esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 7 Singular Pulses in the Spectral Domain
### Nyquist Sinc Pulse with Smooth Spectrum  
The singular spectrum may simulate real pulses, but in real scenarios, singularities cannot occur even in the spectral domain. We can therefore utilize the theorems and conclusions from the previous sections to spectrally smooth the Nyquist-Sinc pulse.  
Thus, the singular rectangular spectrum of the Nyquist-Sinc pulse  
\[A(z=0,\omega)=\theta A_{0}rect_{1}(\omega\theta/2\pi) \tag{82}\]  
can be replaced with its smooth counterpart:  
\[A(z=0,\omega)=\theta A_{0}\frac{1}{2}\bigg{[}erfc\bigg{(}\frac{\omega-\pi/ \theta}{\Delta}\bigg{)}-erfc\bigg{(}\frac{\omega+\pi/\theta}{\Delta}\bigg{)} \bigg{]} \tag{83}\]  
However, this expression can be written as a convolution and is the spectral domain:  
\[A(z=0,\omega)=\theta A_{0}rect_{1}(\omega\theta/2\pi)*\frac{1}{\Delta\sqrt{ \pi}}\exp\bigg{[}-(\omega/\delta)^{2}\bigg{]} \tag{84}\]  
and therefore the initial pulse profile can be written as a product of a sinc function and a Gaussian one:  
\[A(z=0,t)=A_{0}\frac{\sin(\pi t/\theta)}{\pi t/\theta}\frac{1}{2\pi}\exp\bigg{[} -(t\delta/2)^{2}\bigg{]} \tag{85}\]  
Figure 23: Same as Figure 2, but for the intensity (\(|A(t,z)|^{2}\)) of the pulse presented by Equation (80).  
Figure 22: Same as Figure 2, but for Equation (80) and for the final distance of \(\zeta=\beta_{2}z/\theta^{2}=0.3\).  
Using Equations (12) and (80), we finally obtain  
\[A(t,z)=\frac{A_{0}}{2}\exp\left(-i\frac{t^{2}}{2\beta z^{2}}\right)\sqrt{\frac{i \theta^{2}}{2\pi\beta z}}\left[\text{erf}\left(-\frac{t-|\pi\beta z/\theta|}{ \sqrt{2\beta_{2}z(1-i\beta_{2}z^{2}/2)}}\right)-\text{erf}\left(-\frac{t+|\pi \beta z/\theta|}{\sqrt{2\beta_{2}z(1-i\beta_{2}z^{2}/2)}}\right)\right] \tag{86}\]  
In Figure 24, the amplitude of Equation (86) is plotted. Unlike ordinary Nyquist-Sinc pulses, this pulse decays exponentially.
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 3 Methodology of the GRFN  
### General Scheme  
The architecture of the generalized regression forecasting network (GRFN) is visualized in Figure 1. To improve accuracy and efficiency, we establish a non-linear correlation between easily measured body parameters and other detailed dimensions that are normally difficult to integrate for customized clothing design. First, we extract the principal factors by preprocessing [PERSON] et al.'s human body dimension dataset consisting of the data of 106 young women [6]. To prepare the model, the anthropometric dataset is preprocessed, and normalized for input to the KRR regression model. An appropriate \(\lambda\) value is selected by 10-fold cross-validation, multivariate regression is constructed, and the residual sequence between predicted and ground-truth data is calculated. These residual and measured sequences are input to the SVR model, where rough \(C\) and \(g\) hyperparameters are obtained using the grid search method, and the best pair \((C_{\text{best}},g_{\text{best}})\) are obtained using the MMPSO-SVR model. The KRR-based regression estimations are combined with predicted residual correction values to obtain the dimensional parameters of the human body. Finally, detailed body dimensions are obtained by using a few basic body size parameters input to our GRFN model. An interactive intelligent pattern-making system is employed to generate 2D patterns, and garments are simulated on a virtual 3D avatar for clothing display.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 3 Related Works
