esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrode Kinetics of Ion Jelly and Ion Sol-Gel Redox Materials on Screen-Printed Electrodes
## 2 Materials and Methods  
### Preparation of IJ and ISG  
1-Ethyl-3-methylimidazolium ethylsulfate ([emim][EtSO4]) was supplied by Strem Chemicals (Newburyport, MA, USA). 1-octyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([omin][TFSI]) and 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([bmim][TFSI]) were purchased from IoLiTec (Heilbronn, Germany). Bacteriological gelatin, type A \"Cultimed\", was purchased from Panreac (Castellar del Valles, Spain). All other chemicals mentioned were purchased from Sigma-Aldrich (St. Louis, MO, USA). Solutions for electrochemical measurements were prepared with deionized water from a Milli-Q water purification system, while the rest used double distilled (dH\({}_{2}\)O) water.  
Screen-printed electrodes (DRP 110) were acquired from Metrohm Dropsens (Asturias, Spain): both secondary and working electrode materials were carbon ink, and the pseudo-reference electrode was made of silver ink.  
The IJ preparation method was based on a previously described protocol [47]. IJ films were prepared by mixing 100 \(\times\) 10\({}^{-6}\) dm\({}^{3}\) ([emim][EtSO4]) with 0.040 g of gelatin and 260 \(\times\) 10\({}^{-6}\) dm\({}^{3}\) of 0.02 mol\(\cdot\)dm\({}^{-3}\) Tris-HCl buffer, pH 7.5 at 60 \({}^{\circ}\)C, until reaching homogeneity (typically within a few minutes). After 5 min of further stirring, 50 \(\times\) 10\({}^{-6}\) dm\({}^{3}\) aliquots of each solution were deposited on the carbon SPEs. The deposited solution was then left to gel for 1 h at room temperature, followed by maturation under controlled humidity for 4 days inside the headspace of a closed chamber containing a saturated aqueous solution of sodium chloride (water activity, aw of 0.76 [48]). After 4 days in these conditions, the IJ's weight reached equilibrium and was considered mature and ready for use. When depositing on SPEs, the gel was made to cover the three-electrode system (Figure 1).  
Ion sol-gel (ISG) was prepared by using a sol-gel route for immobilizing ionic liquids in silica matrixes using alkoxysilanes (AS) [35; 37]. TMOS and MTMS were added to a solution of ([omin][TFSI]) or ([bmim][TFSI]) in formic acid, in a molar ratio of 0.50.57.8:1 (TMOS:MTMS:FA:IL). A 15% vol of 50 mM sodium PB buffer pH 7.6 and 4% of ([omin][BF4]) were added, and after a very brief stirring, 75 \(\times\) 10\({}^{-6}\) dm\({}^{3}\) of solution was deposited (also covering all three electrodes as done on SPEs). Maturation occurred at room conditions for 7 days.  
The swelling was carried out in conditions similar to those in previous work [24]. Maturated films were weighted accurately and placed inside sample container flask caps, then immersed in 7 \(\times\) 10\({}^{-3}\) dm\({}^{3}\) of distilled water at 4 \({}^{\circ}\)C. The swelling was carried out until a visible equilibrium was reached (24 h). Post-swelling films were removed from excess water and underwent a repetition of the maturation process.
## 3 Link Cost Metric With Reliability Assurance
### Reliability Model for Network With EO Missions
\tag{15}\]  
Finally, from the above equation, it denotes that for better network reliability, there exist two possible methods: reducing the number of each path's hop, or increasing the redundant paths' number.
## 4 Experimental Results  
In this section, experimental results are given to verify the proposed scheme's embedding capacity in terms of the increased rate, the image quality of the stego images, the decrease of pixels modified rate and image steganalysis. From these experiments, eight common eight-bit gray-level images and binary images of size \(512\times 512\) are tested. These eight cover images are airplane, baboon, boat, Elaine, Gold Hill, Lena, peppers and Tiffany, as shown in Figure 2. Two gray-level secret messages (bridge and pentagon) of size \(100\times 100\) are shown in Figure 3. Similarly, the eight binary images are shown in Figure 4. In the maximum embedding capacity test, we also use the random number generator (PRNG) to generate random bits as the secret message to be hidden.  
