esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Insulators are the basis of the stable operation of transmission lines, and it is particularly important to regularly detect the insulation performance of insulators. After several years of research, contactless space electric field detection technology for degraded insulators, based on an unmanned aerial vehicles (UAV) platform, has developed rapidly. However, the existing technology is only limited to the typical vertical/horizontal state research category and cannot apply to complex operating conditions such as attitude disturbance and path tilt. This paper builds a simulation model of insulators at a voltage level of 220 kV by COMSOL finite element software and analyzes the space electric field distribution characteristics of degraded insulators and the influence of different surface conditions, attitude disturbance, path tilt, etc., on the space electric field distribution of insulators. To reduce the error of UAV detection under complex operating conditions of insulators, a method for detecting the electric field of degraded insulators based on sensitive insulators is proposed and verified by experiments. According to the research results, surface conditions of insulators have little influence on their space electric field, while the attitude disturbance and path tilt of UAVs have a great influence on the electric field of insulators. Sensitive insulators at a level of voltage below 220 kV are generally located at the low-voltage end. The principles of sensitive insulators are combined with the space electric field measurement method to measure the space electric field distribution, thus effectively judging whether there are degraded insulators in the string. The method specified in the paper greatly improves the accuracy and detection efficiency of the contactless electric field detection of insulators, which is of great significance for the promotion of the electric field detection method of insulators in practical applications.  
complex working conditions; sensitive insulators; electric field of insulators; non-contact electric field detection +
Footnote †: journal: Applied Sciences  
1
Footnote 1: _Applied Sciences_
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 3 Materials and Methods  
### Chemicals  
Analytical grade volatile phenols (guaiacol, 4-methylguaiacol, 4-ethylguaiacol and 4-ethylphenol), hexamethylene diisocyanate (HDI), \(N\),\(N\)-dimethylformamide (DMF), dibutyltin dilaurate and deuterated NMR solvents (\(d_{6}\)-ethanol, D\({}_{2}\)O and DCI) were purchased from Sigma-Aldrich (Castle Hill, NSW, Australia). Analytical grade chloroform was purchased from Chem-Supply (Adelaide, SA, Australia) for polymer precipitation. The deuterium-labelled normalising standard, \(d_{3}\)-4-methylguaiacol, was purchased from CDN Isotopes (Pointe-Claire, Quebec, Canada). Food grade (\(>\)98% purity) \(\beta\)-CD and \(\gamma\)-CD were sourced from the IMCD Group (Adelaide, SA, Australia). Stock solutions of volatile phenols and deuterated standards were made in pure ethanol (Thermo Fisher  
Figure 5: Reusability of CD-HDI polymers for sequential binding of volatile phenols by (**a**) \(\beta\)-CD-HDI and (**b**) \(\gamma\)-CD-HDI polymers, expressed as relative peak area. Values are means of three replicates \(\pm\) standard error (but some standard errors are obscured by symbols).  
Scientific, Waltham, MA, USA) and stored at -20 \({}^{\circ}\)C. Working solutions were prepared in model wine containing 5 g/L of tartaric acid (Thermo Fisher Scientific) and 12% _v/v_ ethanol, and were stored at -4 \({}^{\circ}\)C. A commercial red wine (a 2017 Barossa Valley Cabernet Sauvignon) was used for the adsorption study.
## 4 Results
### Classification Results Using CCA Fusion
#### 4.5.1 Statistical Analysis of Bagging Ensemble on CCA Fusion  
We have used the Bagging Ensemble classifier to classify the lesion on the vector of the fused feature. The statistics show that the AML class's accuracy is 100 percent, ALL reached 93.182 percent, and MM's accuracy is around 97.78 percent. Sensitivity, precision, F1-score, etc., are cited below in Table 10. The overall accuracy of this classifier was very efficient and reached 97.4 percent. The combined results of the three classes are also shown in Table 10, which is also given below; also, the confusion matrix is shown in Figure 7.  
Overall accuracy achieved by Bagging Ensemble on ALL, AML and MM was 97.04%. The overall statistics of Bagging Ensemble are given below in Table 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Class** & **Accuracy\%** & **Sensitivity\%** & **Specificity\%** & **Precision\%** & **False Positive\%** & **F1-Score\%** \\ \hline ALL & 93.182 & 93.182 & 98.901 & 97.619 & 1.0989 & 95.349 \\ \hline AML & 100 & 100 & 100 & 100 & 0 & 100 \\ \hline MM & 97.778 & 97.778 & 96.667 & 93.617 & 3.333 & 95.652 \\ \hline \end{tabular}
\end{table}
Table 10: Statistics of Bagging Model using CCA Fusion.  
