esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Fabrication Techniques
### Conducting Polymers
#### 3.1.3 Polythiophene Derivatives  
In an effort to find a stable OSC for OECT devices, research recently focused on a third family of conducting polymers, based on sulfur-containing heterocycles. Polythiophene (PT, Figure 3b) and its derivatives, more particularly poly(3,4-ethylenedioxythiophene) (PEDOT) (Figure 3c), have been at the center of the investigations on OECT-based biosensors for the last 15 years. PT derivatives share a good stability towards chemical and electrochemical modification, as well as towards air oxidation [64; 65; 66]. Some of these derivatives were first used in for the fabrication of OECTs by [PERSON] et al. in 1985 [27], again using the same architecture as for their previous works on PPy and PANI, who functionalized Au microelectrodes arrays with poly(3-methylthiophene) and were able to amplify chemical signals such as the reduction of IrCl\({}_{6}\)\({}^{2-}\) to IrCl\({}_{6}\)\({}^{3-}\) at the gate.  
To further improve the characteristics of polythiophene derivatives, new sulfur-containing conducting polymers were designed [64]. One of these derivatives, PEDOT, proved itself of interest in the framework of OECTs. PEDOT is a conducting polymer obtained through the oxidative polymerization of 3,4-ethylenedioxythiophene (EDOT). It exhibits high conductivity (300 S cm\({}^{-1}\)) and high stability in its oxidized state. In addition, PEDOT can be coupled with another polymer, poly(styrene sulfonate) (PSS), to improve its solubility in water (in fact, PEDOT:PSS blend forms a stable colloidal suspension) and also improve its conductivity, by stabilization of the positively charged bipolaron of PEDOT by negatively charged PSS anions (Figure 4).  
Owing to its outstanding characteristics, PEDOT:PSS quickly became one of the most used and studied OSC in the field of OECTs and OECT-based biosensors, and almost completely replaced the aforementioned, less stable OSCs as the choice material for the fabrication of OECTs. This in turn led to a shift in OECT research, from the quest for the most adequate material to the development of improved strategies for the enhancement of the properties of PEDOT:PSS-based OECTs and their fabrication.  
Figure 4: Stabilization of the bipolaron in the PEDOT moiety by the presence of PSS co-polymer moiety.  
Below are discussed some of the strategies employed to enhance the electrical conductivity of PEDOT:PSS, and improve its adhesion to different substrates.  
As the standard conductivity of PEDOT:PSS is intrinsically related to the commercial source of PEDOT:PSS, a table recapitulating all sources of PEDOT:PSS used is available at the end of this section.
## 2 Materials and Methods
### GNSS/INS Heading Determination Techniques
Multirotor UAS can move in any direction with respect to the forward axis and therefore can experience misalignment angles of up to 180\({}^{\circ}\) (Figure 4b). As a result, the GNSS velocity method can produce incorrect GNSS/INS heading estimates for UAS-lidar applications. Equation (7) illustrates one of the conceptual relationships between heading precision and GNSS/INS sensor dynamics: as velocity increases, heading precision improves (Figure 4c). However, this statement is not entirely correct as it oversimplifies the complex nature of GNSS/INS integration. In fact, drift from inertial measurement biases will begin to appear in heading estimates when a sensor is moving at a constant velocity (i.e., direction and speed) [13]. The relationship between heading precision and sensor velocity will be further illustrated within the results of the experiment discussed in Section 3.
