esa-sceva/satcom-chunk-collection huggingface dataset:
Sensors_**2014**, _14_, 11993-12022; doi:10.3390/s140711993  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**On the Network Convergence Process in RPL over IEEE 802.15.4 Multihop Networks: Improvement and Trade-Offs**  
**[PERSON] \({}^{1,2,\star}\) and [PERSON] \({}^{1}\)**  
\({}^{1}\) Universitat Politecnica de Catalunya/Fundacio i2 Cat, C/Esteve Terradas, 7, 08860 Castelldefels, Spain; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Engineering, Toyserkan Branch, Islamic Azad University, Toyserakn, Hamedan Province, 65816-85184, Iran  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-934-137-218.  
_Received: 12 April 2014; in revised form: 20 June 2014 / Accepted: 25 June 2014 / Published: 7 July 2014_  
**Abstract:** The IPv6 Routing Protocol for Low-power and Lossy Networks (RPL) has been recently developed by the Internet Engineering Task Force (IETF). Given its crucial role in enabling the Internet of Things, a significant amount of research effort has already been devoted to RPL. However, the RPL network convergence process has not yet been investigated in detail. In this paper we study the influence of the main RPL parameters and mechanisms on the network convergence process of this protocol in IEEE 802.15.4 multihop networks. We also propose and evaluate a mechanism that leverages an option available in RPL for accelerating the network convergence process. We carry out extensive simulations for a wide range of conditions, considering different network scenarios in terms of size and density. Results show that network convergence performance depends dramatically on the use and adequate configuration of key RPL parameters and mechanisms. The findings and contributions of this work provide a RPL configuration guideline for network convergence performance tuning, as well as a characterization of the related performance trade-offs.  
**Keywords:** RPL; IPv6; IEEE 802.15.4; network convergence; Trickle; RPL parameters
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Distribution Network Flexibility Indicators
#### 2.3.3 Distributed Generation Volatility  
DG volatility refers to the degree of volatility in its power output over a period of time. Since the output of DG is impacted by a variety of factors, including weather conditions, equipment failures, equipment life, and so on. The level of volatility has an important impact on the stable operation and planning of ADN [20].  
To reduce the DG volatility, effective planning and management can be implemented to enhance the reliability and quality of the equipment so as to optimize the operation of ADN. Meanwhile, the use of energy storage devices can help balance the volatility and improve the flexibility of the grid. The DG volatility formula is as follows:  
\[H_{bd}=\frac{1}{T}\times\frac{1}{n-1}{\sum_{t=1}^{T}\sum_{i=1}^{n}H_{bd,i}^{t }\times 100\%} \tag{15}\]  
\[H_{bd,i}^{t}=\left(P_{i}^{t}-P_{av}\right)^{2} \tag{16}\]  
where \(H_{bd}\) is DG volatility; \(n\) is the number of data spots; \(H_{bd}^{t}\) is the volatility of new energy per unit of time; \(P_{i}^{t}\) is the practical generating power of DG in the current period; \(P_{av}\) is the average power generation of DG in a period of time.  
A higher volatility indicates that the output of DG is more variable and the power quality may be more unstable; conversely, it indicates a more stable generation. DGvolatility is usually considered in power system planning or grid connection requirements to facilitate power system scheduling and management.  
Two-Tier Coordinated Planning of SOP and ESS Considering Flexibility Enhancement of Active Distribution Networks  
Considering the impact of strong stochasticity and volatility of WT and PV on coordinated planning, it is necessary to generate a scenario of WT and PV based on C-DCGAN, and then use GA-PSO and cone planning algorithms to carry out two-layer coordinated planning based on the flexibility and economy of ADN using SOP and ESS on the basis of this scenario.
## 3 The Use of the Lozi Map in Electrical Devices, Memristors, and Computers
### Memristors
#### 3.3.1 Memristor-Based Lozi Map with Hidden Hyperchaos  
Nowadays, the discrete memristor model is a well known research hotspot. Many researchers have devoted themselves to the analysis of chaotic phenomena in discrete memristors. Recently, hidden attractors have been discovered in discrete memristor-based maps [50]. Wang et al. [51] included a discrete-time memristor to create a memristive Lozi map. This new 3D memristor-based Lozi map was established by coupling a discrete memristor to the original 2D Lozi map (3):  
\[\left\{\begin{array}{l}x_{n+1}=1-a|x_{n}|+y_{n},\\ y_{n+1}=bx_{n}+ky_{n}sin(z_{n}),\\ z_{n+1}=y_{n}+z_{n},\end{array}\right. \tag{19}\]  
where \(k\) is the real-valued control parameter coupling gain between the discrete-time memristor and the Lozi map.  
Because there are no fixed points and hyperchaos can emerge, the memristor-based Lozi map is a hidden hyperchaotic map.  
