esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Soil Washing with Ferric Chloride on Cadmium Removal and Soil Structure
## 4 Conclusions  
According to our results, FeCl\({}_{3}\) could effectively remove Cd from contaminated soil. The removal efficiency increased with the decrease in soil initial pH, as adjusted by FeCl\({}_{3}\). However, the low pH of 2.0 caused partial loss of soil mineral components, with an Al release of 4.4% in the FeCl\({}_{3}\)-treated soil versus 1.3% in the HCl-treated soil. This is an optimum tradeoff for restoring Cd-polluted soils by adjusting the initial pH to 3.0 with the addition of FeCl\({}_{3}\). Under this condition, the amount of Al loss was less than 0.2%, and the extraction efficiency reached 40.3%, compared to an efficiency of 39.7% with HCl at an initial pH of 2.0. The addition of different agents caused no variation in the soil mineral species. However, the washing agents caused a loss of soil TOC, which was 27.1% and 17.5% in the pH 2.0 and 3.0 FeCl\({}_{3}\) treatments, and 15.5% in the initial pH 2.0 HCl treatment.  
Data curation, Q.S.; formal analysis, Y.Z.; funding acquisition, J.Y.; methodology, L.X.; project administration, J.D.; writing--original draft, X.Z.; writing--review and editing, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (ES201808), the National Key Research and Development Program Project (2019 YFC0408602), the National Natural Science Foundation of China (21806120), the Key Research and Development Projects of Shanxi Province (201903D311004), and the Shanxi Province Science Foundation for Youths (201901D211027).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Results  
In this study, the RSIDI was extracted for 849 sub-basins over the five major Korean river basins using a BPCA-based combination model for the three drought indices for 2001-2019. As a result of the evaluation of the proportion of variation (POV) of the three drought indices by region, the BPCA-based RSIDI explained the average POV (68.9%) of the 849 sub-basins (Han River basin: 68.1%, Nakdong River basin: 68.7%, Geum River basin: 71.3%, Youngsang River basin: 71.7%, and Sumjin River basin: 71.0%), showing a high POV, especially in the southern part of the country. In addition, the calculated RSIDI showed a relatively high correlation with SPI (median: 0.96) and WBDI (median: 0.96). In the case of the ADCI (maximum: 0.91, median: 0.53), the correlation with RSIDI was broad in the region and showed a relatively weak correlation in some areas of the Han and Nadkong River basins; however, on an average, the correlation was 0.53 (\(p\)-value \(<\) 0.001) in 849 sub-basins, indicating that the RSIDI can provide robust and comprehensive integrated drought information by maintaining the inherent characteristics of the three drought indices (Figure 3). By resolving the system equations for each drought index, it has been shown that the relative contribution of the RSIDI to each time-series can be assessed. Figure 4 illustrates the relative contribution of each drought index to the Gojicheon stream (#10011) of the Han River basin.  
In the following section, the droughts in 2001 and 2014, which are the representative severe drought years of severe droughts that have occurred since the 2000s, were assessed using the RSIDI produced by multi-sensor satellite data and multivariate analysis. The application of integrated drought monitoring based on satellite data was evaluated through spatial-temporal variability analysis between the RSIDI and other drought indices using the ROC analysis to test the accuracy of the models. In addition, the onset, intensity, and evolution patterns of droughts were compared to each drought index, and the applicability of the RSIDI was evaluated through an IBB simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Drought Condition** & **SPI** & **ADCI** & **WBDI** & **RSIDI** \\ \hline Normal & \(>\)0 & \(>\)40 & \(>\)0 & \(>\)0 \\ \hline Attention & \(-\)1.0–0 & 30–40 & 0\(--\)0.5 & \(-\)1.0–0 \\ \hline Caution & \(-\)1.0\(--\)1.5 & 20–30 & \(-\)0.5—1.0 & \(-\)1.0—1.5 \\ \hline Alert & \(-\)1.5—2.0 & 10–20 & \(-\)1.0—1.5 & \(-\)1.5—2.0 \\ \hline Serious & \(<\)\(-\)2.0 & 0\(\sim\)10 & \(<\)\(-\)1.5 & \(<\)\(-\)2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of the drought indices used in this study.  
Figure 3: Results of correlation analysis with RS-based integrated drought index (RSIDI) through Bayesian PCA (BPCA); (**a**) SPI (standardized precipitation index), (**b**) ADCI (agricultural dry condition index), and (**c**) WBDI (water budget-based drought index). The lower panel in the figure results from the analysis of the correlation of 849 sub-basins summarized in the boxplot and is illustrated by each applied drought index namely, SPI, ADCI, and WBDI.