# A Universal Malicious Documents Static Detection Framework Based on Feature Generalization
## 6 Experiments and Results
### Experimental Results  
#### 6.3.1 Experiment based on Feature Generalization  
The experimental dataset was divided into a training set (about 80%) and a test set (about 20%), and ten-fold cross-validation was conducted to obtain the average accuracy on the test dataset. The metrics for dichotomous problems are represented by the detection rate (TPR), the false positive rate (FPR), the accuracy rate (ACC), and the F1-score. The experimental results of various documents are reported in Table 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Decision Tree** & **Random Forest** \\ \hline Criterion = “gini” & n\_estimators = 100 \\ Splitter = “best” & Criterion = “gini” \\ max\_depth = None & max\_depth = None \\ min\_samples\_split = 2 & min\_samples\_split = 2 \\ min\_samples\_lea f = 1 & min\_samples\_leaf = 1 \\ min\_weight\_fraction\_leaf = 0.0 & min\_weight\_fraction\_leaf = 0.0 \\ max\_features = None & max\_features = “auto” \\ random\_state = None & max\_leaf\_nodes = None \\ max\_leaf\_nodes = None & min\_impurity\_decrease = 0.0 \\ min\_impurity\_decrease = 0.0 & min\_impurity\_split = None \\ min\_impurity\_split = None & Bootstrap = True \\ class\_weight = None & oob\_score = False \\ Presort = “deprecated’ & n\_jobs = None \\ ccp\_alpha = 0.0 & random\_state = None \\ \multicolumn{2}{c}{verbose = 0} \\ \multicolumn{2}{c}{warm\_start = False} \\ \multicolumn{2}{c}{class\_weight = None} \\ \multicolumn{2}{c}{ccp\_alpha = 0.0} \\ \multicolumn{2}{c}{max\_samples = None} \\ \hline \hline \end{tabular}
\end{table}
Table 6: The default parameters of decision tree and random forest models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Total** & **Benign** & **Malicious** \\ \hline PDF & 500 & 250 & 250 \\ DOC/DOCX & 200 & 100 & 100 \\ XLS/XLSX & 400 & 200 & 200 \\ GIF & 250 & 200 & 50 \\ JPEG & 606 & 300 & 306 \\ PNG & 562 & 262 & 300 \\ RTF & 400 & 200 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The distribution of Dataset2.  
Based on the experimental results, it is evident that the proposed static detection framework for malicious documents based on feature generalization achieved a higher TPR and a lower FPR in the malicious detection experiments of various documents. The TPR of malicious samples and the FPR of benign samples of the classifier obtained by the random forest were found to be better than those of the classifier obtained by the decision tree. It was only in the detection of malicious PNG documents that the detection rate declined. The overall performance of benign sample detection was good, but the FPRs of JPEG and XLS documents were somewhat high.  
In the subsequent experiment, the random forest was used as the classifier. Table 8 reports the detection accuracies of different types of files on Dataset2. The experimental results verify that the proposed method exhibited good scalability performance.
## 1 Introduction
In the present work, we investigate the propagation of a wave packet in monolayer graphene containing a random distribution of nitrogen dopants for various concentrations up to 0.5 at.%. Within the continuum model, the dopants were modeled by attractive potential wells around randomly localized points in the graphene plane, and the Weyl-Dirac equation of motion for the wave packet was numerically solved. These calculations allowed us to investigate how the set of impurities influences the electron transport properties of the doped graphene sample. The transmission of the wave packet across the perturbed region was investigated for various concentrations of dopants. The obtained results suggest that the conductance at a fixed Fermi energy slightly decreases with increasing concentration of dopants. Being a continuous-medium approximation, the model ignores fine details linked to the interactions between impurities, responsible for sublattice segregation observed by STM [49] and predicted theoretically to take place below about 0.2 at.% concentration of N [21]. Also, the model disregards any inter-valley scattering mechanism, because the two Dirac cones of graphene are decoupled. Therefore, the present calculations evaluate the sole contribution of intra-valley mechanism, which dominates the elastic collisions of electrons and holes in graphene on weak-scatter defects. Although less perturbing than its pyridine-like and other forms [50; 51], substitutional nitrogen is not exactly a weak scatterer. This dopant was chosen because of its intrinsic technological importance for some applications of graphene [8; 10]. The results are not expected to be quantitative, because of the approximation made. Nevertheless, the formalism developed here is not without interest, for it may serve as a \"numerical laboratory\" to check sophisticated theoretical formulation of the Drude conductivity of Dirac-Weyl fermions [52; 53]. In fact, the continuous-medium model worked out in this paper is for graphene what the jellium model is for normal metals. It is not necessary to emphasize how much important the physics of jellium is for a qualitative understanding of the metallic state. Let is suffice to mention that the calculations presented below correspond to a system that would represent 40 millions atoms if it were described at the atomic level.
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 3 Materials and Methods
### LoS Probability  
This section builds on the LoS classification method described by [PERSON] [8] and [PERSON] [10], which estimates line-of-sight (LoS) conditions in discrete spatial domains for UAV-based localization. To apply the propagation model described in Section 3.3, it is essential to classify each location fingerprint into line-of-sight (LoS) or non-line-of-sight (NLoS) conditions. However, since continuous fingerprints are used in this study, a probabilistic method is required to infer LoS/NLoS status from surrounding discrete points. Let \(c_{k}\) be a binary variable indicating the LoS status of the \(k\)-th neighbor (1 if LoS, 0 otherwise). For a given particle at position \(\mathbf{u}_{i}\), the probability of LoS is defined as follows:  
\[p_{\text{LoS}}(\mathbf{u}_{i})=\frac{\sum_{k}w_{k}c_{k}}{\sum_{k}w_{k}} \tag{8}\]  
where \(w_{k}=1/d_{ijk}\) is the inverse distance weight between the particle and its \(k\)-th neighboring point. This weighting scheme ensures that closer points contribute more to the LoS probability.  
