doubles (MD), women’s doubles (WD) and mixed doubles (XD) according to their age in the last decades (1990s, 2000s and 2010s). \(\ast\) Different from 1990s decade at \(p<0.01\); \(\dagger\) Different from women’s doubles (WD) at \(p<0.01\).
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems
### Detectors  
Single-photon detectors must combine high performance with low cost and with the possibility of integration, for example in the same silicon photonics platform used for manufacturing other components. None of the currently available solutions meet these requirements, and developing radically new components will take many years. A pragmatic strategy is, therefore, to use currently available detectors to demonstrate the correct operation of QKD systems and protocols while waiting for new technologies to be developed. Figure 2 shows different solutions in terms of readiness, cost, operating temperature, degree of integrability, and efficiency for systems operating at 1550 nm. waveguide single-photon avalanche diodes (WG-SPADs) [77; 78] are very suitable for mass production and can be monolithically integrated into silicon photonic integrated circuits. WG-SPADs are built around a waveguide, so that light can be easily coupled from the optical circuit into the detector. They operate close to room temperature with obvious advantages in terms of cost. Er-doped silicon devices have been tested to evaluate the photocurrent response [79]. Ge-on-Si SPADs (Ge-SPAD) have a germanium absorption layer and share most of the advantages of WG-SPAD, but they operate at lower temperatures (about \(-50\)\({}^{\circ}\)C). Moreover, coupling light from the optical circuit to the detector is not as easy as with WG-SPAD [80]. Using III-V compounds to build a SPAD (III-V SPAD) is currently the most immediately available solution, whereby the integration on the photonic integrated circuit (PIC) is realized by molecular wafer bonding. Compared to Ge direct epitaxial growth on silicon avalanche materials, wafer bonding of III-V SPAD structures onto silicon requires further fabrication steps, such as substrate demounting. Also, electron injection (EI) detectors are fabricated by III-V materials, so they share the same limitations in terms of operating temperature and integrability, but they have no dead time and have the capability to resolve the number of photons simultaneously impinging on the detector [81].  
Figure 2: Quantum detector overview. (WG-SPAD = waveguide single-photon avalanche diodes).
## 2 Materials and Methods
### Assessment of Cytotoxicityality and Wound Healing Capacity  
A 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay was used to assess the viability of NP-exposed HaCaT, CCD966 SK, and Raw264.7 cells [15]. In brief, the cells were seeded (density: 3 \(\times\) 10\({}^{5}\) cells/mL) in 24-well plates and incubated at 37 \({}^{\circ}\)C under 5% CO\({}_{2}\) for 24 h. HaCaT and Raw264.7 cells were maintained in DMEM-FSP, whereas CCD966 SK cells were maintained in MEM-F. Subsequently, various Au-Ag alloy NP concentrations (20-140 mg/L) were added to the plates and incubated for 24 h. Then, the culture medium was discarded, the cells were rinsed, and fresh culture medium was added. Next, 10 \(\upmu\)L of 0.02% MTT solution was added to each well and incubated for 4 h. After the MTT solution was discarded and 0.1 mL of DMSO was added to solubilize the resulting crystals. Absorbance was measured at 570 nm by using an Epoch microplate reader (Winooski, VT, USA). Cell viability was calculated by comparing absorbance between the experimental and control groups.  
Wound healing capacity was assessed following the methods of [PERSON] et al. (2021) and [PERSON] et al. (2022) [16; 17], with slight modifications. In brief, HaCaT and CCD966 SK cells were seeded (density: 5 \(\times\) 10\({}^{5}\) cells/well) in 12-well plates and incubated for 24 h. Then, the cells were scratched with a stile tip and incubated with 0-25 mg/L concentrations of Au-Ag alloy NPs for 24 h to facilitate cell repair. Next, the cells were fixed with FineFIX solution and stained with toluidine blue O. Wound width was measured immediately after injury and after 24 h of repair by using a Lionheart FX automated microscope (BioTek Instruments Inc., Winooski, VT, USA) with Gen5 software (version 3). The degree of wound closure was quantified by measuring the scratch width of 30 wounded HaCaT cells and cell density within the wound area for 30 wounded CCD966 SK cells at both 0 and 24 h. A 25 mg/L MQLE concentration was used as a control treatment in this experiment.
