esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Work  
N-type OTFT was successfully fabricated using solution deposition techniques with fullerene C\({}_{60}\) as the semiconductor channel and was used in the fabrication of humidity and chloride ions sensors for corrosion monitoring applications. The OTFT has low threshold voltage, large charge carrier mobility, and good current ON/OFF ratio. Furthermore, it has low power requirement, relative environmental stability, and experiences little to no losses. Hydrogenated graphene was successfully synthesized using the Birch method and was used along with rGO as the sensing materials for chloride ion and humidity sensors, respectively. The organic sensors exhibit high sensitivity to target analytes, low power requirements, and excellent repeatability. The humidity sensor shows an almost instant response to the presence of surface moisture and very fast recovery. The chloride ion sensor can detect very low concentrations compared to corrosion initiation thresholds. This further proves the chemical affinity of hydrogenated graphene toward Cl\({}^{-}\). These simple devices are reliable and economically attractive candidates for corrosion prevention techniques and can be the starting point for the design and fabrication of a range of OTFT sensors integrated in common sensor nodes and targeting corrosion precursors and early on by-products. Each sensor node includes a humidity sensor to ensure that the adjacent sensors operate in dry conditions only, and prevent the likelihood of errors and false measurements stemming from sensing in humid conditions. This study demonstrates the potential of solution-processed n-type organic thin film transistor sensors using graphene derived materials in non-destructive inspection for structural health monitoring applications. However, further research and optimization are needed for the realization of robust, environmentally stable, and reliable fully organic corrosion sensors.  
Conceptualization, R.P. and M.C.; Methodology, M.C., S.B. and R.P.; validation, M.C. and R.P.; formal analysis, M.C.; investigation, M.C. and R.P.; resources, R.P.; writing--original draft preparation, M.C., S.B. and R.P.; writing--review and editing, R.P. and M.C.; visualization, M.C.; supervision, R.P.; project administration, R.P.; funding acquisition, R.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the NSERC Discovery grant [#2018-04154] and the NRC research contract [#9555842]. The APC was funded by NSERC Discovery grant [#2018-04154]. The authors acknowledge the financial support from National Research Council (NRC) of Canada and NSERC Canada. The authors are thankful to [PERSON] (NRC Ottawa) for her guidance towards identifying corrosion precursors of concern to the aircraft industry.  
The authors declare no conflict of interest.
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites
### Synthesis of GO Alginate Beads  
[PERSON] et al. [56] prepared alginate beads of GO by adding 0.2 g sodium alginate in a 5 wt.% GO suspension in deionized water and then deposited dropwise into a 5 wt.% calcium chloride solution. The alginate beads that were formed were kept in the solution for 3 h. These beads were used for the removal of metals and organic compounds, with maximum adsorption capacities reaching 588.2 mg/g bead.
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 2 Formulation of Analytical Model
### Numerical Simulation  
A three-dimensional finite element model was established by ABAQUS. The seismic fortification intensity is 8 degrees, the layer height is 4500 mm, and the span is 7200 mm. The model and the number of beams, columns, and joints are shown in Figure 2. Both columns and beams are made of Q235 steel. Further details of the beams and columns are shown in Table 1.  
As a result of material aging and environmental corrosion, the structure will inevitably face a variety of structural damage such as cracking, spalling and so on. These damages can change the stiffness of the components and affect the safety of the building structure. In this paper, damage was simulated by decreasing the stiffness of the column. In the detection of damage location, the stiffness of column 1, column 6, and column 11 was reduced by 20%. In the detection of damage degree, the stiffness of column 1 was reduced by 10%, 20%, 30% and 40%, respectively.
# Connected Vehicles Security: A Lightweight Machine Learning Model to Detect VANET Attacks
###### Abstract  
Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied--using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors.  
VANETs, CICIDS2017; connected vehicles; cyber security; internet of things security; vehicular ad hoc networks; balanced dataset; imbalanced dataset; random oversampling; DoS; machine learning; deep learning; feature selection +
Footnote †: journal: 2025, 16, 324  
_Article_
## 4 Datasets
### Layout-Analysis Dataset  
Most of the current layout-analysis tasks are carried out with document images as the research objects, and therefore most of the datasets proposed by researchers are also specific to document layout images. Table 4 shows the presentation and comparison of a range of document-layout-analysis datasets proposed by researchers in recent years.  
