esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Initial Analyses and Findings
### The Factor Analysis
#### 4.3.3 Factor Scores  
After the extraction and rotation of the factors are carried out, factor scores can be calculated depending on the study. If the factor analysis is to be followed by a subsequent analytical procedure, then the use of factor scores essentially enhances the research. The factor scores are the estimated values of each variable within each factor if they are measured directly [64]. In other words, each factor score assigned to a variable represents the underlying theory of the study. Considering this study, the factor scores of the variables represent the plasticity potential of each soil. As a glimpse into the proceeding stage of this study, the higher the score means, the higher plasticity potential, thus, generally creating more problematic soil.  
The theoretical perspective suggests that the calculation of the factor scores can be conducted in infinite ways [72]. Essentially, the factor scores are computed based on the factor loadings of each variable within the factor. Therefore, a high-loading variable will result in a higher score. As [PERSON] [72] suggested, there are many ways to calculate the factor score. These include the simple way of summing the factor loadings or taking the mean of these loadings to represent each variable. However, more sophisticated methods also exist: the regression approach, Bartlett method, and Anderson-Rubin approach. These methods tend to investigate the intercorrelations of the variables and factor loadings to yield a quantity called factor score coefficients. Among them, the main differences are related to the correlations of the factor scores. Such as the regression approach generates highest correlations between factors and factor scores. [PERSON]'s method relates the factor scores considering only their own factor, and Anderson-Rubin produces uncorrelated scores.  
In this study, the five-soil property was chosen for the investigation. These properties are essentially related and proved to be correlated as well. The produced factors must also be correlated based on the aim of the study. Considering the theoretical ways of producing the factor scores and the goal of the research, regression approach was chosen to assign a factor score for each soil. The corresponding factor score coefficients based on the regression approach is given in Appendix A.
# Currency Crises Prediction Using Deep Neural Decision Trees
## 2 Methodology
### Research Steps  
The empirical research for predicting currency crises involved five steps: Creating the sample, data preprocessing, model construction, accuracy assessment, and classification and prediction as shown in Figure 2. The first step (sample creation) was based on obtaining the relevant data from the data sources such as information published by international economic bodies. The attributes of the dataset include measurements of exposure to debt, the external sector, domestic macroeconomic factors, the banking sector, and political attributes. The data preprocessing step involved making the attributes with continuous values discreet, generalizing data and analysis of the relativity of the attributes, and eliminating outlier values. Regarding outliers values, since the deletion of elements of the sample implies a loss of information, only the ends that do not belong to the interval have been suppressed:  
\[(\text{Q}_{1}-3\text{R}_{\text{Q}},\text{Q}_{3}+3\text{R}_{\text{Q}}) \tag{9}\]  
where \(\text{Q}_{1}\) is the first quartile, \(\text{Q}_{3}\) is the third quartile, and \(\text{R}_{\text{Q}}\) is interquartile range.  
The step of constructing the model was based on inductively learning from the preprocessed data using the DNDT algorithm defined in Section 2 and choosing the significant independent variables via the proposed sensitivity analysis. To do so, the sample was randomly divided into three mutually exclusive datasets: Training (70%), validation (10%), and testing (20%). This process used the 10 fold cross-validation method with 500 iterations to estimate error ratios [37]. The first subset of data was used to train the models and estimating the parameters. The second subset was used for model selection. Finally, the third dataset (testing) was used to evaluate the predictive accuracy of the model in the accuracy assessment step. This was complemented by the analysis of the model's robustness and its predictive capacity for currency crises at the global level in the classification and prediction step. All variables used in this study were considered in every dataset of training, validation, and testing data  
Figure 2: Flowchart of the research.