As illustrated in Figure 5,This approach allows a smooth interpolation of LoS likelihood across continuous space, enabling probabilistic classification of LoS/NLoS and effective application of the propagation model in Section 3.3. Compared to binary thresholding, this method improves stability in environments with uncertain boundary conditions.
### Research Contributions  
Despite the notable advancements in the technology, there are still a few disadvantages in the existing methods. Most of the existing methods classify the pulmonary nodules as either benign or malignant, but if we also identify the subclass, then this will assist the medical practitioner in diagnosing the pulmonary nodules in a better way. Therefore, it is important for an automatic classifier to classify the in-depth classifications of the lung nodules. The main contributions of the proposed research are as follows:  
* The gradient boosting algorithm is modified in a sequential manner by feeding the wrong prediction as the input to the next classifier model, thus improving the efficacy of the classification.
* An effective framework for pulmonary nodule classification from CT images using the modified gradient boosting method for the classification of benign (class 1 and 2) and malignant (class 4 and 5) lung nodules are proposed.
* The random walker method is utilized as the boundary extraction method to segment the pulmonary nodule according to seeds governed by the user.
* Features such as the intensity and texture are extracted using Reisz wavelet coefficients and an LBP filter.
* The performance of the proposed segmentation and classifier model using the modified gradient boosting algorithm method is evaluated by comparing with the existing methods, and is found to be superior.
## 4 Simulation Results and Discussion
### Case 2: Sensitivity Analysis with Criterion Variation Within \(\pm 10\%\)
#### 4.3.2 Scenario 2: Variation in Upload Speed and Download Speed  
In this scenario, in addition to the previous variation in upload speed, we randomly vary the initial download speed values while keeping the values of other criteria unchanged. Table 16 shows the random variation rates applied to the initial download speed values of each satellite. The resulting decision matrix is shown in Table 17.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Satellite ID** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
**Variation rate (\%)** & 10 & \(-5\) & \(-6\) & \(-1\) & 2 & 3 & 10 & \(-10\) & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Random variation rates applied to download speed, for criterion variation within \(\pm 10\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Satellite ID** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
**Variation rate (\%)** & 10 & \(-5\) & \(-6\) & \(-1\) & 2 & 3 & 10 & \(-10\) & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Decision matrix of Scenario 1, for criterion variation within \(\pm 10\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Satellite ID** & **Upload Speed** & **Download Speed** & **Ping [ms]** & **Packet Loss** [\%]** \\ \hline
1 & 11.49 & 188.94 & 49.50 & 0.01 \\ \hline
2 & 12.08 & 220.21 & 62.67 & 0.16 \\ \hline
3 & 12.56 & 213.23 & 62.34 & 2.88 \\ \hline
4 & 15.88 & 221.39
## 3 Geometric Phasor Representation of Periodic Voltage and Current Waveforms  
We start by considering periodic voltage and current waveforms of an arbitrary single-phase system. Both signals can be considered elements of the vector space with inner and outer product. Suppose a non-sinusoidal voltage \(u(t)=\sqrt{2}\sum_{p\in L\cup N}U_{p}\sin\left(p\omega t+\alpha_{p}\right)\) where \(p\) is the harmonic order of \(u(t)\) and a current \(i(t)=\sqrt{2}\sum_{q\in N\cup M}I_{q}\sin\left(q\omega t+\beta_{q}\right)\) where \(q\) is the harmonic order of \(i(t)\). It is assumed that a group of voltage harmonics N exist that have corresponding current harmonics of the same frequencies, that components L of the supply voltage exist without corresponding currents, and that components M of current exist without corresponding voltages. In linear operation, \(\beta_{q}=\alpha_{q}-\phi_{q}\), \(\phi_{q}\)is the harmonic impedance phase angle and \(L=\left\{\varphi\right\},M=\left\{\varphi\right\}\). The capital \(U_{p}\) and \(I_{q}\) represent rms values of \(u_{p}(t)\) and \(i_{q}(t)\). In the \(\{\mathcal{CG}_{n},\odot\}\) structure spanned by an orthonormal basis \(\{\sigma_{1},\sigma_{2},\sigma_{3},\ldots\sigma_{n}\}\), the associated \(p\)-th harmonic voltage and \(q\)-th harmonic current can be represented by the geometric-phasors:  
\[\widetilde{U}_{p}=U_{p}e^{i\alpha_{p}}\sigma_{p} \tag{11}\]  
\[\widetilde{I}_{q}=I_{p}e^{i\beta_{q}}\sigma_{q} \tag{12}\]  