PubLayNet is a high-quality document-layout dataset generated by automatically annotating the document layouts of PubMed CentralTM PDF articles, which covers typical document layout elements and is suitable for training a model for recognizing scientific document layouts. DocBank is a large-scale dataset built using a weakly supervised approach that enables document-layout-analysis models to integrate textual and visual information for downstream tasks. The IIIT-AR-13K dataset is very effective as training data for solving the detection of graphical objects in business documents and technical articles. ReadingBank, a document-image dataset containing reading-order, text and layout information, is the first ever large-scale dataset and develops the power of neural networks for reading-order detection. TNCR is a dataset based on the analysis of tables in images, which can help in the study of table detection, structure recognition and classification in document-image layout. NewsNet7 is a real-world newspaper-image dataset that is primarily used to analyze the state of various complex layouts in document layouts. LIE is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
an information-extraction dataset that focuses on the extraction of structural and semantic knowledge from visually rich documents.  
These datasets have been constructed primarily for the automatic detection of layout elements such as tables, text, captions, graphics, etc. in document images, and have contributed significantly to the field of document-image-layout analysis. Nevertheless, these datasets still have some flaws and shortcomings. Some of the currently available datasets do not take into account the relationships among the elements of the analyzed layout. The number of data in some datasets is relatively small, which makes it difficult to satisfy the training samples of large-scale deep-neural-network learning models and to solve other problems.
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 3 Path Generation Algorithm for Graders
### Work Path Generation Algorithm
#### 3.3.3 Calculate Repeated Rate and Effective Width  
Next, it was necessary to calculate the repeated rate and effective width. A repeated rate, as mentioned earlier, is required to achieve work completeness by partially overlapping the blade. The degree of blade overlap is termed repeated rate. To maintain high overall quality, the repeated rate should be consistent across all work lines, meaning that the repeated rate for each line is kept the same. Before calculating the exact repeated rate, a range for repeated rate is provided, and within this range, the repeated rate should be minimized to achieve the minimum number of work lines. For work along the outer boundary line, because the equipment tends to extend beyond the outer boundary line, work is started at a distance equal to the width of the equipment to account for its effective width. Thus, given the total road width \(W_{r}\), width of the equipment attachment \(W_{a}\), the number of work lines \(n\), and the repeated rate \(x\), the following relationship holds:  
\[W_{r}=W_{a}\{1+(n-1)x\}W_{a}+2s \tag{1}\]  
Figure 9: Outer line coordinate data frame.  
The equation involves two unknowns, \(x\) and \(n\). To satisfy the equation, we start with \(n=2\) and increase the number of work lines until the repeated rate \(x\) falls within the given range and satisfies the equation. The effective width, which is the valid width for the blade to operate, is denoted as \(W_{e}\), and is calculated as follows:  
\[W_{e}=W_{a}(1-x) \tag{2}\]
# Mechanical Performance Characterization of Lignin-Modified Asphal Mixture
###### Abstract  
Lignin, as a bio-based waste, has been utilized in the asphalt industry due to various advantages. This study aimed to investigate the effects of two lignin products (lignin powder and lignin fiber) on the mechanical properties of asphalt mixtures. The raveling, rutting, thermal and fatigue cracking resistance, and moisture susceptibility of different asphalt mixtures were respectively evaluated by the Cantabro test, wheel loading tracking test, semicircular bending test, four-point beam bending test, and freezing-thaw cyclic test. Results show that asphalt mixture with lignin powder-modified asphalt improved the overall mechanical performance. However, lignin fiber showed contradictory effects on certain mechanical properties, i.e., improved rutting resistance and thermal cracking resistance of asphalt mixture, degraded abrasion resistance, fatigue performance, and moisture stability. Therefore, cautions need to be taken when incorporating lignin fiber into asphalt mixture.  