Figure 6: Confusion Matrix of CNN-2.
# Accurate Collaborative Globally-Referenced Digital Mapping with Standard GNSS
## 3 GNSS Error Analysis
### Pseudorange Measurement
\]  
or  
\[\mathbf{\rho}=\mathbf{h}(\tilde{\mathbf{x}},\tilde{\mathbf{I}},\tilde{\mathbf{T}},t_{\mathds{R}})+ H(\mathbf{x}-\tilde{\mathbf{x}})+\tilde{\mathbf{I}}+\tilde{\mathbf{T}}+\mathbf{w}. \tag{2}\]  
Rearranging measured and modeled quantities on the left-hand side to get the standard form for a linearized measurement model yields  
\[\mathbf{z}\triangleq\mathbf{\rho}-\mathbf{h}(\tilde{\mathbf{x}},\tilde{\mathbf{I}},\tilde{\mathbf{T}},t_{\mathds{R}})+H\tilde{\mathbf{x}}==\mathbf{z}=H\mathbf{x}+\tilde{\mathbf{I}}+\tilde{\mathbf{T}} +\mathbf{w}. \tag{3}\]  
The \(i\)th row of the measurement sensitivity matrix \(H\) is  
\[H_{i}\approx \begin{bmatrix}\frac{\mathbf{r}_{\mathds{R}}^{T}(t_{\mathds{R}})-\mathbf{ r}_{\mathds{R}}^{T}(t_{\mathds{R}}-\delta t_{\mathds{R}}-\delta t_{\mathds{R}} \cap\mathds{G}_{i})}{\|\mathbf{r}_{\mathds{R}}(t_{\mathds{R}})-\mathbf{r}_{\mathds{R} }(t_{\mathds{R}}-\delta t_{\mathds{R}}-\delta t_{\mathds{R}}\cap\mathds{G}_{ i})\|},&1\end{bmatrix} .\]  
By solving (3) for \(\mathbf{x}\), updating \(\tilde{\mathbf{x}}\), and iterating until convergence, R obtains its state estimate \(\hat{\mathbf{x}}(t_{\mathds{R}})\):  
\[\hat{\mathbf{x}}(t_{\mathds{R}})\triangleq \begin{bmatrix}\hat{\mathbf{r}}_{\mathds{ R}}(t_{\mathds{R}})\\ \delta t_{\mathds{R}}(t_{\mathds{R}})\end{bmatrix} .\]  
For dynamic applications such as vehicle tracking, the state \(\mathbf{x}(t_{\mathds{R}})\) is typically augmented to include the time derivatives of \(\mathbf{r}_{\mathds{R}}(t_{\mathds{R}})\) and \(\mathbf{t}_{\mathds{R}}(t_{\mathds{R}})\), and the measurement model typically assumes direct measurement of apparent Doppler frequency.
# Intelligent Prediction of Aeroengine Wear Based on the SVR Optimized by GMPSO
## 4 Aeroengine Wear Prediction Case
### Comparison with Other Methods  
In order to verify the accuracy and consistency of the GMSO-SVR prediction method proposed in this paper, it was compared with the commonly used back propagation (BP) network [21] and auto regressive moving average (ARMA) [22]. Because the weights of the BP network are initialized randomly, the BP network performs calculations several times, and the best result was used for the comparison. Specifically, based on the data structure, BP network is a three-layer network with 13 neurons in the input layer and one neuron in the output layer. As for the hidden layer, it can be calculated using an empirical formula \(\sqrt{1+13}+\eta\), in which 1 and 13 represent the number of neurons in the output and input layers, respectively, and \(\eta\) is a positive integer generated randomly between 1 to 10. The error goal for BP is set as 0.001 and the iteration number is 500. ARMA is a classical method for processing time series; after calculation, the order of the model is (4, 8), then the autoregressive coefficient is calculated as [0.5909 0.5777 0.3926 0.5616] 1\({}_{\times}\) 4, and the moving average coefficient is calculated as [0.2464, 0.4266,..., \(-\)0.7759, \(-\)0.4764] 1\({}_{\times}\) 8. Table 5 shows the comparison of the different prediction results. Table 5 demonstrates that the SVR optimized by means of GMPSO possessed more stable performance and better prediction accuracy.
