esa-sceva/satcom-chunk-collection huggingface dataset:
# Tunable RF Filters Based on Liquid Crystal for Space Applications
## 3 Qualification Tests and Results  
The test campaign described in this section is a qualification test campaign that was performed on the filter. The kind of test and the test sequence mimic the environments to which the filter is exposed during satellite launch and in-orbit service. Since it is a qualification test campaign, the test level is exaggerated compared to real application, hence proofing design margins. The test criterion is the RF functionality of the filter.  
Previous radiation tests on a different liquid crystal mixture indicate that no damage is induced by space radiation [9]. The container material Rexolite is known to be insensitive to radiation [7].
# Coordinated Optimization of Feeder Flex-Route Transit Scheduling for Urban Rail Systems
## 2 Literature Review
Currently, bus operation planning in most cities still relies on manual scheduling [19]. Therefore, an intelligent bus operation planning method must be developed, considering operational demand constraints. This approachintegrates vehicle scheduling design with timetable optimization to determine the optimal departure intervals, departure frequency, and fleet size for flex-route transit, culminating in a comprehensive operational plan. This paper aims to address these gaps by introducing an integrated optimization approach that aligns flex-route transit scheduling with urban rail timetables, leveraging demand-driven adjustments and dynamic scheduling mechanisms to improve transit network performance. The key elements of this approach include:  
1. Joint Optimization of Path and Timetable--Unlike prior studies that optimize these components separately, this research integrates them to enhance coordination between flex-route transit and urban rail. 2. Passenger Demand and Transfer Behavior Analysis--The proposed model incorporates real-time demand data and passenger transfer patterns to enhance service reliability and efficiency. 3. A Dynamic Scheduling Framework--By developing a mixed-integer nonlinear programming (MINLP) model, this study introduces a more flexible and adaptive scheduling mechanism, allowing for real-time adjustments to improve system resilience. 4. Cost-Efficiency and Service Quality Trade-Off--The model considers both operator costs and passenger-centric factors (waiting times, transfer failures, travel delays) to find an optimal balance.
## Abbreviations  
The following abbreviations are used in this manuscript:  
AI Artificial intelligence  
AIoT Artificial Intelligence of Things  
AIIoT Artificial Intelligence Industry Internet of Things  
ANN Artificial Neural Network  
AP Average Precision  
CAE Computer-Aided Engineering  
CoAP Constrained Application Protocol  
DL Deep Learning  
DNN Deep Neural Network  
FN False Negative  
FP False Positive  
GA Genetic Algorithms  
HD High-Definition  
HPDC High-Pressure Die Casting  
HTTP Hypertext Transfer Protocol  
IDE Integrated Development Environment  
IIoT Industrial Internet of Things  
IoT Internet of Things  
ML Machine Learning  
MQMT Message Queuing Telemetry Transport  
NIA Nonconformity Indicator Algorithm  
NN Neural Network  
PSO Particle Swarm Optimization  
RQ Research Question  
SAS Shared Access Signature  
SDN Software-Defined Networking  
TN True Negative  
TP True Positive
###### Abstract  
This paper introduces Marker-Assisted Region Scanning for Simultaneous Localization and Mapping (MARS-SLAM), a novel approach to optimizing SLAM in unknown environments. Designed to enhance autonomous exploration in extreme conditions, MARS-SLAM ensures efficient navigation while providing a systematic method for verifying mapping completion. The approach leverages virtual markers to track unexplored regions, guiding the robot through an organized and comprehensive exploration process. Markers are placed at the LiDAR sensor's range limit in free areas, maintaining a dynamic list of regions yet to be visited. Mapping is considered complete when no markers remain, signifying full coverage of the environment. Target marker selection is based on age (creation order) and distance (path length from the robot). The method was validated in three virtual environments of varying complexity, demonstrating superior performance compared to alternative navigation strategies, including predefined zigzag routes and routes generated by Ant Colony Optimization (ACO). Experimental results show that MARS-SLAM achieves complete and accurate mapping while significantly reducing the number of poses required. Specifically, it achieves a 64.39% reduction in poses compared to ACO and 71.07% compared to zigzag navigation, highlighting its efficiency in complex environments.  