# Statistical Characterization of Supraharmonics in Low-Voltage Distribution Networks
## 2 Materials and Methods
### GOF Evaluation: Adjusted Determination Coefficient and \(Q\)-\(Q\) Plots  
The adjusted determination coefficient (ADC) is calculated to quantitatively assess the GOF of each distribution [28; 29; 30], and it is the metric used to select the most appropriate distribution for each data sample.  
The ADC is defined from the determination coefficient (DC); if the domain of the random variable is divided in \(B\) bins for GOF assessment purposes, the DC is:  
\[DC=1-\frac{\sum_{i=1}^{B}\ \Big{(}\frac{N_{b_{i}}}{N}-p_{b_{i}}\Big{)}^{2}}{ \sum_{i=1}^{B}\Big{(}\frac{N_{b_{i}}}{N}-\overline{N}_{b}\Big{)}^{2}}, \tag{16}\]  
where \(p_{b_{i}}\) is the theoretical cumulative probability (calculated from the estimated distribution) of the random variable to belong to the \(i\)th bin \(b_{i}\), \(N_{b_{i}}\) is the number of data samples that are observed and lie in the \(i\)th bin \(b_{i}\), and:  
\[\overline{N}_{b_{i}}=\frac{1}{B}\sum_{i=1}^{B}N_{b_{i}}. \tag{17}\]  
The ADC is introduced to calibrate the DC by the number of parameters involved in the estimated distributions, as more parameters typically tend to better capture the shape of the empirical data in characterization purposes but may be less useful out-of-sample as they tend to overfit the data. The ADC is defined as:  
\[ADC=1-(1-DC)\frac{N-1}{N-n_{p}}, \tag{18}\]  
where \(n_{p}\) is the number of parameters of the distribution.  
The DC and ADC maximum value is 1; the closer they are to 1, the better is the fit. In this paper, the ADC is considered for GOF purposes in order to discern also by the number of parameters involved in the distribution.  
The quantile-quantile (Q-Q) plots are further used for a graphical interpretation of the GOF. Q-Q plots illustrate the empirical data quantiles versus the theoretical quantilesdetermined by the estimated distribution. Q-Q plots that resemble a straight line (i.e., the bisector of the first quadrant) are more prone to identify an acceptable GOF.
# Practical Application of Mesh Opportunistic Networks
## 5 Discussion  
Several reasons have been put forward to explain why mesh OppNets are not deployed extensively. It is true that the companies developing these systems do not have a clear implementation policy, the battery consumption reduces the autonomy of the devices and above all, a free communication infrastructure represents a clear threat to the monopoly of telephone operators [38]. The results regarding the communications and coordination of professional teams were indeed useful, once the devices were configured and linked to the mobile phones through the dedicated app.
# Application of Machine Learning Techniques to
###### Abstract  
Climatic data archives, including grid-based remote-sensing and general circulation model (GCM) data, are used to identify future climate change trends. The performances of climate models vary in regions with spatio-temporal climatic heterogeneities because of uncertainties in model equations, anthropogenic forcing or climate variability. Hence, GCMs should be selected from climatically homogeneous zones. This study presents a framework for selecting GCMs and detecting future climate change trends after regionalizing the Indus river sub-basins in three basic steps: (1) regionalization of large river basins, based on spatial climate homogeneities, for four seasons using different machine learning algorithms and daily gridded precipitation data for 1975-2004; (2) selection of GCMs in each homogeneous climate region based on performance to simulate past climate and its temporal distribution pattern; (3) detecting future precipitation change trends using projected data (2006-2099) from the selected model for two future scenarios. The comprehensive framework, subject to some limitations and assumptions, provides divisional boundaries for the climatic zones in the study area, suitable GCMs for climate change impact projections for adaptation studies and spatially mapped precipitation change trend projections for four seasons. Thus, the importance of machine learning techniques for different types of analyses and managing long-term data is highlighted.  
climate zone; climate change impact; Jhelum River Basin; Chenab River Basin
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 3 Results and Analysis
### The Relationships of the Models  
The relationships of the three team-communication models are illustrated in Figure 1. The investigation model provides analysis requirements to the process model and the full set of influencing factors to the performance-factor model. The process model receives the analysis requirements from the investigation model and uses them to refine the functions' aspects to ensure the comprehensiveness of the communication analysis. It provides the performance-factor model with the basis of the selection of performance factors from the full set of influencing factors to analyze the performance of an accident scenario. Then, it receives back the suitable performance factors to perform a team-communication analysis.  
The performance-factor model receives the complete set of communication influencing factors from the investigation model and combines them with its standard performance factors. Next, the suitable performance factors can be selected based on the basis given by the process model that specifies a certain accident scenario. The selected performance factors are then returned to the process model to be used to perform communication analysis.  
While the investigation and performance-factor model can be updated, they are generally static and act as influencing/performance factors providers to the process model. The process model is the main part of team-communication analysis, and it is developed based on FRAM, with modifications and additions to suit the communication analysis.  