## 2 Navigation and Localization  
Navigation and localization are two of the most important challenges for underwater robotics [11]. These continue being issues to solve for many applications such as collaborative missions. DR and INS are traditional methods for AUV localization and navigation but have issues as the decrement of the pose accuracy over time. In addition to traditional technologies, this problem was addressed in the past with acoustic technologies as Long Baseline (LBL) [15; 16; 17], Short Baseline (SBL) [18; 19], or Ultra-Short Baseline (USBL) [20; 21; 22; 23] systems. Acoustic positioning systems, though, require careful calibration of the sound velocity, as they suffer from multipath Doppler effects and susceptibility to thermoclines; they also have a limited range and accuracy [24]. Geophysical Navigation (GN) is also a solution for vehicle localization. Matching algorithms such as TERrain COntour-Matching (TERCOM) and Sandia Inertial Terrain Aided Navigation (SITAN) are relatively mature, however new algorithms are currently being proposed [25]. The main limitation of GN systems is the need for a geophysical map to compare the measurements from the sensors. Visual-based systems have been a trend for vehicle navigation at land and air environments, but there are several problems related to light propagation and detection in underwater environments. Additionally, most visual-based methods for autonomous navigation depend on the presence of features in the images taken, which, even if they exist, are difficult to extract due to the limited illumination conditions. In recent years, the field of AUV localization is shifting from old technologies, towards more dynamic approaches that require less infrastructure and offers a better performance [13]. This section presents a survey on single-vehicle localization and navigation technologies--including different methods, sensor, and approaches--in the understanding that those can be applied in multi-vehicle collaborative navigation schemes.
# Calibration of Mean Wind Profiles Using Wind Lidar Measurements
## 3 Problem Statement
(a) \(M=1000\) sets of \(N\) pseudo-experimental profiles each are selected from the available 10,000; (b) for each of the \(M=1000\) sets of \(N\) profiles, the average parameters \(\mu_{z0}\) (\(i\)) and \(\mu_{zd}\) (\(i\)) were evaluated, \(i=1\) to 1000, by averaging the parameters \(z_{0}\)(\(i\),\(j\)) and \(z_{d}\)(\(i\),\(j\)), with \(j=1\) to \(N\); (c) the mean values, \(\mu_{z0}\) (\(N\)) and \(\mu_{zd}\) (\(N\)), the standard deviation, \(\sigma_{z0}\) (\(N\)) and \(\sigma_{zd}\) (\(N\)), and the Coefficient of Variation, CoV\(z_{0}\)(\(N\)) and CoV\(z_{d}\)(\(N\)), of \(\mu_{z0}\) (\(i\)) and \(\mu_{zd}\) (\(i\)) were calculated for each value of \(N\). In Figures 4 and 5, the bias of the mean values \(\mu_{z0}\)(\(N\)) and \(\mu_{zd}\)(\(N\)), together with the confidence interval (bias \(\pm\) Coefficient of Variation), is shown as a function of the number \(N\) of measured profiles for the 4 cases in Table 1. In particular, the relative bias is defined as the ratio between the mean value and target value of the parameters \(z_{0}\) and \(z_{d}\), with a relative bias of one representing an unbiased estimation. These results show how an accurate estimation of the pair of parameters \(z_{0}\) and \(z_{d}\) can be achieved only for sites with \(z_{0}+z_{d}\) values approaching the lowest measurement point (e.g., Case (d)), and when a reasonably large number of measured profiles is available (e.g., \(N>50\)). On the other hand, for sites with a larger distance between the base of the logarithmic wind profile and the lowest measurement point (e.g., Case (a) and Case (b)), an accurate estimation of the profile parameters cannot be achieved even when a very large number of measures is available. Figure 3: Measurements points (red and black stars) and identified profiles (red and black lines) corresponding to two different pairs of parameters \(z_{0}\) and \(z_{d}\) (red and black diamonds in Figure 2a), and comparison with the target profile for Case (a) (blue line). Figure 4: Bias (red circles) and confidence interval (bias \(\pm\) coefficient of variation, black stars) of \(z_{0}\) as a function of the sample size \(N\): Case (a) \(z_{0}\) = 0.05 m, \(z_{d}\) = 5 m; Case (b) \(z_{0}\) = 1.5 m, \(z_{d}\) = 5 m; Case (c) \(z_{0}\) = 0.05 m, \(z_{d}\) = 20 m; Case (d) \(z_{0}\) = 1.5 m, \(z_{d}\) = 20 m.  