[31] also investigated the performance of missing data techniques on various tasks including regression, classification and bankruptcy prediction using Auto Associative Neural Networks (AANN). Ref. [32] developed a novel methodology for missing data imputation during the data acquisition phase. In their approach, the authors distributed computation among a range of stationary and mobile devices based on the edge computing paradigm, allowing the network to efficiently scale horizontally, thereby increasing the number of sensing devises and reducing the effect of missing values caused sensing errors. Various other methods have been proposed in previous research for sensor calibration. [PERSON] et al. [5] developed a sensor calibration method based on Neural Networks (NN) using on-site data for CO, Benzene, \(NO_{x}\) and \(NO_{2}\) pollutants in a municipal air quality monitoring station with the use of solid state LCS collected over a 13 months period. Their research showed the viability of achieving neural calibration, which will allow sensor devices to successfully estimate environmental pollutants with optimal results over a bounded amount of training sessions. [PERSON] et al. made use of Artificial Neural Network (ANN), Simple Linear Regression (SLR) and Multivariate Linear Regression (MLR) to calibrate a group of LCS (\(O_{3}\), \(NO_{2}\), NO, CO and \(CO_{2}\)) over a calibration period of two weeks [33]. Uncertainty measurements estimated by the regression of sensor and ground truth data showed that ANN was a suitable model for calibrating sensor clusters. On the contrary, SLR and MLR yielded measurements with high levels of uncertainty.
## 3 Results
### MuSC-EVs Rapidly Deliver Protein into Myotubes  
After establishing that MuSCs release a substantial amount of EVs, we worked to demonstrate that these EVs could deliver proteins to recipient cells. Importantly, for these experiments we used differentiated C2C12 myotubes as recipient cells to better model adult muscle fibers, in vivo. MuSC-EVs were incubated with carboxyfluorescein succinimidyl ester (CFSE) to label the amino ends of all proteins present in the sample and were delivered to myotubes by adding the labeled EVs directly to the culture media. Following a 24 h incubation, myotubes were visually inspected using fluorescent microscopy. There appeared to be an abundance of fluorescent puncta within the cultured myotubes, suggesting the cells were able to uptake the CFSE-labeled protein from the MuSC-EVs (Figure 2B). Using the same technique previously described, we next quantified the kinetics of MuSC-EV protein delivery by incubating MuSC-EVs in the culture media of myotubes for various durations ranging from 5 min to 48 h. Fluorescence from the CFSE dye was then quantified to measure the amount of protein uptake in the myotubes. Remarkably, significant delivery of MuSC-EV protein was detected within the myotubes as early as 2 h after exposure (\(p\) = 0.0141; Figure 2H) and for upwards of 48 h after exposure (\(p\) = 0.0071; Figures 2K and 3), indicating the cargo was not rapidly degraded once delivered into the myotubes. The greatest quantity of labeled protein was observed at 24 h after exposure (\(p\) = 0.0004; Figure 2J). All groups were compared to untreated control myotubes.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 3 Scenarios and Requirements
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sector** & **Use Cases** \\ \hline \multirow{2}{*}{**Home automation**} & Domotics \\  & Child/elderly tracking \\  & Smart metering \\ \hline \multirow{4}{*}{**Industrial automation**} & Remote maintenance/control \\  & Distribution automation (smart grid) \\ \cline{1-1}  & Logistics \\ \cline{1-1}  & Local asset tracking management \\ \cline{1-1}  & Smart metering \\ \hline \multirow{4}{*}{**Public infrastructure**} & City smart lighting \\ \cline{1-1}  & Smart parking \\ \cline{1-1}  & Intelligent buildings \\ \cline{1-1}  & Predictive maintenance \\ \hline \multirow{2}{*}{**Natural resources**} & Environmental monitoring \\ \cline{1-1}  & Natural disasters detection \\ \hline \multirow{2}{*}{**Smart agriculture and farming**} & Agriculture monitoring \\ \cline{1-1}  & Animal monitoring \\ \cline{1-1}  & Silo stock monitoring \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Use cases supported by HARE protocol stack.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
###### Abstract  
Power consumption is a primary interest in Wireless Sensor Networks (WSNs), and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way.  