The embedding capacity (bpp) and image quality (PSNR: peak signal-to-noise ratio) are two important criteria to evaluate the stego image in the data hiding system. The PSNR is defined as Equation (6):  
\[PSNR = 10\times\log_{10}(\frac{255^{2}}{MSE}) \tag{6}\]  
Figure 3: Secret images.  
Figure 2: Eight \(512\times 512\) gray test images. (**a**) F16; (**b**) baboon; (**c**) boat; (**d**) [PERSON]; (**e**) [PERSON]; (**f**) [PERSON]; (**g**) pepper; (**h**) [PERSON].  
where the MSE (mean square error) is defined as Equation (7).  
\[MSE = \frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}(C(i,j)-S(i,j))^{2} \tag{7}\]  
where \(M\), \(N\) is the image size and \(C(i,j)\) and \(S(i,j)\) are the pixel values of the stego image and the original image (cover image), respectively.  
Figure 4: Eight binary images. (**a**) F16; (**b**) baboon; (**c**) boat; (**d**) [PERSON]; (**e**) [PERSON]; (**f**) [PERSON]; (**g**) pepper; (**h**) [PERSON].  
Figure 5: Stego images (proposed scheme \(n=3,k=3\)). (**a**) F16; (**b**) baboon; (**c**) boat; (**d**) [PERSON]; (**e**) [PERSON]; (**f**) [PERSON]; (**g**) pepper; (**h**) [PERSON].
# Monitoring of the Variation in Pore Sizes of Woven Geotextiles with Uniaxial Tensile Strain
## 1 Introduction
The large pore size (i.e., O\({}_{95}\)), medium pore size (i.e., O\({}_{50}\)), and small pore size (i.e., O\({}_{10}\)) obtained composite the pore size-frequency distribution of woven geotextiles. The variations of different characteristic pore sizes, O\({}_{95}\), O\({}_{80}\), O\({}_{50}\), O\({}_{30}\), and O\({}_{10}\) under each 1% tensile strain, are recorded as uniaxial tensile strain increases. Three representative specific pore diameters O\({}_{95}\), O\({}_{50}\), and O\({}_{10}\) are monitored with uniaxial tensile strain. Five comparable pore sizes with each specific pore size (i.e., pore sizes ranging from O\({}_{93}\) to O\({}_{97}\) with O\({}_{95}\)) are monitored when uniaxial tensile strain increases. Using MATLAB code, the colorful pore size distribution diagrams demonstrate the variation in specific pore sizes of the specimen in a visual way. The pore-opening sizes are observed to agree well with the bell-shaped Gaussian distribution. The results exhibit an accurate prediction of the variations in pore diameters with uniaxial tensile strain for all tested woven geotextiles.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 7 Conclusions  
The VVS relay and ROCOF relay are widely applied to detect the islanding condition. However, the performances of both VVS relay and ROCOF relay are not good. There are inherent non-detection zones and mal-operations are also possible. The proposed composite islanding detection solution in this paper applies both VVS and ROCOF features of the islanding condition. It detects the VVS in the same direction to initiate the calculation of accumulated phase angle drift. It monitors the real-time ROCOF to reset the calculations. The operation criterion is not directly based on VVS or ROCOF, but only based on the accumulated phase angle drift. So more sensitive settings for VVS and ROCOF function can be applied, which can reduce the ratio of NDZ. Significantly, the balanced three phase voltage vector shifts are applied to distinguish the islanding condition from the unbalanced system fault. For three-phase fault, this proposed islanding detection solution can remain stable dependent on the proper angle drift setting. This composite islanding detection solution is verified in Matlab Simscape physical simulation based on the synchronous machine and the practical waveform recorded from different sites. The results show this composite islanding detection solution is stable under the system fault and sensitive for the islanding condition where the power imbalance is more than 10%. The next step of this research is to perform the field tests in as many as possible different countries.  