Figure 5: Bias (red circles) and confidence interval (bias \(\pm\) coefficient of variation, black stars) of \(z_{d}\) as a function of the sample size \(N\): Case (a) \(z_{0}\) = 0.05 m, \(z_{d}\) = 5 m; Case (b) \(z_{0}\) = 1.5 m, \(z_{d}\) = 5 m; Case (c) \(z_{0}\) = 0.05 m, \(z_{d}\) = 20 m; Case (d) \(z_{0}\) = 1.5 m, \(z_{d}\) = 20 m.
## 2 Research Context
### Modular Product Platform  
The literature delineates two predominant perspectives on defining the platform concept [27].  
Definition #1 (set of stable modules) [38]: The first school of thought defines a platform as a set of stable parts, modules, or technologies common to a product family [13]. The platform concept aims to identify stable components for the entire product family and modules that offer variety to meet specific customer needs [27]. The reuse of this stable sub-assembly yields a competitive advantage by minimizing costs [5], due to economies of scale among the stable components [6]. The reduction in part variety or internal variety leads to decreased complexity. This same reuse of design results in enhanced design efficiency [25; 27]. However, reusing a core of stable components to form a platform may result in reduced variety, reduced customization [39], and reduced speed of change [27].  
Definition #2 (set of rules and interfaces) [40]: The second definition of a platform is characterized by its modular architecture, interfaces, and standard design rules [13]. Interfaces and rules enable flexibility in product design [25; 41]. This flexibility leads to a greater variety of products without the need for design modifications [41] and improves time to market [25]. From this set of rules and interfaces, multiple products can be created by attaching new modules or technologies to the predefined interfaces without altering the platform [27]. The ease of integrating new modules and technologies enables this platform definition to accommodate a wide variety of products within the same platform [6].  
Despite its increased flexibility, this platform concept facilitates the reuse of knowledge, interfaces, technologies, functionalities, and architectural rules, as well as the pooling of human skills and relationships. This common platform reduces the number of different elements [25] as well as complexity [31] and enables efficiency and effectiveness in design [13], as well as in many other tasks [25].
## 5 Conclusions and Future Work  
The prime goals of the proposed work were attained with the complete design of an emergency response system and with the successful implementation of a TestBed, which carried out the intended communication using VANET. Our study provided ways in which the scientific society could achieve the required goals and objectives of an emergency response system applicable in a vehicular environment, and presented real-time statistics of an emergency response system along with the disaster statistics of an affected area. Furthermore, it provided a way to measure the performance of a network in terms of round-trip-time with reference to the number of hops along the route from the source to destination. Moreover, the research work may also be used to receive knowledge about reliability, and delay factors based on the hop-count value along the route within the network. The proposed prototype application was examined through experimental validation by fetching the user message from the GPIO pins of the Raspberry Pi through serial communication, and the geographical coordinates of the user from the GPS sensor. The communication was performed using the Transmission Control Protocol, mainly because of its connection-oriented and reliable nature. Out of all the commercially available ad hoc routing protocols, BATMAN-ADV was selected because of its sophisticated abstraction. It hides all lower-level complexities and provides an easy-to-use virtual interface between Layers 2 and 3 (Network and Data Link, respectively) of the TCP/IP model. This study, along with the implemented TestBed, provides an effective way to exchange crucial information amongst volunteers and staff working in dire situations, such as that of a post-disaster rescue-and-relief operation. Since fast and reliable communication plays a key role in such scenarios, this work offers a viable solution that can be implemented by the concerned authorities at times of catastrophic disaster.  
Conceptualization, K.A.K.; Data curation, A.S.; Formal analysis, K.A.K.; Investigation, A.Q. and J.P.; Methodology, K.A.K.; Software, A.S.; Supervision, A.Q. and J.P.; Validation, K.A.K., [PERSON], A.Q. and J.P.; Writing--original draft, K.A.K.; Writing--review & editing, K.A.K., O.C. and J.P.  
This research was supported by the European Commission in the framework of Erasmus Mundus and within the project CLINK. It is also funded by International Graduate School (IGS), Universitat Bremen, Bremen, Germany.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
## 3 Materials and Methods  
### Design of Experiments  
The general scheme of the experiment is shown in Figure 1. IMUs were fixed on both wrists of the boxer, including a gyroscope and an accelerometer, which allowed the rotational and translational movements of the hands to be tracked. Along with the IMUs, wireless transmitters were installed on the wrists, which, via a Bluetooth channel, transmitted data to a computer for analysis using the ANN. Boxers punched in shadow boxing mode. The data of the measuring modules began to be transmitted to the computer with a frequency of 1 millisecond after reaching a certain threshold of the absolute value of acceleration, thus determining the moment when the punch was made. With this solution, the data transmission channel was less loaded.  