lignin; asphalt mixture; mechanical performance +
Footnote †: journal: Applied Sciences
# Enhancing Clay Soil's Geotechnical Properties Utilizing Sintered Gypsum and Glass Powder
## 1 Introduction
Incorporating waste materials into expansive soils has been the subject of numerous research projects that aim to enhance the expansive soil's engineering properties while addressing their environmental impacts. These efforts have highlighted waste glass as a promising component for soil stabilization [18; 19; 20; 21; 22; 23]. For instance, [PERSON] et al. [24] employed waste soda lime glass powder (WSLGP) to enhance clay soil across various concentrations (3%, 6%, 9%, and 12%), revealing an increase in UCS within the 3% to 6% range but a decline from 6% to 12%. Similarly, [PERSON] et al. [25] explored the impact of waste glass powder (at concentrations of 3%, 5%, 7%, and 9% by dry soil weight), concluding that 7% waste glass powder yielded the optimal results in CBR and UCS tests. The engineering properties of black cotton soil were studied by [26]. They found that adding 4% waste glass powder improved the UCS and CBR values, which indicate improved bearing capacity. Other percentages tested were 2%, 6%, and 8% by dry soil weight. Noteworthy is the contrast with previous studies, which primarily focused on RG (recycled glass) powder at concentrations of up to 10%. In the current investigation, the study offered to incorporate sand-size particles and elevate RG content to 40%, yielding a 30% reduction in the plasticity of mixtures. Furthermore, adding 25% RG led to a remarkable 45% increase in strength and an astonishing 130% surge in bearing capacity. In stark contrast, a mere 6% glass powder yielded a striking 100% strength enhancement and an impressive 200% boost in bearing capacity [27]. In addition, [PERSON] et al. [28] utilized glass powder for expansive clay soil, varying the concentrations (6%, 12%, 18%, 27%, and 36% of dry soil weight) and achieving a notable enhancement in UCS of up to 27% waste glass powder incorporation, subsequently reduced at 36%. This study aims to explore additional applications of glass powder in geotechnical engineering. However, combining glass powder and sintered gypsum to enhance clay soil has barely been attempted. Moreover, the tempered glass from PV panels remains scarcely utilized within geotechnical engineering practices. An innovative aspect of this investigation involves the utilization of EoL PV panels in geotechnical applications. This approach presents a novel idea that could address the disposal issue of EoL PV panels and their sustainable management.
# A Lightweight Perceptron-Based Intrusion Detection System for Fog Computing
### The Work Contribution  
According to Cisco fog computing white paper [1], a fog node can be hosted in a large set of devices, including industrial controllers, switches, routers, embedded servers, and video surveillance cameras. Many of those devices may not have sufficient computational power to perform, for example, data analysis, cryptographic protocols, and intrusion detection algorithms efficiently and provide real time responses and feedbacks. Hence, there is a need for lightweight algorithms and protocols which do not require the use of high-end computing resources. A game-theoretical approach for light-weight IDS was implemented in [5] by combining anomaly and signature detection to achieve high detection and low false positive rates. [PERSON] et al. [6] proposed a lightweight IDS named Sample Selected Extreme Learning Machine (SS-ELM) to overcome the space limitation of fog nodes. The KDD Cup 99 dataset was used and SS-ELM was shown to outperform the classical backpropagation algorithm in terms of detection accuracy and training time. [PERSON] et al. [7] introduced a distributed and lightweight IDS based on an Artificial Immune System (AIS) with detection accuracy reaching 98%. One major drawback of the work in lightweight IDS is using old datasets. On the other hand, IDS models that used modern datasets were designed for cloud platforms. In this study, we develop a lightweight IDS using a contemporary dataset. The key contributions of this work are:  
* Using a modern dataset, ADFA-LD and ADFA-WD, instead of the out-dated KDD Cup 99 dataset.
* Implementing algorithms with low computational complexity for feature extraction and selection.
* Using a single hidden layer MLP for practical detection time.
* Achieving a good detection rate relative to models used on the cloud.
* Testing the performance on a fog node (Raspberry Pi 3).  
The paper is organized as follows: the next section discusses the related work of IDS in fog computing. Section 3 is an overview of the methodology and the technical background of this work. In Section 4, we discuss the requirements of the numerical experiment in terms of software and hardware used. Implementation of the model and evaluation of the results is presented in Section 5. Section 6 is the conclusion and future work is presented in Section 7.
## 4 Experiments and Results
### Experimental Results of Robot Error Prediction and Accuracy Compensation  
In the experiments for robot error prediction and joint angle compensation, 80 groups of joint angles are randomly selected from the determined task workspace to form the test point set, and the real robot errors at these test points are measured. Among the test points, there is a significant change in the orientation of the robot end-effector. The proposed positioning error prediction method is used to estimate the positioning errors at the test points, and the distance between the estimated positioning errors and the true errors represents the accuracy of the error prediction model, as shown in Method 1 in Figure 13. Based on the proposed positioning error prediction model, after calibrating with 160 uniformly sampled data points and optimizing the joint weights using the particle filter method, the maximum error of the prediction model is less than 0.172 mm, and the average prediction error across the 80 test points is 0.089 mm. To compare the practical performance of different robot error prediction methods, other error prediction methods are reproduced, as shown in Figure 13. Method 2 utilizes an error prediction approach based on the MLP model [14] with the same sampling point set in Cartesian space, achieving a maximum prediction error of 0.359 mm and an average prediction error of 0.212 mm; Method 3 employs the spatial grid IDW algorithm for error prediction in Cartesian space [32] with the same sampling point set, resulting in a maximum error of 0.942 mm and an average error of 0.502 mm for the test point set. Results of the comparative experiment on error prediction methods are shown in Table 3.  