where \(U_{p}=\|\widetilde{U}_{p}\|\), \(I_{q}=\|\widetilde{I}_{q}\|\). Then:  
\[\widetilde{U}=\sum_{p\in L\cup N}U_{p}e^{i\alpha_{p}}\sigma_{p} \tag{13}\]  
\[\widetilde{I}=\sum_{q\in N\cup M}\left\{I_{q}e^{i(\alpha_{q}-\phi_{q})}\sigma _{q}\Big{|}_{q\in N}+\left.I_{q}e^{i\beta_{q}}\right|_{q\in M}\right\} \tag{14}\]  
In particular, on linear operation the Equation (14) is given by \(\widetilde{I}=\sum_{q\in N}I_{q}e^{i(\alpha_{q}-\phi_{q})}\sigma_{q}\) geometric -phasor, where \(\widetilde{I}_{q}\) harmonic current can be decomposed as follows:  
\[\widetilde{I}_{q}=I_{q}e^{i\alpha_{q}}e^{-j\phi_{q}}\sigma_{q}=\widetilde{I}_ {q}e^{-j\phi_{q}}\sigma_{q}=\widetilde{I}_{q}\cos\phi_{q}\sigma_{q}-j\widetilde {I}_{q}sin\phi_{q}\sigma_{q}=\widetilde{I}_{q\|}-j\widetilde{I}_{q\bot} \tag{15}\]  
The subscripts \" \(\parallel\)\" and \"\(\bot\)\" indicates \"in phase\" and \"in quadrature\" respectively. The \(\widetilde{I}_{q}^{*}\) conjugate harmonic current is given by:  
\[\widetilde{I}_{q}^{*}=\widetilde{I}_{q\|}^{*}+j\widetilde{I}_{q\bot}^{*} \tag{16}\]  
where \((*)\) is the standard \"conjugate complex\"operation.
## 4 Methodology
### Feature Creation
Features corresponding to _Features Group 2_ indicate the expected vapor pressure at P2 for the sensed temperature at T2, considering the mixtures from Table 2. _Features Group 3_ groups three categorical features defined in relation to P1, where thresholds were defined based on average P1 pressure values and standard deviations of each group and their relation to measured LPG C5 content. The features in _Features Group 4_ are analogous to the features from the _Features Group 2_, computing the expected T1 temperature based on pressure P1, for LPG mixtures specified in Table 2. These features are used to compute the _Features Group 5_ when contrasted with sensed pressure at P1. The _Features Group 6_ computes the ratio between the estimated T1 temperature, and the pressure at P1. Finally, _Features Group 7_ indicates whether the ratio between the estimated temperature T1 and pressure P1 is greater than the value measured from the diagrams obtained under normal operating conditions. Figure 4: Schematic diagram of an LPG debutanizer column. In the diagram we reference two locations on which the sensors are placed. In this research, we developed models that take into account only sensors P1 and T2. We created a total of 198 features. To avoid overfitting the machine learning models, we selected only \(K\) features, obtaining \(K\) from \(\sqrt{N}\), where \(N\) is the number of instances in the training subset, as suggested by [82]. Feature selection was performed by computing their mutual information [83], and selecting the _top_\(K\) most informative ones. We describe the correlation between the selected features and target C5 content values we aim to forecast in Figures 5-7.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design
### Hybrid Equalizer
#### 3.2.2 Design of Fixed Analog Feedforward Equalizer
As the number of iterations increases, \(i=0,1,\ldots,i_{\max}\), the estimates are more reliable, and the coefficients tend toward 1. Then, we fixed \(\mathbf{\Psi}=\mathbf{I}_{U}\) for the analog part calculations. Therefore, from Equation (A5) we have \(\overline{\mathbf{R}}_{k}=\sigma_{n}^{2}\sigma_{u}^{-2}\mathbf{I}_{U}\), and as \(\mathbf{w}_{a,r}\) has magnitude \(N_{rx}^{-1/2}\) then we have \((\mathbf{w}_{a,r})^{H}\overline{\mathbf{R}}_{k}\mathbf{w}_{a,r}=\sigma_{n}^{2} \sigma_{u}^{-2}\). The first norm of Equation (28) does not depend on \(\mathbf{w}_{a,r}\) while the second one is a correlation involving \(\mathbf{w}_{a,r}\). Therefore, we may use the second norm of Equation (28) as a metric to select the best entry of dictionary, i.e., Equation (25) is equivalent to the following optimization problem:  
\[\begin{array}{rl}\mathbf{w}_{a,r}=&\operatorname*{argmax}_{k=1}^{N_{c}} \|\overline{\mathbf{W}}_{res,k,r-1}\mathbf{w}_{a,r}\|_{F}^{2}\\ &\operatorname*{s
## 1 Introduction  
### Background  
In recent years, significant global efforts have been devoted to developing renewable energy sources to reduce the demand for fossil fuels as well as to limit carbon emissions and air pollution. In particular, distributed and small-scale wind and solar photovoltaic (PV) power generation systems have undergone dramatic growth [1; 2]. Furthermore, microgrids (MGs) have attracted attention owing to their potential to provide electricity in a reliable, economical, efficient, and environmentally-friendly manner from distributed energy resources (DERs) [3; 4]. MGs provide an effective means of overcoming the intermittency of DERs and enabling bidirectional transactions. Moreover, they contribute to limiting carbon emissions, allow for the diversification of energy sources, and reduce cost [5].  