SLAM; robotic navigation; path planning; mapping optimization; 2D mapping Accepted: 19 March 2025  
1
# Study on Gearbox Fault Warning Based on the Improved M-IALO-GRU Model
## 3 Method, Principle, and Improvement
### Improved IALO-GRU Prediction Model  
Based on the SCADA data of the wind turbine, to realize the state of wind motor gearbox prediction using the data with a nonlinear, dimensional, complex, and temporal time span, research shows that GRU can effectively handle such data, but for the super parameter setting of the experiment, the paper puts forward the prediction model of IIALO-GRU. The specific process is shown in Figure 6.  
1. For the optimization objective and algorithm initialization, the optimization objective function is set to initialize the improved IALO population M, in which the number of ants and ant lions is M/2, the optimization target parameter dimension is four, and the number of iterations is set at 50;
2. For initial sample generation and location initialization, Latin hypercubic sampling (LHS) uses the uniform-sampling and random-sampling methods to generate the initial sampling points to ensure the uniform coverage of the search space. Use these sample points to initialize the population locations. During the iteration, the mean square error (MSE) based on the GRU network model was evaluated as a function of fitness;
3. For GRU hyperparameter setting and optimization, set the hyperparameters, such as the hidden-layer cells, GRU unit layers, training batch size, and learning rate of the GRU network. The IALO model combined with the flight algorithm updates the position and speed to further optimize the local solution;
4. For adaptive value adjustment and convergence judgment, as the iteration process progresses, the adaptive value gradually decreases, and the current optimal solution is recorded. When the IALO algorithm reaches the maximum number of iterationsor satisfies the termination condition, the solution is treated as a convergence, finally obtaining the optimal hyperparameter solution of the GRU network.
## 4 Computer Simulations
### Simulation: Root mean square (RMS) error of the zero order fringe peak identification  
After the zero order fringe peak was identified, fine tuning was calculated for resolution enhancement. Phase error \(\Phi_{error,i}\) between computer generated zero order fringe peak \(p_{t}\) (or \(p_{0}\)) and fine-tuned zero order fringe peak \(\hat{p}_{t}\) (or \(\hat{p}_{0}\)) was calculated. Phase error \(\Phi_{error,i}\) was averaged over 30 simulations at a given _SNR_ and this average gives out the root mean square (RMS) error of the zero order fringe peak identification.  
\[\Phi_{RMS}=\sqrt{\sum\limits_{i=1}^{30}\bigl{(}\Phi_{error,i}\bigr{)}^{2}} \tag{26}\]  
Figure 11: Change of RMS error as a function of _SNR_ (10–40 dB).  
Figure 10: Extrapolation of miss rate on BER curve.  
Figure 11 shows the change of RMS error along with the _SNR_ in the range of 10 dB to 40 dB and Figure 12 shows the change of RMS error along with the _SNR_ in the range of 30 dB to 40 dB. It is shown that the minimum _SNR_ required to achieve RMS error less than 10\({}^{-3}\) [fringe] (which is the fine tuning step size) must be greater than 35 dB _SNR_.
## 4 Conclusions  
The results of the research obtained in this study acted as the basis for formulating the following conclusions:  
The corrosion resistance tests centered around the Bego Wirobond(r) C dental alloy containing Co 59.8(8) wt.%, Cr 31.5(4) wt.%, and Mo 8.8(6) wt.% in the environment of artificial saliva, both before (1) (2) and after being modified with NaF (3) (4), Listerine(r) (5), and Meridol(r) (6) mouthwashes, and comparatively in a physiological saline solution (7), revealed the susceptibility of the tested material to pitting corrosion. The results obtained using the anodic polarization curves j = f(E) confirmed the passivation process that takes place on the tested material. The lowest corrosion resistance was obtained in the artificial saliva solution with pH = 7.4 (1). The highest corrosion resistance for the CoCrMo dental alloy after electrochemical tests was obtained in a saliva solution with pH = 5.5 and the addition of NaF (4). The corrosion resistance of the CoCrMo alloy is related to the chemical composition of the environment in which the corrosion tests were carried out. The results obtained were unambiguous, indicating the effects of modifying the artificial saliva environment on the resistance of the CoCrMo dental alloy. It was shown that F ions help to improve corrosion resistance in the artificial saliva environment by supporting the construction of the passive layer.  