# Relational Fisher Analysis: Dimensionality Reduction in Relational Data with Global Convergence +
## 3 Methodology
### Optimization of RFA
Therefore, Formula (10) can be transformed to  
\[\widetilde{\mathcal{L}}=\min_{\widetilde{\mathbf{W}}^{T}\widetilde{\mathbf{W} }=\mathbf{I}_{d}}\frac{\mathrm{tr}(\widetilde{\mathbf{W}}^{T}\widetilde{ \mathbf{S}}_{I}\widetilde{\mathbf{W}})}{\mathrm{tr}(\widetilde{\mathbf{W}}^{T} \widetilde{\mathbf{S}}_{p}\widetilde{\mathbf{W}})}, \tag{11}\]  
where \(\mathbf{V}=\widetilde{\mathbf{U}}\widetilde{\mathbf{W}},\widetilde{\mathbf{W} }\in\Re^{r\times d}\), \(\widehat{\mathbf{S}}_{I}=\widetilde{\mathbf{U}}^{T}\widetilde{\mathbf{S}}_{I} \widetilde{\mathbf{U}}\) and \(\widehat{\mathbf{S}}_{P}=\widetilde{\mathbf{U}}^{T}\widetilde{\mathbf{S}}_{P} \widetilde{\mathbf{U}}\). We can further rewrite the problem (11) as  
\[\widetilde{\mathcal{L}}=\min_{\widetilde{\mathbf{W}}^{T}\widetilde{\mathbf{W} }=\mathbf{I}_{d}}\frac{\mathrm{tr}(\widetilde{\mathbf{W}}^{T}\widehat{ \mathbf{S}}_{I}\widetilde{\mathbf{W}})}{\mathrm{tr}(\widetilde{\mathbf{W}}^{T} \widetilde{\mathbf{S}}_{I}\widetilde{\mathbf{W}})}, \tag{12}\]  
where \(\widehat{\mathbf{S}}_{T}=\widetilde{\mathbf{U}}^{T}(\widetilde{\mathbf{S}}_{I} +\widetilde{\mathbf{S}}_{P})\widetilde{\mathbf{U}}=\widehat{\mathbf{S}}_{I} +\widehat{\mathbf{S}}_{P}\). Since \(\widehat{\mathbf{S}}_{T}\) is positive definite, for any orthonormal matrix \(\widetilde{\mathbf{W}}\), Problem (12) satisfies that the denominator is positive. (2) **Efficient iterative optimization. ** The original trace ratio problem (12) can be rewritten as a trace difference problem:  
\[\widetilde{\mathbf{W}}^{*}=\operatorname*{argmin}_{\widetilde{\mathbf{W}}^{T} \widetilde{\mathbf{W}}=\mathbf{I}_{d}}\mathrm{tr}(\widetilde{\mathbf{W}}^{T}( \widehat{\mathbf{S}}_{I}-\widetilde{\eta}\widetilde{\mathbf{S}}_{T}) \widetilde{\mathbf{W}}), \tag{13}\]  
where \(\widetilde{\eta}\) is a parameter which can be calculated in the iterative process. In the iterative process, we first randomly initialize the target matrix \(\widetilde{\mathbf{W}}\) to be an arbitrary orthogonal matrix as \(\widetilde{\mathbf{W}}_{0}\in\Re^{r\times d}\), and then calculate \(\widetilde{  
method, we select some superior projection directions for each \(\widetilde{\mathbf{W}}_{t}\), as performed in [47]. Our selection criterion is  
\[\mathcal{L}=\min_{\mathbf{W}}\frac{\text{tr}(\mathbf{V}^{T}\mathbf{S}_{I} \mathbf{V})}{\text{tr}(\mathbf{V}^{T}\mathbf{S}_{P}\mathbf{V})}+\lambda\text{ tr}(\mathbf{V}^{T}\mathbf{V}), \tag{5}\]0  
where \(\Phi\) is a set of \(r\times d\) matrices with columns formed by eigenvectors of \(\widehat{\mathbf{S}}_{t}-\widetilde{\eta}_{t-1}\widehat{\mathbf{S}}_{T}\). We use the eigenvectors corresponding to \(d\) smallest ITR-score [48] to initialize the selection. Algorithm 1 specifically describes the iterative procedure of Problem (12). ```
1:Initialize: Initialize \(\widetilde{\mathbf{W}}\) as an orthonormal matrix \(\widetilde{\mathbf{W}}_{0}\in\Re^{r\times d}\) and Let \(\widetilde{\eta}_{0}=0\).
# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 3 Materials and Methods
### Fault Diagnosis  
One of the goals of this paper is to examine the impact that our devised adaptive imputation system has on ML-aided machine fault diagnosis. To do this, we employ the random forest (RF) classifier. RF is a powerful ensemble learning method that can be used for both classification and regression tasks. It functions by combining the outputs of multiple decision trees (DTs) to enhance the model's predictive accuracy and to avoid overfitting, an issue that is common in models using a single DT.  
During the training process for a RF model, multiple DTs are constructed using different subsets of the data and a randomly selected subset of features. This ensures that each DT in the forest is distinct, increasing the overall generalizability of the model. The DTs are not pruned, which allows the model to capture complex data patterns. When RF is being employed in a classification task, the output of the model is determined by a majority vote across all of the DTs.  
For fault diagnosis specifically, RF offers significant benefits due to its robustness against noise and its ability to handle large multidimensional datasets, which are typical when dealing with sensor data. RF comes with inherent feature selection capabilities that help manage multicollinearity and enhances the interpretability of the model by evaluating feature important. This is crucial in fault diagnosis, where understanding which sensor readings or operational parameters are most indicative of a fault can lead to more effective predictive maintenance strategies.
# Distributed Uniform Streaming Framework:
## 7 Conclusions  
In this paper, we demonstrated how we can create application components that can communicate with each other using an abstract middleware interface that is implemented in the DUST Core. The DUST Coordinators can then use these application components to optimize the distributed resource consumption. In our experiments, we showed that the coordinators can optimize the resource consumption but are only able to find local minima in distributed resource consumption. Finally, we demonstrated a real-world example of a system architecture that uses the DUST Core which creates platform transparency and enables software reusability.  
Future research could investigate a combination of a global search algorithm with the Contract Net Protocol. This would allow the global method, which is computationally complex, to find an optimal placement and the less computationally complex Contract Net Protocol to make changes during the calculations of the global method. Another research direction is the research on device failure because edge and fog devices are more susceptible to failures than cloud infrastructure. One method would be to recover from a device failure and move the application components towards another execution device to continue the application execution. Another method would be to try to predict when a device will fail and move the application components from that device before the device has failed (e.g., a device with a low battery). Because of the computational peaks that arise from event processing, the techniques described by [PERSON] et al. [8] could be combined with the Contract Net Protocol and a global search algorithm.  
Conceptualization, S.V., J.H., T.H., S.B., R.E., M.S., S.M. and P.H.; methodology, S.V., S.M., P.H.; software, S.V., J.H., T.H., S.B., R.E. and M.S.; validation, S.V. and J.H.; formal analysis, S.V.; investigation, S.V., J.H., T.H., S.B. and R.E.; resources, S.V.; data curation, S.V.; writing--original draft preparation, S.V.; writing--review and editing, S.V., J.H., T.H., R.E., S.M. and P.H.; visualization, S.V.; supervision, S.M. and P.H.; project administration, S.M. and P.H.; funding acquisition, S.M. and P.H.  
This research was funded by the Smart Highway project from Agentschap Innoveren en Ondernemen (VLAIO) grant number HBC.2017.0612.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAN & Controller Area Network \\ CNET & Contract Net Protocol \\ DUST & Distributed Uniform Streaming \\ IoT & Internet of Things \\ KPI & Key Performance Indicator \\ OBU & Onboard Unit \\ OEM & original equipment manufacturer \\ V2X & Vehicle-to-everything \\ QoS & Quality of Service \\ \end{tabular}
# Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array
## 4 DoA Estimation
### Signal Detection  
Knowledge of the number of signals U is a requirement in DoA estimation. Various techniques used for the computation of the number of signals (i.e., detection) have been given; therefore, readers are encouraged to visit [30] for details. Certain signal detection techniques estimate the subspace dimension from the eigenvalue matrix, for instance, by statistically testing the number of eigenvalues belonging to the noise space [31]. For partially correlated source signal detection, the best methods are model-based methods, e.g., WSF detection and the generalized likelihood ratio test [31]. These methods estimate the DoA and detect the number of U simultaneously. Furthermore, it is expected that these approaches work very well for correlated or coherent signal detection. This paper does not examine signal detection, but U is assumed to already exist as prior knowledge, just as in previous works on this topic, such as [2; 13; 36; 37; 38; 39].