The value of absolute acceleration after which the program decided that this was the beginning of the punch was 2.5 m/s\({}^{2}\). Of course, this method of determining the beginning of the punch has a number of disadvantages. The main drawback is that the boxer can make quick hand movements that the system can mistake for a punch. However, this disadvantage is compensated for by the fact that the classification in the ANN included the category \"movements without punch\", which eliminated incorrect cases.  
The boxers wore standard sportswear and 10-ounce boxing gloves.  
After the ANN model was trained, it was saved in a file with the extension.h5, together with the topology of the network (weights and bias). This file was then converted and embedded in the microcontroller using the X-CUBE-AI code generator [17]. Classification based on the trained data from the experiments was carried out in a microcontroller.
## 4 Experiment and Results
### Results and Discussions
#### 4.2.3 Performance Comparison of Multi-Channel Images with Different Resolutions  
The second experiment compares the performance of ADLCNN models trained on multi-channel images of different resolutions. Here, we have designed three different resolutions, namely 5 \(\times\) 5, 25 \(\times\) 25, and 125 \(\times\) 125.  
Table 5 shows the performance results of the ADLCNN method for predicting the cumulative delay level of departures for different resolutions of multi-channel images. Here, the model uses a channel combination of Chanel_combi7 as input. At a 25 \(\times\) 25 resolution, the model achieves an overall accuracy of 91.61% and a recall of 0.98 for the majority class (A0). However, class imbalance leads to lower accuracy for the minority class (A1), with only 73 out of 172 A1 samples correctly predicted, so the subsequent experiments of comparing the performances of the different methods are carried out.  
Table 6 reports the average processing time per step during training and testing at various image resolutions. The computational cost increases dramatically with resolution at 125 \(\times\) 125, training steps average 238 ms--over 25 times longer than at 25 \(\times\) 25--and testing steps average 58 ms, nearly 18 times longer. Considering both predictive performance and efficiency, a 25 \(\times\) 25 resolution is optimal.
## 7 Human Safety Data with an Indication of the Role of Health Care Providers
The internal dose of catechins is vital for the occurrence and severity of hepatotoxicity. It is worth mentioning that the safe intake level is 338 mg EGCG/day (fed or fasted) in solid dosage form for adults. An Observed Safe Level (OSL) of 704 mg EGCG/day (fed or fasted) might be for tea preparations in beverage form [138]. According to United States Pharmacopeia adverse event case reports, hepatotoxicity with EGCG intake is from 140 mg to \(\sim\)1000 mg/day and substantial inter-individual variability in susceptibility, possibly due to genetic factors [139]. Intake of EGCG with green tea infusions and GTE-based beverages is up to about 450 mg EGCG/person/day in Europe and even higher in Asia [140]. It is essential to point out that in clinical intervention studies, liver effects were not observed after intakes below 600 mg EGCG/person/day, so a tolerable upper intake level of 300 mg EGCG/person/day is proposed for food supplements [140]. GTE may have hepatotoxic solvent or pesticide residues, pyrrolizidine alkaloids, and elemental impurities. The monograph of Powdered Decaffeinated Green Tea Extract contains information about not taking on an empty stomach and in the case of a liver problem. Patients should have a medical consultation when liver problems occur, such as abdominal pain, dark urine, or jaundice (yellowing of the skin or eyes) [139]. The dietary supplements are suspected of inducing liver damage, e.g., SLIMQUICK(r) as a weight loss product can lead to severe acute hepatocellular liver injury, which may result in transplantation. GTE should be studied further as a possible cause for liver injury [141]. In the review from 2018, there were 65 studies from 104 that indicated that green tea or GTE was generally well tolerated without side effects. AEs were mostly related to gastrointestinal disturbance, such as primary nausea (22 studies), abdominal pain or discomfort (17 studies), diarrhea (14 studies), dysppesia/indigestion (12 studies) and/or elevated liver enzymes (11 studies). Green tea preparations consumed in beverage forms are better tolerated than in solid bolus dose exposure. Treatment-related early death and severe toxicity in the liver, kidney, thymus, spleen and pancreas were reported in the studies involving highly concentrated green tea catechins or purified EGCG administered via bolus dose (gavage or capsules) as low as 150 mg EGCG/kg/day [138]. It is worth mentioning that the application of nanosized vitamin E can increase the absorption rate to a greater extent than in the case of normal food-related material. These results were obtained in safety analyses performed on food-related nanoscale materials for human consumption. The discussed combination for sure does not lead to signs of acute toxicity in rats, treated as a research model [142].