The paper was a collaborative effort among the authors. [PERSON] performed the simulation, analyzed the data, and wrote the paper. [PERSON] provided critical comments.  
This work was supported in part by the Fundamental Research Funds for the Central Universities of China. Meanwhile, this work was supported in part by Schneider Electric.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### ECG Denoising
Combining amplitude and isoelectric line padding involves adjusting the signal's amplitude and aligning it with the isoelectric line before padding. This method helps preserve both the relative magnitude and baseline of the ECG signals, leading to more accurate denoising. The ECG signal denoising with multi-layer filter visualization is shown in Figures 4-6, where the red line is \(y\) prediction, the blue line is \(y\) test, and the red region is an error prediction (error region). The error region displays the size of the difference between the ground truth and the predicted results. The ECG signal denoising result with the amplitude-isoelectric line centered method produces good metrics and has the ability to reduce the noise in the signal, however, during the shuffled scenario, the performance results for training and validation can vary significantly (refer to Table 3). Using distributed data in the shuffled scenario has produced good testing performance.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Brief Overview of Machine Learning Algorithms
#### 3.5.4 Support Vector Machine (SVM)  
The objective of pattern classification is to develop a model that surpasses the performance of existing models on the training data. Traditional training methods in machine learning aim to find models that accurately classify each input-output pair within their respective class. However, if the model is overly tailored to the training data, it may start memorizing the data instead of learning to generalize, resulting in a decline in its ability to correctly classify future data [62]. The support vector machine (SVM) is a supervised machine learning technique that has been widely utilized in the machine learning community since the late 1990s [63]. SVM is a powerful tool that can be applied to both classification and regression problems. It is particularly well-suited for binary classification problems. In SVM, the decision boundary is determined by selecting a hyperplane that effectively separates the classes. The hyperplane is chosen in a manner that maximizes the distance or margin between the support vectors, which are the data points closest to the decision boundary.
## 3 Numerical Simulation
### Model Establishment and Parameter Selection
#### 3.1.1 Rock Parameters  
In light of the significant deformation exhibited by rocks during blasting, the JH-2 constitutive model was employed to elucidate the correlation between rock strength and impact load. The model keyword was designated as *JOHNSON_HOLMQUIST_CERAMIC. As an illustrative example, the Class III dolomite limestone in the construction section of the Dianzhong Water Diversion Project can be characterized through the application of indoor triaxial compression testing, uniaxial tensile testing, uniaxial compression testing, and engineering geological report data, as outlined in Table 1. Based on the existing fundamental physical and mechanical parameters, the parameters of the JH-2 model were calculated [25; 26], as illustrated in Table 2. The *MAT_ADD_EROSION keyword was used to define the compressive strength and failure principal stress of the rock as the rock failure criterion, so that the rock fails when the compressive strength reaches a set value or the unit principal stress reaches a set value, thus simulating the failure of rock blasting.
## 5 Conclusions  
Monitoring pest insect populations is a fundamental issue in agriculture and forestry protection. One of the most popular methods to monitoring pests is by means of a set of traps strategically distributed across the desired monitoring area. To collect trap data, typically, human operators visually check the traps and annotate the number of captured individuals with a frequency of 15 to 30 days. This method has two main drawbacks: (1) it is labor intensive and therefore costly and (2) all monitoring traps cannot be synchronized to measure the target pest population. Given that the traditional monitoring techniques are labor intensive with a poor temporal resolution measurement, the dynamics of pest population density in the field cannot be accurately monitored. Consequently, the estimation for a target pest population will be limited to a long-term scale.  
In order to improve the trap monitoring pest system, we proposed a low-cost system based on battery-powered wireless image sensors, that accurately monitor pest population with a higher temporal resolution and a significant reduction of pest monitoring costs, as no human intervention is  
required during the monitoring process. The main advantages of our proposal are: (1) higher scalability, being able to deploy in small monitoring areas (greenhouses) as in large plantation extensions, (2) low-cost wireless image sensor (less than 100 \(\upepsilon\) per sensor), (3) very low-power consumption that allows zero maintenance during the operational life of sensor nodes (up to 25 years), (4) temporal resolution of trap monitoring data is higher and it can be programmable, and (5) trap monitoring data may be available in real time through an Internet connection.  