## 2 Experimental Methods and Design
### Experimental procedure  
The experiment, which involved a substantial number of people, spanned six consecutive days from 15 September to 20 September 2023. Each day, the experiment was conducted during two time slots: from 2:00 p.m. to 6:00 p.m. and from 7:00 p.m. to 9:00 p.m. Participants were provided with information regarding the experimental protocol prior to the commencement of the experiment. Prior to commencing the experiment, the personnel equipped the participants with 64-bit electrode caps and simultaneously applied GT5 medical conductive paste to minimize impedance that may arise during the reading process; this ensures that the resistance of each electrode remains below 5 kilo-ohms. Following the EEG equipment setup, the subjects were required to wear VR all-in-one equipment. To achieve this, the See VR virtual reality eye-tracking device was used for eye calibration. The calibration process ensures that both eyes have a calibration rate of at least 80%. Once the subjects put on the experimental apparatus, they were allowed one minute to acclimate to the experiment. After ensuring that the subjects did not experience any noticeable discomfort, the video demonstration of design D1 commenced. During this demonstration, the experimenters assumed a comfortable seated position and were able to gradually rotate their heads for observation. The EEG waves were recorded using SAGA EEG analysis software (see Figure 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Details** & **Unit: Person** \\ \hline \multirow{3}{*}{Gender} & Male & 12 \\  & Female & 18 \\  & 20–24 & 16 \\ \multirow{3}{*}{Age Group} & 24–28 & 13 \\  & 29–30 & 1 \\ \multirow{3}{*}{Education Background} & Bachelor’s Degree & 3 \\  & Master’s Degree and Above & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Basic information about experimental subjects.  
Figure 3: Experiment process.  
Following the initial set of experiments, we promptly took away the participants' virtual reality (VR) devices within 30 s. Subsequently, the participants completed paper-based subjective questionnaires on the desktop within a one-minute timeframe, without any external interference in the experimental room during the scoring process. Following the completion of the scoring process, participants proceeded to a 5 min interval of relaxation, during which the personnel verified the proper functioning of the equipment. Once the patients finished resting and donned the VR equipment once more, the second set of situations was shown, concluding all the simulation studies. The entire experiment was carried out in a comfortable seated position to the greatest extent possible. To prevent interference from the external environment, all personnel not involved in the experiment were excluded. The experiment lasted around 35 min. To guarantee the precision and dependability of EEG signal extraction, novel participants utilized uncontaminated and dehydrated EEG caps, while other equipment was calibrated to the default values.
# Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
## 5 Construction of Collapsibility Evaluation Method
### Steps for Collapsibility Evaluation  
In summary, the collapsibility evaluation steps based on the four basic physical indicators were as follows:  
1. The target soil layer dry density value (\(\rho_{d}\)) was calculated using Equation (9).
2. The water content (\(w\)) and specific gravity (\(G_{s}\)) of the target aeolian sand layer were determined using basic physical property tests.
3. The water content, dry density, and specific gravity were substituted into Equations (10) and (11) to obtain the void ratio (\(e\)) and saturation (\(S_{r}\)): \[e=\frac{G_{s}}{\rho_{d}}-1\] (10) \[S_{r}=\frac{\omega\cdot G_{s}}{e}\] (11)
4. The collapsibility coefficient of the target aeolian sand layer was predicted based on the four physical parameters, i.e., the water content, dry density, pore ratio, and saturation.
5. Based on the collapsibility coefficient, the degree of collapsibility was evaluated according to relevant specifications [45; 46].  
This is a method for evaluating the collapsibility of aeolian sand based on physical indicators. However, the main problem lies in Step 4. Given that there is no clear mathematical relationship between the four parameters and the collapsibility coefficient, other regression algorithms must be used to establish the relationship between them. As the amount of experimental data was not large, there was a single dependent variable versus multiple independent variables in this study, and because of the fact that the SVM can solve machine learning problems under small-sample conditions, as well as simplifying common classification and regression problems, we attempted to use the SVM to establish a regression relationship between the four physical indicators and the collapsibility coefficient in this study.