Keywords:power consumption; reliability; wireless sensor networks; sensitivity analysis; formal models +
Footnote †: journal: Sensors
## 2 Datasets, Feature Extraction, and Classification Methods
### Classification Methods
#### 2.3.2 Stacked Generalization  
Stacked Generalization is an ensemble method where a new model learns how to best combine the predictions from multiple existing models. In this approach, any learning algorithm could be used to combine them.  
In particular, the generation of classifiers that are accurate and diverse is only the first part in an ensemble classifier generation process. The second part (as important as the former one) is the method used to obtain the ensemble outputs by combining the outputs of the base classifiers. Two of the approaches used to combine the outputs of the base classifiers are: (a) majority voting; (b) average of probabilities. Alternatively, there are methods that may be able to learn the so-called combination rules. These methods (called meta-classifiers) are particularly useful when the base classifiers do not have the same success rate (among them) when classifying instances. This may happen when the base classifiers are generated using different training sets or different training algorithms.  
Stacked Generalization (also called Stacking) [22] builds a classifier that takes as inputs values, the output values of the base classifiers, and learns to map these values of the base classifiers into the correct final output value. In other words, no voting strategy is applied in order to combine the predictions of the base classifiers. In this case, a meta-classifier is used. The base classifiers are trained with the training set and the meta-classifier is trained with the predictions of the base classifiers. Figure 2 shows a scheme of one of the stacking approaches used.  
The predictions of the classifiers are obtained from a different partition set than the one used for training. This is achieved by dividing the training set into several partitions. Therefore, Stacking can also be seen as a sophisticated form of attribute extraction. Stacking base classifiers are usually trained using different algorithms. Another strategy would be to use different views, i.e., subsets of attributes obtained by each feature extraction method. This type of Stacking strategy is often called multi-view stacking and has been successfully used when applied to other (but somehow similar) problems [38; 39; 40].  
Figure 3 shows a flow diagram of the general data processing strategy followed in the paper. The figure shows the processing chain divided into two parts. The first of them shows that different types of features and feature combination strategies are obtained from  
Figure 2: Diagram of the Stacked Generalization approach.  
the raw data values, and different classifiers are trained and used to obtain the classification accuracy results for the different datasets. In the second part, these classification accuracy results, for each dataset, are ranked, and the statistical significance of their differences are obtained in order to assess which method is better, and whether the differences among classification strategies are statistically significant or not.
# Research on the Pavement Performance of Slag/Fly Ash-Based Geopolymer-Stabilized Macadam
## 3 Results and Discussion
### Microscopic Test
#### 3.5.3 XRD Analysis  
Figure 8 shows XRD of slag-based geopolymer and slag-based geopolymer added with fly ash (GFA-4) after curing for 28 d. The main components of slag are CaO and SiO\({}_{2}\). A variety of gel phases are produced under the action of alkali activators due to high content of Ca and low content of Si and Al, among which calcium-rich phase products, including calcium carbonate, account for the majority. Calcium carbonate is generated due to carbonation of slag after contacting with air. In addition, diffraction peaks are generated near 27\({}^{\circ}\) and 31\({}^{\circ}\), indicating that it also contains zeolites (NaAlSi\({}_{3}\)O\({}_{8}\), CaAl\({}_{2}\)Si\({}_{2}\)O\({}_{8}\)) and C-A-S-H, N-A-S-H gels. In addition, a large plenty of unreacted Ca(OH)\({}_{2}\) and quartz crystals are also identified. After addition the fly ash containing a large amount of Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\), it is found that the unreacted Ca(OH)\({}_{2}\) in the system was consumed considerably. As a result, the intensity of the diffraction peaks of the generated hydrated calcium silicate, hydrated aluminosilicate, and alkali calcium aluminate crystal improved. This indicates that the hydration and polymerization reaction are improved [34; 35].  
Figure 8: XRD patterns of slag-based geopolymer and slag-based geopolymer added with fly ash.  