# Toward a State-of-the-Art of Fly-Rock Prediction Technology in Open-Pit Mines Using EANNs Model
## 6 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Model** & **MAE** & **MAPE** & **RMSE** & **VAF** & **R\({}^{2}\)** & **Rank for MAE** & **Rank for MAPE** & **Rank for RMSE** & **Rank for VAF** & **Rank for R\({}^{2}\)** \\ \hline ANN & 3.227 & 0.019 & 5.71 & 97.242 & 0.974 & 4 & 5 & 2 & 3 & 2 \\ ANN & 3.162 & 0.019 & 5.55 & 97.349 & 0.975 & 6 & 5 & 4 & 4 & 3 \\ ANN & 3.202 & 0.02 & 5.684 & 97.225 & 0.975 & 5 & 4 & 3 & 2 & 3 \\ ANN & 3.322 & 0.021 & 5.459 & 97.437 & 0.975 & 2 & 2 & 6 & 6 & 3 \\ ANN & 3.300 & 0.021 & 5.945 & 96.919 & 0.973 & 3 & 2 & 1 & 1 & 1 \\ ANN & 3.568 & 0.022 & 5.541 & 97.373 & 0.975 & 1 & 1 & 5 & 5 & 3 \\ EANNs & 2.277 & 0.017 & 4.346 & 98.464 & 0.986 & 7 & 7 & 7 & 7 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical indexes of the models for a set of data in the testing. Although the EANNs' performance is outstanding; however, the effects of the input parameters need to be taken into account to evaluate the importance of the inputs for predicting FR. Accordingly, the sensitivity of the input parameters was analyzed, as shown in Figure 10. Eventually, ST and W were found to have the most effect on the training of the proposed EANNs model, whereas the remaining parameters (i.e., PF, S, B) also affect the model, but not much.
###### Abstract  
This paper investigates the problem of the time-varying formation control of a second-order dynamic agent based on a distributed dynamic event-triggered algorithm. In this problem, each agent can exchange the information of its position and velocity with its neighbors via limited communication ability. Our approach provides a new dynamic event triggering mechanism to reduce the number of triggering times while maintaining satisfactory control performance. Further, a novel Lyapunov function is proposed to guarantee that the group of agents asymptotically tracks the desired time-varying formation trajectory. The practical applicability of the event triggering mechanism is also indicated by excluding the Zeno behavior in the proposed control algorithm. Finally, the validity and effectiveness of the proposed method are demonstrated via illustrative examples of the time-varying formation flight for six quadcopters.  
distributed formation control; dynamic event-triggered control; multiple quadcopter systems; time-varying formation Article
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 2 Research Methodology
### Inclusion Phase  
The documents were categorized into two different groups, RGS-centered and network communication-centered. A qualitative synthesis was performed for all documents, and according to the objective, quantitative synthesis was made.  
The initial search yielded a total of 7032 documents. These documents were evaluated and filtered according to the PRISMA methodology. In the final phase, 113 documents of interest were selected, and quantitative and qualitative analysis was performed for such documents. The query text is shown in Figure 1. The next section provides a summary of research concerning RGSs, along with an assessment of application prerequisites and scenarios of use.
## 1 Introduction
Section 3 provides a comprehensive study of typical noises in PLC networks for AMI, including normative noises, noises measured in actual operational deployments of the Spanish DSO (Distribution System Operator) Union Fenosa Distribucion, and noises coming from DG and EV equipment measured in the LINTER (Grid Interoperability Laboratory) [39], also from Union Fenosa Distribucion. Section 4 reviews different noise mitigation techniques used in PLC networks, paying special attention to one of the preferred solutions by DSO for this purpose: the use of filters. Although filter design is a well-known topic, the filters for this application have to meet constrained requirements, such as bearing currents up to tens of amperes while staying compact and low-cost, or presenting high input impedance. Thus, Section 4 presents a filter specifically designed to facilitate assessing the suitability of several filter configurations to different problematic scenarios in AMI. Section 5 indeed describes the setup and methodology followed in the set of tests that were carried out in the LINTER to evaluate the effectiveness of the aforementioned configurable filter to mitigate the effects of a selection of the most representative noises presented in Section 3. Section 6 presents the spectral measurements taken during these tests. Section 7 briefly introduces PRIME Analytics [40], a novel software tool that allowsfor network forensics, and analyzes the effects of the noise sources and filter configurations in PRIME network performance using it. Section 8 discusses the main contributions and findings of the paper. Finally, Section 9 wraps up and draws conclusions. The configurable filter and the network forensics tool PRIME Analytics represent novel developments that have been carried out within the scope of the Spanish R&D project OSIRIS [41]. The presented tests and analysis have been also carried out within the scope of the OSIRIS project and aim to serve as a token of how the gathered measurements can be used to learn through experimentation. Nevertheless, the noise records are available together with the paper so that they can be used in theoretical and simulation works to further improve PLC network performance in Smart Grid scenarios.