For certain specific control parameters, the 3D memristor-based Lozi map can show heterogeneous and homogeneous hidden multistability. It should be noted that heterogeneous hidden multistability implies the coexisting behavior of multiple hidden attractors of different stability types, while homogeneous hidden multistability indicates the coexisting behavior of multiple hidden attractors of the same stability type that only differ in dynamic intervals. In addition to the coexistence of these heterogeneous hidden attractors, the memristor-based Lozi map is very likely to produce the coexistence of homogeneous hidden hyperchaotic attractors, i.e., homogeneous hidden multistability. Therefore, the homogeneous hidden hyperchaotic attractors from the 3D memristor-based Lozi map can be robustly controlled by the memristor's initial conditions.  
Additionally, Wang et al. implemented this memristor in a digital circuit based on a high-performance microcontroller. They physically obtained an image of the hyperchaotic hidden attractors using a digital oscilloscope. Eventually, a digital platform was exploited and its experimental phase portraits were obtained to confirm the numerical portraits.
## 2 Model Introduction
### AWOA-DBiLSTM Neural Network Modeling
#### 2.4.3 AWOA-DBiLSTM Model Deployment  
The schematic of the AWOA-DBiLSTM network model proposed in this paper is depicted in Figure 8. In this model, the learning rate was set to 0.01, which was chosen to ensure the stability of the model during the training process and to avoid dispersion of the model during the training process. The epoch was set to 200 to ensure that the model had more chances to learn the features and patterns of the training data and, at the same time, to reduce the risk of overfitting the model. The batchsize was set to 64, and the number of BiLSTM layers of the deep model was set to two layers based on the data characteristics of the present study. This study first preprocessed the AUV trajectory data, including removing abnormal and redundant entries from the dataset and dividing them into training and testing sets. The segmented dataset was then fed into the model to train the model and predict the trajectory. BiLSTM, DBiLSTM, and AWOA-DBiLSTM were selected for experimental comparison to validate the feasibility of the model, with Root Mean Square Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE) used as evaluation metrics for the model.  
The dataset used for this study was obtained through the AUV's built-in navigation system, system depth sensors, and attitude control equipment [35]. The AUV collects various types of data through sensors and instrumentation during autonomous navigation in the water. The acquisition process involves collecting one piece of data every two minutes, and each piece of data includes the time, underwater acoustic data, water parameters (temperature, salinity, dissolved oxygen, etc.), the navigational trajectory (latitude, longitude, depth), and sailing attitude (pitch angle, yaw angle, roll angle). In this study, factor analysis was used to select the most relevant features for trajectory prediction and to determine the time, positional trajectory, and physical attitude to be selected as the experimental data to be input into the model. In addition, the preprocessed dataset was divided in a ratio of 8:2 to obtain the training set and test set for experiments.  
In the AWOA-DBiLSTM model, data are preprocessed and then fed into the DBiLSTM model for training. The DBiLSTM model contains two BiLSTM layers: Firstly, the physical attitude information of the AUV is extracted by the first BiLSTM layer. Subsequently, the noise information is filtered out using a gating mechanism, and then input to the second BiLSTM layer to fuse with the position information and optimize the neural network with AWOA to achieve the training of this model. Finally, the test set is input to the trained model for prediction and the model performance is assessed by evaluation metrics.
## 1 Introduction
### Layer Level Feature Classification  
Literature has investigated layer-by-layer evaluation for material extrusion methods [38; 39] and DED applications [40]. In [38], [PERSON] et al. focus on residual strain issues that occur during the extrusion process. In the investigation, researchers measure the solidification-induced residual strain distribution via a novel optical backscatter reflectometry (OBR) based fiber-optic sensing system was embedded to measure distributed residual strains. [PERSON], et al. create a self-monitoring system with real-time camera images to evaluate interlayer imperfections during the build process for a fused deposition modeling (FDM) print [39]. Researchers trained a CNN that was capable of predicting delamination and warping issues in real-time with an accuracy of 97.5%.  
Other studies have focused on layer evaluation techniques involving image-based detection systems. [PERSON] et al. investigated layer-wise image-based evaluation for defect detection during metallic powder bed fusion with supervised ML [41]. The technique included using a high-resolution digital single-lens reflex camera to capture images at each layer during a build process. Using a Support Vector Machine (SVM), features are extracted from the imagery dataset and classified as either a 'flaw' or 'normal' class. These two classes are used to represent either an \"undesirable interruption in the typical structure of the material or a nominal build condition\" (respectively) [41]. The authors utilized ex-situ CT scans to identify incomplete fusion, porosity, cracks, or inclusions and successfully built a 3D high-resolution dataset of defects. The SVM was capable of detecting defects in situ with greater than 80% accuracy. Another study employed a similar tactic of utilizing high-resolution optical imagery to detect defects in situ during a metal AM build process [42]. In this investigation [PERSON], et al. focuses on Lack-of-fusion defects from parts built using the PBF approach. In this approach, the algorithm is designed to create a 3D volumetric image of defects that is then compared to a computer-aided design (CAD) model to determine sensitivity and specificity metrics. In total, 28 intentional defects were placed throughout the part varying in size and shape. Sensitivity was calculated as 0.915 and specificity calculated as 0.840. In [43], [PERSON] et al. developed a CNN that was capable of detecting deformations in large-scale AM concrete construction. The technique uses a segmentation process by applying a binary mask to RGB input images to extract concrete layers. Defects are detected with 97.5% accuracy.