Figure 1: The relationships of the team-communication models. The team-communication investigation model is presented in [29], samples of the process model are provided in Figures 4, 6, and 7, and the performance factors are listed in Table 1.
# Optimal Arrangement Strategy of IoT Sensors in Urban Drainage Networks: A Review
## 3 Arrangement of IoT Sensors in the Drainage Network
### Optimization of Monitoring Point Locations
#### 3.3.4 Design Evaluation Methods Based on Complex Network Theory  
Most optimized IoT sensor arrangements are designed based on the time-series datasets of monitoring points [22] or pollutant transport and dispersion distributions in the pipeline network [35,39,58] based on SWMM pipeline network hydrodynamic simulations. Other methods have also been adopted instead of hydrodynamic models, focusing on the topological structure of drainage networks. The placement of sensors was optimized by evaluating the degree to which each node was affected by abnormal events in the pipeline network. [PERSON] et al. [85] analyzed the potential and feasibility of complex network theory (CNT) in the assessment of the vulnerability and resilience of UDNs, the design of optimal monitoring points, and the study of the propagation of pollutants. They also proposed a series of topology-based indexes and coefficients that were utilized to guide the optimal placement of sensors. This approach considers the relative position and connections between the nodes and facilitates the optimal arrangement and evaluation of the sensors without performing hydraulic simulations, which serves as an alternative approach to solving these types of problems.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 4 Deep Learning-Based Modulation Recognition Methods
### Introduction to the VLC MFR Datasets
They constructed a channel model incorporating scattering, absorption, and turbulence effects to generate constellation diagram samples. Fifteen modulation formats were selected, covering M-ary QAM, M-ary ASK, M-ary PSK, and BPSK. Within the SNR range of 6-15 dB, with a step size of 3 dB, 1200 original constellation diagram samples were generated. The samples were converted to grayscale and resized to 28 \(\times\) 28 pixels. A total of 300 samples were selected to form the dataset. The training set and validation set had different modulation formats. During the training phase, the dataset was further divided into a support set and a query set. The support set contained samples and labels of known classes for model tuning. The query set contained samples of the same classes for aggregating and optimizing parameters to achieve knowledge transfer and generalization. Currently, research teams primarily generate datasets based on their own simulated or experimental VLC systems, which vary in architecture and channel conditions. This results in objective differences in model performance across different datasets, affecting the universality of research findings. The number and types of modulation formats considered by each team also differ, but they generally cover common formats such as PSK and QAM. Constructing a comprehensive dataset that encompasses current mainstream VLC systems and their modulation formats is a monumental task. As VLC technology advances, unique modulation formats continue to emerge, and the expansion of dataset categories will rely on the spirit of open-source collaboration among research teams. Moving forward, we advocate for the widespread open-sourcing of VLC modulation recognition datasets. We encourage teams with comprehensive equipment and experimental systems to collect VLC-MFR signal sample datasets and hope to see these datasets made open-source. This will promote improvements in VLC-MFR technology under real-world channel conditions. Before VLC achieves a balance between performance and efficiency and is widely applied on a large scale, different system architectures will coexist objectively. Therefore, the open-sourcing of simulation datasets is also welcomed by researchers. Simulation datasets offer high flexibility in construction and can be iteratively updated quickly once open-sourced, which is conducive to forming a more complete VLC model and dataset and driving the vigorous development of VLC MFR technology.
###### Abstract  
The direct position determination approach was recently presented as a promising technique for the localization of a transmitting source with accuracy higher than that of the conventional two-step localization method. In this paper, the theoretical performance of a direct position determination estimator proposed by [PERSON] is examined for situations in which the array model errors are present. Our study starts from a matrix eigen-perturbation result, which expresses the perturbation of eigenvalues as a function of the disturbance added to the Hermitian matrix. The first-order asymptotic expression of the positioning errors is presented, from which an analytical expression for the mean square error of the direct localization is available. Additionally, explicit formulas for computing the probabilities of a successful localization are deduced. Finally, Cramer-Rao bound expressions for the position estimation are derived for two cases: (1) array model errors are absent and (2) array model errors are present. The obtained Cramer-Rao bounds provide insights into the effects of the array model errors on the localization accuracy. Simulation results support and corroborate the theoretical developments made in this paper.  