## 3 Materials and Production
### Summary  
According to the estimations, approximately 160-180 kton of silica sand is used each year in Sweden directly as bed material for FBC. Most of this silica sand is subsequently disposed of without being further utilized. This is a significant amount and corresponds to about 20% of the total Swedish silica sand production. It can be argued that this alone is a reason to start considering alternatives. Figure 8 presents the estimated potential for slag-based bed materials compared to current estimated silica sand consumption.  
Figure 7: Copper production in Sweden and Finland between the years 2004–2017, in kton.  
Iron ore could be an interesting alternative as bed material. There are two products (magnetic fines and hematite fines) produced today at a large scale, both of which could potentially be utilized as bed material with no or just minor further processing. Thus, even though the selling price for iron ore fines can be expected to be higher than the by-products discussed here, it is not necessarily so that the final cost for using the material is higher. The cost for material preparation is discussed in Section 4.1. Out of the by-products discussed, water granulated copper smelter slag (Jarnsand) might have the largest potential when it comes to production volume and potential bed material yield. There is already a large production of granulated slag today, and apart from that, there is a significantly larger amount of air-cooled slag which is barely utilized at all. As far as the authors are aware, neither LKAB's iron ore fines nor Boliden's Jarnsand, or similar materials, have been evaluated as bed material in small- or large-scale experiments. The next step should therefore be to conduct tests to determine their properties. Activity with oxygen and gaseous fuel can easily be determined in small-scale experiments. Mechanical properties such as agglomeration tendency and mechanical integrity can be determined in large-scale experiments or in lab-scale experiments simulating extreme conditions. Before that is done, not much more can be concluded about their suitability, but comparisons to similar materials can be done. Copper smelter slag contains a significant amount of iron, more than for example LD-slag which has proven to have sufficient oxygen-carrying capacity in OCAC. Apart from iron oxide, the main components of copper slag are Si-, Ca- and Al-oxide. Ca- and Al-oxides are known to reduce the propensity for agglomeration. Si-oxides are known to react with alkali and phosphorous from the fuel and make up the main part of available conventional bed materials. There is no apparent reason to believe that the main components of Jarnsand should cause any direct problems in the boiler or when handling the bottom ash. The same can be said about iron ore fines.  
Another by-product that shows good potential when it comes to volume is LD-slag. The main drawback for LD-slag might be the large amounts of fines produced when crushing the material, leading to the need for intensive sieving activities and a large material loss.
## 7 Current Challenges and Future Research Directions
### Large-Scale Network with More than One Controller  
The increasing expansion of network capacity and controller scalability owing to computation limit is another significant difficulty. The stability and scalability of a multi-controller platform must be prioritized. The controllers in a distributed controllers scenario are conceptually central. The main controller is a root with a global view of the network, and the others are local controllers with network information only in their domain. However, synchronizing these controllers is another issue that must be addressed. Proposing an LDDoS attack method based on machine learning effective in large-scale networks with more than one controller is a challenge for the SDN community.
## 2 Materials and Methods
### Methods for Calculating the Quality Factor (Q)
#### 2.3.3 The ICRP Report 60 Recommendation  
For radiation protection purposes, the International Committee on Radiation Protection (ICRP) Report 60, expressed the quality factor (Q) as a function of LET in water, \(Q(L)\). The particular mathematical expression of \(Q(L)\) was deduced on the basis of various in vitro and animal radiobiological studies [3]. The recommended LET dependence of \(Q\) is as follows:  
\[Q(L)=1,\ L<10\ \mathrm{keV}/\upmu \tag{31}\]  
\[Q(L)=0.32L-2.2\,\ 10\mathrm{keV}/\upmu\leq L\leq 100\ \mathrm{keV}/\upmu \tag{32}\]  
\[Q(L)=\frac{300}{\sqrt{L}},\ L>100\ \mathrm{keV}/\upmu \tag{33}\]
# Travel-Time Inversion Method of Converted Shear Waves Using RayInvr Algorithm
## 2 Data and Methodology
### Method  
In this study, we apply the RayInvr algorithm [17] for crustal S-wave velocity inversion. RayInvr allows the flexibility of ray tracing and inverts different converted S-waves phases, simulating different mode conversions. It also has the ability to switch between forward modelling and inversion, to allow Vp/Vs to vary within model layers.  