Figure 7: Infrared spectrum of slag/fly-ash-based geopolymer-stabilized macadam.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Watermark Extraction Network  
The watermark extraction network in this section is equivalent to the inverse process of the watermark embedding network, as shown in Figure 10. There are two types of input images: one with the watermark information represented as \(I_{a}\), and one without the watermark information represented as \(I_{n}\). Firstly, the two-level DWT on input images to obtain the frequency information of three parts: \(F_{1}^{\prime}\), \(F_{2}^{\prime}\), and \(LL_{2}^{\prime}\). Similar to the embedding process, three independent branch networks are used to extract features of \(F_{1}^{\prime}\), \(F_{2}^{\prime}\), and \(LL_{2}^{\prime}\), and then the extracted features are channel concatenated--the features extracted by the network corresponding to \(F_{1}^{\prime}\) need to be downsampled before channel concatenation.  
Subsequently, the extracted watermark information is obtained through Transformer blocks and convolutional processing. If the input image is \(I_{a}\), containing the watermark information, the extracted watermark information is denoted as \(W_{d}\). If the input image is \(I_{n}\), not containing any watermark, the extracted information is a matrix of the same size as the watermark image, with all values equal to 0, denoted as \(W_{z}\). This is performed to prevent over-fitting of the watermark extraction network. The MSE between the extracted watermark information and Wo is less than the threshold value of 0.01 as the discriminator, determining whether the image contains a watermark or not.
## 3 Design Concept and Operation of Enhanced-ADALINE Algorithm
### Working Principle of ADALINE Module  
Figure 2 provides a block diagram showing the control process of a generalized ADALINE module. According to ADALINE concept, a modified W-H weight updating technique is applied to continuously update two weight factors \(W\) (\(W_{1,sin}\) for sine and \(W_{1,cos}\) for cosine) of the fundamental component. The overall updating process performed by the W-H weight updating technique is as follows:  
\[W(updated)=W(current)+\frac{\gamma e_{c}Y}{Y^{T}Y}, \tag{1}\]  
where \(W=\left[\begin{array}{c}W_{1,sin}\\ W_{1,cos}\end{array}\right]\) represents the weight factor, \(Y=\left[\begin{array}{c}sin(\omega t)\\ cos(\omega t)\end{array}\right]\) represents the fundamental sine and cosine components, \(e_{c}=i_{L}-i_{L,fund,est}\) is the error that resulted between the measured load current \(i_{L}\) and estimated \(i_{L,fund,est}\) signals, and \(\gamma\) is the learning rate.  
In each updating loop, \(e_{c}\) is first computed to update the weight factors which will be applied in the subsequent loops. At the same time, \(i_{L,fund,est}\) will be updated approaching the characteristic of \(i_{L}\) which reduces the resulted error \(e_{c}\). After a few iterations, the estimated \(i_{L,fund,est}\) will form according to the fundamental component of \(i_{L}\). However, updating only two weight factors for the fundamental component cannot completely remove the resulted error \(e_{c}\) because \(i_{L}\) also contains harmonic components. Hence, to minimize this problem, suitable learning rate \(\gamma\) in the range of zero to one (\(0<\gamma<1\)) is added. Anyhow, the best \(\gamma\) value reported for fundamental current estimation is 0.0006 [16; 19]. Note that a fully updated \(i_{L,fund,est}\) will have the following expression.  
\[i_{L,fund,est}=W_{1,sin}\,sin(\omega t)+W_{1,cos}cos(\omega t). \tag{2}\]  
Once \(i_{L,fund,est}\) is fully updated, the weight factors (\(W_{1,sin}\) and \(W_{1,cos}\)) will be applied to compute the magnitude of the fundamental component \(I_{L,fund,mag}\) according to the following approach.  