Keywords:direct position determination (DPD); array signal processing; array model errors; mean square error (MSE); success probability (SP); Cramer-Rao bound (CRB) +
Footnote †: journal: Sensors
# Trajectory Optimization for UAV-Aided IoT Secure Communication Against Multiple Eavesdroppers +
## 2 System Model and Problem Formulation
### Problem Formulation  
The study aims to enhance physical layer security in an IoT communication system with multiple eavesdroppers by optimizing the UAV's flight trajectory. Considering the worst-case scenario in confidential transmission, our objective is to maximize the minimum security rate at the communication destination. Accordingly, the original optimization problem is formulated as follows:  
\[\begin{split}\text{\bf P0:}&\max_{\{x_{k}(n),\,y_{k }(n)\}}\min_{k}\,\sum_{n=1}^{N}R_{g}^{\text{sec}}(n)\\ &\text{s.t.}&\eqref{eq:11}-\eqref{eq:12}.\end{split} \tag{12}\]  
It is noteworthy that, due to Equation (12), problem \(P0\) is an inherently non-convex optimization problem. Thus, directly solving this problem poses great computational challenges. To simplify this, the original problem will be transformed and reformulated in the following section, enabling an optimal solution to be derived using convex optimization techniques.
## 4 Internal Dissipative Acoustic Fluid Equations in the Frequency Domain  
### Internal Dissipative Acoustic Fluid Equations  
The internal fluid is called a _dissipative acoustic fluid_, and is assumed to be homogeneous, compressible, dissipative, and at rest in the reference configuration. This dissipative acoustic fluid--for which the dissipation due to thermal conduction is neglected and for which the motions are assumed to be irrotational--is driven by the Helmholtz equation with an additional internal dissipative term. For additional details concerning dissipation in acoustic fluids, we refer the reader to [2; 4; 6; 9]. Let \(\rho_{0}\) be the mass density and let \(c_{0}\) be the constant speed of sound in the acoustic fluid at equilibrium in the reference configuration \(\Omega\). The Helmholtz equation with a dissipative term and a source term \(Q\) is written as [41]  
\[\frac{1}{\rho_{0}}\,\boldsymbol{\
abla}^{2}p+i\omega\frac{\tau}{\rho_{0}} \boldsymbol{\
abla}^{2}p+\frac{\omega^{2}}{\rho_{0}c_{0}^{2}}\,p=-\frac{1}{ \rho_{0}}(i\omega Q-\tau c_{0}^{2}\boldsymbol{\
abla}^{2}Q)\quad\text{in}\quad \Omega\,, \tag{5}\]  
in which \(\tau\) is given by  
\[\tau=\frac{1}{\rho_{0}c_{0}^{2}}\Big{(}\frac{4}{3}\eta+\zeta\Big{)}>0\,. \tag{6}\]  
The constant \(\eta\) is the dynamic viscosity, \(\
u=\eta/\rho_{0}\) is the kinematic viscosity, and \(\zeta\) is the second viscosity which can depend on \(\omega\). Therefore, \(\tau\) can depend on frequency \(\omega\). To simplify the notation, we write \(\tau\) instead of \(\tau(\omega)\). Taking \(\tau=0\) in Equation (5) yields the usual Helmholtz equation with an acoustic source for wave propagation in an inviscid acoustic fluid. It should be noted that the dissipation term is proportional to \(\boldsymbol{\
abla}^{2}p\) and not to \(p\) (what modifies the boundary condition defined after), and that the acoustic source term exhibits a \(\boldsymbol{\
abla}^{2}Q\) term.
# A Network Traffic Prediction Method for AIOps Based on TDA and Attention GRU
## 5 Experimental Results and Analysis
### Feature Comparison Experiment  
To explore the advantages and disadvantages of the combination of topological features and spatio-temporal features, this study used the GRU model to predict the received traffic value in the next 24 h and uses the original features, PCA features, ICA features, TSNE features, and features extracted by TDA + CNN as training data to carry out the first group of comparative experiments. The experimental results are listed in Table 3. In Table 3, the best and worst values for each evaluation indicators are marked in bold.  
It can be seen from Table 3 that the model trained with TDA + CNN features is significantly better than the model trained with other features in MSE, \(R^{2}\), 10% ACC, and other evaluation indexes and was similar to the model trained with TSNE features and ICA features in MAPE and 15% ACC but still better than both. The model trained with PCA features achieved the worst score in most indicators, which shows that PCA is not as good as other comparison methods in feature dimensionality reduction of time-series data. It is worth noting that the score of the original feature is also better than that of PCA, which indicates that PCA loses some useful information in the process of dimensionality reduction of the data, resulting in a decline in the prediction performance of the model.  
From the perspective of dimension reduction principle, PCA maps high-dimensional data to low-dimensional space through linear projection. TSNE enables similar objects to have a higher probability of being selected. CMPA [27] selects effective features with the help of chaotic sequences. In this paper, spatial features are combined with temporal features, which obviously have stronger abilities in feature expression.  
In conclusion, the TDA + CNN feature training model achieved the best score on most evaluation indexes, which proves the feasibility of applying this method to time-series prediction and the powerful ability of this method in feature extraction and data dimensionality reduction.