## 2 Materials and Methods
### Functional Effect of SNPs Identified with MR  
In order to obtain information on which SNPs contributed to the results observed with MR analysis, we used the mr_singlesnp function implemented in the TwoSampleMR package. The function obtains the MR estimates using each of the SNPs singly as it performs the analysis multiple times for each exposure-outcome combination, each time using a different single SNP. We used the default method to perform the single SNP MR (Wald ratio). Results from these analyses were shown with a forest plot in which we compared the MR estimates using different MR methods against single SNP tests (i.e., the MR estimates obtained using a single IV). SNPs showing a significant effect for a casual effect of the exposure on the outcome in single SNP tests were further investigated in order to obtain information on their potential functional effect. We searched variants in RegulomeDB to obtain the probability score. The RegulomeDB probability score is computed integrating functional genomics features along with continuous values such as ChIP-seq signal, DNase-seq signal, information content change, and DeepSEA scores. The score ranges from 0 to 1, with 1 being most likely to be a regulatory variant [49]. Furthermore, we searched whether SNPs acted as expression quantitative trait loci (eQTL) based on genotyping and gene expression data from Genotype-Tissue Expression (GTEx) v.8 in brain regions [50]. In the GTEx project, gene expression was measured in a range of 114-209 participants (based on the selected tissue) with Illumina TrueSeq RNA sequencing or Affymetrix Human Gene 1.1 ST Expression Array, while genotyping data were obtained with whole genome sequencing, whole exome sequencing, Illumina OMNI 5M, 2.5M or Exome SNP arrays. We reported cis eQTLs significant based on false discovery rate (FDR). In addition, we investigated whether proteins encoded by the genes for which significant eQTLs were identified showed a protein-protein interaction (PPI) enrichment using STRING [51]. A significant PPI indicates that the identified proteins have more interactions among themselves than would be expected for a random set of proteins of the same size and degree distribution drawn from the genome, suggesting they are at least partly biologically connected.
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
## 4 Discussion
### Effect of Biochar on Soil Properties and Metal(oid)s Concentration
#### 4.1.2 Total Metal(loid)s  
The high content of metal(loid)s in the unvegetated soils of the Guadiamar Green Corridor is a consequence of the continuous contamination process characterized by the oxidation of the remaining tailing in the soils [43]. The concentration of metal(loid)s in the contaminated soil treatments showed very high values compared to the values found in the non-contaminated soil, especially for As and Pb (Table 1). The only exception is represented by the Zn content. In fact, in NC soil high concentrations of this metal have been detected, even higher than those found in RA soil. These values of concentrations for Zn in NC soil were in the range of geochemical background values in the area [44], and the low values in RA may be related to the acid pH of this soils, which increased the leaching process of this element over time, lowering the total concentration in the polluted soil in relation to the non-polluted one [33]. However, in all treatments the total Zn content remained below the limits established by the Regional Government to declare a soil as contaminated (10,000 mg kg\({}^{-1}\)) [45].  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Shoot BAF** & **RA** & **RA4B** & **RA8B** & **NC** \\ \hline
**Pb** (mg/kg) & 1139.01 & 171.96 & 75.58 & 0.41 \\
**As** (mg/kg) & 215.27 & 68.82 & 30.47 & 10.36 \\
**Zn** (mg/kg) & 111.60 & 48.04 & 17.81 & 13.73 \\
**Cu** (mg/kg) & 9.02 & 2.51 & 1.40 & 4.97 \\ \hline
**Root BAF** & & & & \\ \hline
**Pb** (mg/kg) & 584.27 & 3214.57 & 1326.78 & 3.93 \\
**As** (mg/kg) & 77.38 & 941.96 & 390.50 & 64.85 \\
**Zn** (mg/kg) & 16.34 & 62.01 & 21.69 & 23.28 \\
**Cu** (mg/kg) & 5.83 & 10.63 & 5.01 & 12.52 \\ \hline \end{tabular}
\end{table}
Table 5: Bioaccumulation factor (BAF) in root and shoot, in the non-contaminated soil (NC), in the unamended contaminated soil (RA), and in soil amended with biochar at 4% (RA4B) and 8% (RA8B).  