At present, the majority of electrical energy consumed is provided by nuclear or fuel power plants with high capacities and reliability. However, the costs of investing in such power plants remains very high. Hence, DERs implemented at a smaller scale have been attracting considerable interest,as they incur smaller capital requirements as well as have a lower environmental impact [6]. However, the increased use of renewable energy and high-efficiency distributed generation (DG) sources in power systems has resulted in power generation systems becoming smaller. Furthermore, these power systems are located closer to consumers on MGs, which comprise distribution networks (DNs) for a range of energy resources such as fuel cells, wind turbines (MTs), combined heat and power (CHP) systems, and PV systems [7]. The incorporation of DG sources into a network offers numerous benefits with respect to the overall performance, provided that these sources are optimally scheduled and coordinated [8]. An important challenge regarding MGs is the optimization of their operation, which involves determining the best means of generating value for each unit to maximize the profit. In this regard, DG sources that are capable of CHP production are being used extensively in MGs as well as with DERs that are stochastic in nature [9]. Since power generation by DERs includes an element of uncertainty, it is necessary to forecast their output. This topic formed part of a previous study in which the load uncertainties and those associated with DERs were considered [10].
## 3 Results  
### Lightning Transient Voltages  
#### 3.1.1 Field Test Overview  
Considering the randomness of lightning strikes, the measuring system was installed for a certain duration in a 220 kV Yan-Zhuang overhead transmission line connecting the Yanshenshang substation (Y-substation) and the Zhuangshang substation (Z-substation) with 78 towers and the length of 39.8 km. The area of the transmission lines has an average ground flash density about 1.012 times/km\({}^{2}\) in 2015 and 2.155 times/km\({}^{2}\) in 2016. Measuring systems were arranged on the entrances of Y-substation and Z-substation respectively.  
The causes for the transient voltage in a substation are various, including the operations, lightning strikes, electro-magnetic interferences, etc. To distinguish the actual lightning transient voltage, the measuring systems are synchronized with the lightning location system of China (LLS). The LLS includes over 940 lightning detection terminals covering most areas of China. Its average detection efficiency is higher than 90% and the location errors are smaller than 500 m. The errors of GPS clock used in the measuring system and LLS are both within one hundred nanoseconds.  
A ground flash event usually contains the first stroke and several subsequent strokes. The period between two continuous strokes is usually longer than 30 ms [22]. Since the length of Yan-Zhuang line is 39.8 km, the difference of the triggering time of the measuring systems and the LLS will not be longer than 150 \(\upmu\)s. Thus, the recorded data is considered to be a lightning transient voltage waveform caused by an involved ground flash, while the measuring system is triggered and the LLS has recorded a ground flash near the Yan-Zhuang line in 1 ms. Moreover, both the event that the measuring equipment is triggered and a ground flash strike to the point near the monitored transmission line are of small probabilities. When two small-probability events happen almost at the same time, it is believed that these two observed processes are the same event.
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
## 1 Introduction
It can measure the methane concentration of landfill gas within a range of 10 m and a time less than 25 s. After assembling the apparatus, the relationship between methane concentration and the optical signal was calibrated in the laboratory-based on Beer-Lambert law. After this, the in situ testing of methane emissions was performed at a large-scale landfill in China to demonstrate the accuracy of the TDLAS method.