Based on asymptotic ray theory of the zeroth order, the ray-tracing equations (Equation (1)) were solved by Runge-Kutta method [21] with error control, and Snell's Law is applied to model interfaces. In Equation (1), \(\theta\) is the angle between the tangent of the ray and the z axis, \(v\) is the wave velocity, \(v_{z}\) and \(v_{x}\) are the partial derivatives of the velocity on the \(z\) and \(x\) axis, respectively. The initial values of \(x\), \(z\) and \(\theta\) are \(x_{0}\), \(z_{0}\) (shooting coordinates) and \(\theta_{0}\) (take-off angles of rays).  
\[\frac{\mathrm{d}x}{\mathrm{d}z}=\tan\theta.\frac{\mathrm{d}\theta}{\mathrm{d}z }=\frac{v_{z}\tan\theta-v_{x}}{v} \tag{1}\]  
Equation (2) is the discrete form of the travel-time equation, where \(l_{i}\) and \(v_{i}\) are the path length and velocity of the \(i\)-th ray segment. Equation (3) is derived from Equation (2) to linearize by Taylor series expansion, and the higher-order terms are neglected. In Equation (3), A is the partial derivative matrix, \(\Delta m\) is the model parameter adjustment vector, and \(\Delta t\) is the travel-time residual vector. The model parameter adjustment vector was calculated in each inversion and used to update the model. After multiple iterations, inversion will be terminated when the preset criteria are reached, then an optimum inverted model will be obtained.  
\[t=\sum_{i=1}^{n}\frac{l_{i}}{v_{i}} \tag{2}\]  
\[\Delta t=A\Delta m \tag{3}\]Figure 3: The forward P-wave velocity model along profile NS5. Gray circles represent OBS locations.  
Figure 2: (**a**) Vertical seismic profile from OBS 06, with a reduction velocity of 6 km/s and a band-pass filter of 3–10 Hz (see Figure 1 for location). (**b**) The phase picking and fitting of OBS 06. The coloured lines represent picked travel times, and the thin, black lines represent calculated travel times from the final velocity model. (**c**) Ray tracing for OBS 06. Different line colours correspond to different phases.
## 3 System Description
### Energy Model  
The network energy dissipation model [39], seen in Figure 4, is employed in the proposed model. The energy consumption for sending and receiving a \(l\)-bit message across a distance of \(d\) is described by Equations (1) and (2).  
\[E_{tx}(l,d)=\begin{cases}IE_{elce}+l\in_{fs}d^{2}&d\leq d_{0}\\ IE_{elce}+l\in_{mp}d^{4}&d>d_{0}\end{cases} \tag{1}\]  
\[E_{rx}(l,d)=lE_{elce} \tag{2}\]  
where E\({}_{\text{elec}}\) represents the power consumed by the sender's electrical circuits, \(\in_{fs}\) indicates the energy that sensor nodes utilize to send data over free space channels (line of sight), and the energy consumption of data transmission over the multipath channel is \(\in_{\text{mp}}\).  
The threshold distance computation d\({}_{0}\) is given in Equation (3)  
\[d_{0}=\sqrt{\in_{fs}/\in_{mp}} \tag{3}\]
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods
### Performance Metrics  
The accuracy and precision of the 3-D VFI method were assessed in terms of mean relative bias and standard deviation of velocity estimates, as proposed in [29]. For each point of interest \(\text{P}(x_{\text{p}},y_{\text{p}},\)\(z_{\text{p}})\), located at distances \(\leq\) (\(0.9\times\text{R}\)) from the flow axis, the mean relative bias of the axial velocity component, \(v_{z}\), is defined as  
\[\overline{B}_{v_{z}}=\frac{1}{\bar{v}_{max}}\frac{1}{N_{p}}\sum_{p=1}^{N_{p}} \left(\bar{v}_{z}\big{(}x_{p},y_{p},z_{p}\big{)}-\overline{v}_{z}\big{(}x_{p}, y_{p},z_{p}\big{)}\right) \tag{1}\]  
where \(\overline{v}_{z}\big{(}x_{p},y_{p},z_{p}\big{)}\) and \(\bar{v}_{z}\big{(}x_{p},y_{p},z_{p}\big{)}\) are the temporal means of the estimated and reference velocity components, respectively, while \(N_{p}\) is the number of points of interest. \(\bar{v}_{max}\) is the reference peak velocity (7.5 cm/s). On the other hand, the mean relative standard deviation was calculated as  
\[\overline{\sigma}_{v_{z}}=\frac{1}{\bar{v}_{max}}\sqrt{\frac{1}{N_{p}}\sum_{ p=1}^{N_{p}}\sigma_{v_{z}}\big{(}x_{p},y_{p},z_{p}\big{)}^{2}} \tag{2}\]  
where  
\[\sigma_{v_{z}}\big{(}x_{p},y_{p},z_{p}\big{)}=\sqrt{\frac{1}{(N_{F}-1)}\sum_{ u=1}^{N_{F}}\bigl{(}v_{z}\big{(}x_{p},y_{p},z_{p},t_{u}\big{)}-\overline{v _{z}}\big{(}x_{p},y_{p},z_{p}\big{)}\big{)}^{2}} \tag{3}\]  
is the standard deviation evaluated for each velocity component averaged over the total number of frames \(N_{F}\). Similarly, mean relative biases and standard deviations for the lateral velocity components, \(v_{x}\) and \(v_{y}\), and for the velocity module, \(v_{Module}\), were obtained through equivalent equations.