Regarding the total Cu content in RA, the values found do not differ much from the geochemical background values of the area [43], and in all treatment remained below the limits established in Decree 18/2015 of the Regional Government to declare a soil contaminated by this element (595 mg kg\({}^{-1}\)). By contrast, total concentrations of As and Pb found in RA, RA4B, and RA8B exceed the regulatory levels established (275 mg kg\({}^{-1}\) for Pb, 36 mg kg\({}^{-1}\) for As) to declare a contaminated soil by the Regional Government [45], representing a threaten for the soil biota as evidenced in previous works [46].  
The high concentrations of these two elements are related to their low mobility in soils, in the case of As, this element was strongly retained in the soils by iron oxides in the affected area [33,46]. In the case of Pb, the precipitation in the polluted soils is related to the retention by lead hydroxysulphates such as plumbojarose [47].
## 1 Introduction
Fully synthetic methods, on the other hand, do not retain any real data, potentially offering enhanced privacy protection. Lastly, imputation methods focus on filling missing values by leveraging available data, typically resulting in datasets that mix real and imputed values. Imputation may utilize only the incomplete dataset or, alternatively, include a complete reference dataset to enhance the generated values. Building on the concept of partially synthetic data, a recent study has proposed algorithms that use visualization tools, in particular UMAP, to validate and enhance synthetic data generation [24]. This approach trains a UMAP model on reference data and then generates synthetic samples for one or various attributes. A range of synthetic values are assigned to the attribute that is being generated, and the resulting samples are transformed into the UMAP space. Therefore, the generated values are validated by comparing the UMAP transformation of reference data with th  
Figure 1: (**A**) Fully synthetic data. (**B**) Partially synthetic data. (**C**) Imputed data. Synthetic values in light gray and real values in dark gray. However, while partially synthetic data reduce certain privacy risks, they may still retain identifying characteristics of the original data. This study seeks to expand upon prior work by adapting the partially synthetic generation method for fully synthetic data, which does not contain any real data elements, thereby ensuring a higher level of privacy protection while maintaining data utility. The primary aim of this paper is to adapt our previously proposed algorithm, initially designed for partially synthetic data generation, to enable the generation of fully synthetic data. This adaptation allows for the creation of datasets that contain no real data components, thus offering an elevated level of privacy protection. By iteratively applying the generation process across all features, the algorithm ensures that the generated dataset is entirely synthetic, expanding its potential applicability. The structure of this paper is as follows. Section 2 outlines the methodological adaptations for fully synthetic data generation, detailing algorithmic steps, datasets used, evaluation metrics, and potential ethical considerations. Section 3 presents the experimental results obtained by applying the algorithm to a healthcare dataset, alongside a comprehensive quality assessment of the synthetic data. Finally, Section 4 discusses the strengths, limitations, and potential directions for future research.
## 4 Sensor Trajectory Design
### Single Sensor Trajectory Design Procedure  
We summarize the sensor trajectory design procedure in the following algorithm.  
Trajectory design based localization algorithm:  
Initialization: Use the initial position of the sensor to generate the initial estimator parameter by localization algorithm, e.g., Kalman-related methods.  
Repeat until n is reached:  
1. Generate \(\mathbf{S}_{k+1}\) through the maximization of (69) or (72). The maximizer of (69) or (72) can be produced in an analytical way described in Section 4.4;
2. Gather the measurement information \(\hat{\phi}_{k+1}\), \(\hat{\theta}_{k+1}\), \(\hat{\sigma}^{2}\) and \(\xi^{2}\) from the sensor at \(\mathbf{S}_{k+1}\);
3. Use localization technique to generate \(\mathbf{\hat{T}}^{k+1}\);
4. \(k=k+1\).  
Output the target location.  
In many applications, various location-based services need to determine accurate positioning information, such as for public safety services, field rescue and other fields. It is necessary to track the target under motion constraints. Unlike static sensors, which have a fixed density and perception range, mobile sensors can cover a larger area over time without increasing their number. In addition, their spatial distribution can change dynamically to accommodate the movement of the target, thus providing measurements of information about its position. Choosing the best sensing location is particularly important, especially given time-constrained applications such as tracking hostile targets. For example, autonomous underwater vehicles (AUV) are underwater unmanned autonomous vehicle platforms without a cable connection that can be applied for underwater environment monitoring, offshore oil engineering operations, underwater search and mapping after loading appropriate sensors. In the main-slave UUVS (Unmanned Underwater Vehicles) system, the slave-UUV only carries the azimuth information of the main-UUV under a cluttered environment and realizes the convergence with the main-UUV as soon as possible through autonomous optimization of its movement trajectory. The localization method and trajectory planning method proposed in this paper can be applied to the above scenarios.