The proposed image sensor system may be easily fixed inside the existing traps to wirelessly deliver images to central station. When images are received at central station, typically a PC with no power restrictions, they may be processed applying image recognition algorithms to evaluate the number of individuals found at each image. The processing results are attached to each image as metadata as well as the timestamp and geolocation info, and the resulting images are conveniently stored in the control station, making them available through an internet connection.  
Although this work is focused in the image sensor design, considering a simple communication model with the central station (direct communication), it proves that wireless image sensor technology will leverage the deployment of efficient pest monitoring systems with a considerably cost reduction (low-cost nodes and no human monitoring costs) and near zero maintenance due to the large operational life of the proposed image sensors.
## 5 Analysis of Calculation Results and Discussion  
In order to reduce the influence of rock heterogeneity on the analysis of specific energies, eight groups were formed according to the type of rock material from which samples for laboratory testing were excluded. For each group, a statistical test (Dixon test) was performed on the values of specific cutting and drilling energies to determine the outliers [35]. This was followed by basic statistical data processing to determine the arithmetic mean values of specific energies of individual groups, after which linear regression models of the dependence of specific cutting energy on specific drilling energy were calculated using the least squares method and the orthogonal distance regression method. The latter method involves the error of both variables [36], which refers to the cases described in this paper because both the abscissa and the ordinate contain the data obtained by measurements. Using both methods, the equations of the line of the dependence of the specific energy of rock cutting on the specific drilling energy were calculated, and the deviations in the coefficients of slope of the line and sections on the ordinate, i.e., in the coefficients of determination are minimal. For simplicity of calculation, explicit equations of lines obtained by the least squares method were chosen, so Equation (22) shows the linear dependence of specific rock cutting energy on specific drilling energy obtained using a measuring system for measuring electrical power, and Equation (23) shows linear dependence of specific rock cutting energy on specific drilling energies obtained by means of a measuring system with a measuring transducer of force and torque. \[SE_{\text{C}}=0.27\cdot SE_{\text{D}}-78.71\hskip 14.226378 ptR^{2}=0.71 \tag{22}\]  
\[SE_{\text{C}}=0.35\cdot SE_{\text{D}}-203.06\hskip 14.226378 ptR^{2}=0.56 \tag{23}\]  
Determination coefficients indicate a strong and moderate correlation. The calculation of the measurement uncertainty was performed on the basis of data from the calibrations and specifications of the manufacturers of measuring instruments and equipment. In addition, it was assumed that the input quantities, on the basis of which the standard measurement uncertainty of the input quantities estimate is calculated, are independent. The overall representation of the calculated specific energies with the corresponding measurement uncertainties is visible in Table 4, while the graphical representations are visible in Figures 7 and 8.
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## 3 Results and Discussion
### Properties of the CNF Films
Therefore, the results of this study further confirm that CNF films have outstanding oxygen barrier properties, possibly derived from the crystalline structure of the cellulose fibrils and from the formation of dense fibrillary networks with unconnected pores, which hinder gas transfer across the material [3; 19]. One of the main drawbacks in films produced from lignocellulosic materials is their hydrophilicity, which typically results in poor water barrier properties. As shown in Table 4, the water vapour permeability of CNF films was decreased by using water-extracted pulps. The best performance was achieved by the BBK240.10 film, in which the permeability to moisture was decreased about 25%. This improved performance may be due to the removal of hygroscopic hemicelluloses during the hot water extraction, and the consequent decrease in the amount of free hydroxyl groups. Nonetheless, it should be noted that the moisture (and oxygen) barrier properties of the films might not be directly comparable owing to some differences in film thickness. The vapour permeability values shown in Table 4 were slightly higher than those obtained earlier [44] for CNF films from bleached birch kraft pulp. However, in the aforementioned study, a mechanical pre-treatment prior to fibrillation was applied instead of TEMPO-mediated oxidation, and the number of passes also differed, which could have had an effect on the formation of fibrillar networks and the availability of water sorption sites in the CNF and in their corresponding films. In addition to the vapour barrier properties, the hygroscopicity of the films was measured in a DVS apparatus during a full sorption (adsorption followed by desorption) cycle. Typical of lignocellulosic materials, all the CNF films showed a hysteresis in their hygroscopic behaviour, in which the moisture content at a given RH was higher during the desorption than during the adsorption process (Figure 5). The results also showed that all the CNF films had a similar hygroscopicity at RH below 60%. Above this RH level, the films from water-extracted bleached pulps were slightly more hydrophobic, while the film from water-extracted unbleached pulp showed a similar hygroscopicity as the CNF film made from the original bleached kraft pulp. The slightly higher hygroscopicity of the UBK240.10 film, compared to the BBK240.10 film with a similar xylan content, may be due to the presence of free hydroxyls in the lignin structure. Figure 5: Equilibrium moisture content (EMC) of CNF films as a function of RH in the atmosphere. Sample names are the same as in Figures 1–3.