# Uncertainty-Aware Active Meta-Learning for Few-Shot Text Classification
## 1 Introduction
The proposed technique, Uncertainty-Aware Meta-Learning, focuses on selecting high-quality tasks from the available data during the learning process. Additionally, a new loss function is designed to re-weight the importance of each task based on the model's uncertainty about the input data, supporting model training in low-resource natural language understanding environments. This method represents a significant advancement in meta-learning, offering a potential solution to some of the most pressing issues in the field. Several studies have explored uncertainty quantification in NLP, particularly leveraging Bayesian inference, probabilistic modeling, and ensemble methods [9; 10; 11; 12], discussing the use of Bayesian inference, probabilistic modeling, and ensemble methods. However, most do not address the challenges of low-resource NLP scenarios. The primary contribution of this study is the extension of meta-learning techniques, previously focused mainly on computer vision, to the field of natural language processing to address this issue. This paper is expected to contribute to the following subject:  
* We propose a novel active meta-learning approach that prioritizes high-uncertainty tasks and introduces a new Uncertainty-Based Sample-Balanced (USB) loss for better task generalization, summarized in Figure 1.
* We extend meta-learning to NLP, demonstrating improved performance in few-shot sentiment (ARSC) and relation classification (FewRel) tasks. * We introduce uncertainty quantification for task selection and validate UA-AML's effectiveness with improved performance in few-shot NLP scenarios.
## 4 MHAR-OLSR Protocol
### CPN MHAR-OLSR Model  
This section describes the colored Petri net modeling technique used to implement the MHAR protocol concept. In particular, the model developed in this paper is referred to as the MHAR-OLSR protocol.  
The model in Figure 2 presents the main module of the hierarchical CPN model describing the generic behavior of the protocol's algorithm:  
* The topology of the network is defined as a set of tokens in the place \"Topology\". Every token is a triplet containing a bow connecting two nodes (N1, arc, N2). The order of the nodes in this triplet tells us the direction of the flow.
* After detecting the presence of the data, the place \"Found Data\" ensures the activation of \"_Path_Calculation_\" and \"_Path_Choice_\" sub-models.
* The places \"Found Path tree\" gives the list of the calculated paths. That represents the input arc expressions with those of the \"Identified Neighbor\" of the sub-model \"_Path_Classification_\".
## 3 Results and Discussion
### Optimization of HS-SPME
#### 3.2.7 The Effect of Desorption Time  
Optimizing the desorption time can prevent the insufficient relaxation caused by an inadequate desorption time, which affects the subsequent extraction. [PERSON] et al. (2016) investigated the desorption time in six time periods (2-7 min). Some components were not completely desorbed within 2 min. With the extension of the desorption time, the total peak area, main peak area and total peak number increased and reached the platform at 5 min, indicating that the best resolution time is 5 min. In addition, the TN and TA of volatile compounds did not show obvious distinction from 4 to 7 min, and the analyte in the fiber showed sufficient desorption within 5 min. It could be inferred that highly volatile compounds can be decomposed within 2 min because they are scarcely affected by the desorption time. The low volatile composition can be decomposed gradually with the extension of analytical time, and the sulfur-containing compounds can be completely decomposed within 5 min.