Furthermore, the 80 test points are compensated based on the proposed joint angle compensation method, obtaining the corresponding compensated joint angles. The robot is then moved to the compensated joint angles, and the positions of the compensated TCPs are measured. The distance between the compensated TCP positions and the target TCP positions is calculated, which represents the joint angle compensation accuracy. According  
Figure 13: Practical accuracy comparison of robot error prediction models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Error Prediction Method** & \
\begin{tabular}{c} **Average Estimated Error** \\ **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Estimated Error** \\ **(mm)** \\ \end{tabular} \\ \hline Method 1 & The proposed method & 0.089 & 0.172 \\ \hline Method 2 & MLP model in Cartesian space [14] & 0212 & 0.359 \\ \hline Method 3 & The spatial grid IDW algorithm [32] & 0.502 & 0.942 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the comparative experiment on error prediction methods.  
to the experimental verification, the robot positioning error is reduced from 4.96 mm to 0.28 mm after the compensation of joint angles, as shown in Figure 14.
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 5 Failure Analysis
### Single Point of Failure  
In contrast to CMFs that originate in the protected logic, SPOFs are caused by the logic outside the SoR. One of the most common ways to deal with SPOF is to introduce an additional protection layer consisting of ECC, hardware or software protocol checkers, watchdogs, etc. In contrast to the classic DCLS approach, VDCLS reduces the risk of SPOF by including instruction and data cache controllers to the SoR. More modules inside the SoR leave less unprotected logic that is the source of SPOF.
## 4 Numerical Experiments, Results and Discussions
### Discussions of the Role of 2-opt Algorithm  
From Table 2, Figures 5 and 6, it can be seen that, compared with the DICA1, the convergence rate of the DICA2 is slower, and the results it obtained are also worse. In the earlier stage of iterations, the convergence rate of the DICA2 is acceptable, but in the later stage of iterations, it stagnated at a solution which is very poor. The cause of this phenomenon is that in the earlier stage of the iterations, the individuals in the population are diverse and generally bad, it is very easy to find a solution which is better than the current best solution in the assimilation process, in the revolution process and in the competition process; but with the increase of the number of iterations, all the individuals in the population become more and more similar with the imperialist, the diversity of population decline, relying on the revolution process which using randomly generated countries to obtain a solution which is better than the current best solution is very difficult, so the DICA2 very easily stagnates at a very poor solution.  
Figure 6: Trend lines of the two algorithms on fl1400.  
In DICA1, the revolution process is achieved by improved some randomly selected colonies by the 2-opt algorithm. Due to its strong local search ability, the 2-opt algorithm can greatly improve the quality of a colony, using this mechanism can easily find a solution which is greatly better than the current best solution. Meanwhile, the mechanism of the DICA1 can quickly replace the new find best solution to the position of the imperialist, and then to guide the further evolution of the entire population. So its convergence rate is very fast and the solutions it obtained are very good.  
The revised assimilation makes it possible that utilizing the original ICA to solve TSP, and the revised revolution combined with 2-opt algorithm ensures the algorithm to find a superior solution quickly.  
Furthermore, it can be seen from the last column \"Ave.time\", when the scale of TSP is small, using 2-opt algorithm would not significantly increase the time consumption. When the scale of TSP is large, the time consumption increases obviously. The main reason is that the 2-opt algorithm costs more time when applied to solve large-scale TSP.
## 1 Introduction
### Introducing the main requirement of Healthcare 4.0 systems and IoMT.  
Understanding the main requirements of Healthcare 4.0 systems empowers healthcare practitioners to adapt to the latest technological advancements in their practice. They can integrate data analytics and AI tools to make evidence-based decisions, personalize treatment plans, and improve patient outcomes. Awareness of data security and privacy requirements helps practitioners implement measures to protect medical data.  
The main requirements of Healthcare 4.0 systems provide researchers with clear directions. They can focus on developing innovative technologies or solutions that address the specific needs of the healthcare industry in this paradigm. The introduction of IoMT opens up numerous research opportunities. Researchers can explore the potential of new medical devices, algorithms, and applications in remote monitoring, diagnostics, and patient care.