\[I_{L,fund,mag}=\sqrt{W_{1,sin}^{2}+W_{1,cos}^{2}}. \tag{3}\]
## 3 Network Architecture and Topology  
A distributed WSN-based ITS application accomplishes four different main tasks: (i) acquisition of information, (ii) data distribution, (iii) data processing in order to plan the necessary actions, and finally, (iv) execution of the appropriate actions. Since these tasks may be carried out independently, it can be considered that they correspondingly define four differentiated subsystems which are present in ITS systems, namely, the Sensing subsystem, the Distribution subsystem, the Decision Making subsystem and the Execution subsystem. In this paper these subsystems have been abstracted in a reference architecture depicted in Figure 2, which is inspired by the architectures proposed in [34, 35]. It is defined by placing the different subsystems at separate hierarchical levels, where each subsystem may interact with its neighbors at the immediately higher and lower levels. The architecture of a particular system will be based on the allocation of these subsystems into tiers of distributed and potentially heterogeneous devices. This may result in architectures varying from single tiered architectures, where all the devices perform all the tasks of the system, to multi-tiered architectures, in which each tier of devices specializes in the tasks of one or several subsystems as will be described in Section 3.5. The following subsections describe into more detail each of the four subsystems mentioned above. In addition, Table 3 in Appendix summarizes how the reviewed systems implement these subsystems.
## 5 Results and Discussion  
### Predictive Ability Assessment  
Result of the predictive ability evaluation of the eight predisposing factors is shown in Table 1. It is noted that the 10-fold cross validation was used to ensure the stable assessment result, as suggested in [88]. It could be seen that slope the highest predictive with the average merit (AM) is 0.225, followed by distance to river (AM of 0.171), lithology (AM of 0.148), aspect (AM of 0.129), and elevation (AM of 0.102). In contrast, soil type (AM of 0.038), distance to fault (AM of 0.055), and land cover (AM of 0.077) have low predictive ability values (Table 1).  
The findings are reasonable because slope is widely recognized as the most important factor for landslide in various projects [89; 90]. From the above results, it could be seen that all predisposing factors revealed predictive values to landslide model; therefore, we concluded that they are all relevant factors and are included in this analysis.
## 3 Results and Discussion
### Future Application of ISEs in FGD Processes  
Nitrite and nitrate are of particular concern with the tested FGD wastewater samples. The existing concentrations are an order of magnitude below the lower limit of the electrodes. Furthermore, without dilution the error due to activity would be significant. Chloride and calcium concentrations in the FGD wastewater are high enough that dilution by a factor of 10,100 would still allow for useful measurements. Moreover, there is a relatively low concentration of interfering ions when considering the chloride and calcium ISEs. Further improvements can be made by incorporating developing technologies such as the recent work by [PERSON] et al., but this still requires a description of the ISE behavior over a large range of conditions [11].  
One additional consideration is the form of calcium present. FGD wastewater contains calcium primarily as gypsum, which can precipitate from water easily. Previous research has shown that the activity of calcium as gypsum is not exactly a linear function of ionic strength, but rather has a local minimum at relatively high concentrations (approximately 22,000 PPM) [12]. While this does not restrict the applications of the tested ISEs in this specific FGD wastewater treatment process, processes with higher concentrations of calcium may need more complex models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficients** & **Ca2+ Electrode** & **Cl**– **Electrode** \\ \hline C1 & \(-\)225,080 & 409,810 \\ C2 & \(-\)29,363 & 52,273 \\ C3 & \(-\)33,661 & 82,528 \\ C4 & 776,53 & \(-\)1393,7 \\ C5 & \(-\)63,897 & \(-\)200,910 \\ C6 & \(-\)4266.8 & 10,370 \\ C7 & 101.12 & \(-\)177.72 \\ C8 & \(-\)7828.9 & \(-\)25,655 \\ C9 & 116.02 & \(-\)280.78 \\ C10 & \(-\)10,720 & \(-\)40,428 \\ C11 & 216.09 & 677.06 \\ C12 & 14.705 & \(-\)35.269 \\ C13 & \(-\)127.24 & \(-\)5100.8 \\ C14 & 26.474 & 86.447 \\ C15 & 36.263 & 136.24 \\ C16 & 4.3036 & 17.188 \\ Standard Error (mV) & 0.14106 & 1.1002 \\ Standard Error [M] & \(8.6159\times 10^{-7}\) & \(2.3273\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Calibration Equations for Calcium and Chloride ISE’s.