# Determination of the Operating Temperature of the Gas-Insulated Transmission Line +
###### Abstract  
Gas-insulated lines (GILs) have been increasingly used as high-current basducts for high-power transmission. Temperature is one of the most important factors affecting the performance and ampacity of GILs. In this paper, an analytical method was proposed to determine the temperature of a three-phase high-current busduct in the form of a single pole GIL. First, power losses in the phase conductors and enclosures were determined analytically with the skin, and proximity effects were taken into account. The determined power losses were used as heat sources in thermal analysis. Considering the natural convection and radiation heat transfer effects, the heat balance equations on the surface of the phase conductors and the screens were established, respectively. Subsequently, the temperature of the phase conductors and the enclosures were determined. The validation of the proposed method was carried out using the finite element method and laboratory measurements.  
gas-insulated line; high-current busduct; power losses; thermal analysis Article
# A Fuzzy Method for Joint Resource Allocation and Stable Pairing in D2D Communications
## 2 System Model  
In this paper, we consider the problem of joint resource allocation and stable pairing in an underlay in-band D2D enabled cellular communication network. The system model consists of a single cell with a cellular user and a number of co-channel D2D nodes. Some D2D nodes are transmitters and some are receivers, in such a way that there are several potential transmitters for each receiver. The transmitters of each receiver are specified separately to share different contents (Figure 1). In this scenario, it is assumed that the potential D2D transmitters around a receiver have a similar content that may be different from the potential D2D transmitters of the other D2D receiver. Therefore, different D2D categories (that consist of a D2D receiver and a number of potential D2D transmitters) are assumed to have different contents. Moreover, the considered scenario is a centralized protocol in which all the decisions are made by the base station. So, the set of potential D2D transmitters for each receiver are specified by their contents based on the base station decision.  
We tend to find the most suitable transmitter for pairing with each receiver so that stability is maintained. By stability, we mean a situation in which the battery levels and  
Figure 1: System model.  
the data rates of two potential paired nodes are acceptable. For this purpose, considering potential transmitting D2D users as fuzzy nodes, the normalized smartphone battery levels and data rates are used to obtain an index for stability. Note that because of interference considerations, a D2D receiver may not be allowed to pair with any of the transmitters.  
Let \(\mathcal{D}_{r}=\{r_{1},r_{2},...,r_{N}\}\) be the set of D2D receivers, where \(N\in\mathbb{N}\) is the maximum number of co-channel D2D receivers with a cellular user in the network. For each D2D receiver \(r_{i}\) (\(i=1,\ldots,N\)), the set \(\mathcal{D}_{t_{i}}=\{t_{1i},t_{2i},...,t_{Ki}\}\) denotes the potential D2D transmitters, where \(K\in\mathbb{N}\) is the number of D2D transmitters around the receiver \(r_{i}\). Moreover, the D2D transmitters are located in a circle centered on each D2D receiver with radius \(R_{max}\), where \(R_{max}\) is the maximum search radius of D2D receivers.  
The notations used in this paper are summarized in Table 1.