The use of electrochemical impedance spectroscopy in the study of pitting corrosion that occurs on the CoCrMo alloy in the tested corrosion environments allowed the impedance of the electrode-solution interface to be characterized by approximating the impedance data using the CPE1 model of the electrical equivalent circuit. On the basis of the obtained impedance spectra, the activation mechanism of the corrosion processes was found in all of the subject's environments.  
The SKP characterization of the CoCrMo alloy after corrosion tests revealed the effect of artificial saliva modification on the electronic properties of the Bego Wirobond(r) C dental alloy.  
Conceptualization, B.L. and K.G.-K.; methodology, B.L., P.O. and J.K.; validation, P.O., J.K. and K.G.-K.; formal analysis, B.L., P.O., J.K. and K.G.-K.; investigation, B.L., P.O., J.K., and K.G.-K.; resources, B.L. and K.G.-K.; data curation, P.O. and J.K.; writing--original draft preparation, B.L., P.O. and J.K.; writing--review and editing, K.G.-K.; visualization, P.O. and J.K.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 2 Experimental Setup and Methods  
### Sample Preparation  
In this study, we employed twelve kinds of aerosols whose properties are listed in Table 1. During experiments, we used two kinds of aerosol generators. The water-soluble samples were generated by a Met One 255 atomizer (Met One Instruments, Inc., Medford, OR, USA). These samples were delivered to a diffusion dryer and a dilution chamber. The powder-based samples were generated by a solid particle aerosol generator (TSI-3400A) and diluted in a dilution chamber. The experimental conditions for aerosol generation were strictly controlled to ensure the stability and consistency of the experiments. The size distributions of experimental samples were monitored synchronously by an aerodynamic particle size (TSI, 3321). As shown in Figure 1, each type of aerosol shows a unique particle-size distribution, so the size distributions can be used as an important identifying feature. However, due to considerable overlaps of the size distributions shown in Figure 1, it is not enough to classify all these aerosol types only by aerosol size. To classify aerosol types unambiguously, more properties, such as refractive index, absorption, or morphology, should be considered and evaluated.  
In the experiments, three polystyrene latex particles with different diameters were selected as standard samples that are uniform spheres and have no optical absorption effect. Na\({}_{2}\)SO\({}_{4}\), NaCl, and NaNO\({}_{3}\) are typical water-soluble salts and can be generated as quasi-spherical aerosols. The complex refractive index of carbon particles has a non-negligible imaginary part; we selected several carbon samples with various morphological features for experiments. Arizona dust is road dust from Arizona in the United States and can be divided into various categories based on different sizes, which has been used as a standard test for dust and particulate matter for decades. Fly ash is a kind of coal combustion product, which is approximately spherical and contains a variety of metal elements.
## 3 Methodology
### P(True)-Based Confidence Evaluation Mechanism  
In this section, we propose a confidence evaluation mechanism that quantifies the reliability of LLMs' prediction results using the P(True) confidence scoring method, and selects the candidate with the highest score as the final prediction, as illustrated in Figure 4. Compared with traditional uncertainty quantification methods such as Monte Carlo Dropout [39], the P(True)-based confidence scoring mechanism proposed in this study provides a lightweight and interpretable alternative. Conventional approaches typically estimate predictive uncertainty by introducing stochasticity during the forward pass or by constructing multiple models; while these methods offer a degree of stability and theoretical robustness, they often incur substantial computational overhead. In contrast, the P(True) method requires neither architectural modifications nor repeated sampling during inference. It directly derives a normalized confidence score from the LLM's semantic judgment of whether a candidate triple is true, formulated as a binary classification task. This approach enables both ease of implementation and high inference efficiency. A comparative evaluation between the P(True) method, MC Dropout, and a hybrid approach combining both is presented in Section 5.6.
# Information and Communication Technologies and Agricultural Production: New Evidence from Africa
## 1 Introduction
Second, our study is more holistic in examining the effect of information and communication technologies on agricultural production. Recent attempts have examined the impact of information and communication technologies on agriculture in terms of the environmental, social, and economic outcomes, particularly relating to changes in farmers' identities, values, and practices [57, 58]. We capture agricultural production to include both crops and livestock, which remain sparing in the extant literature, and also departure from [PERSON] et al. [56], which captured agricultural performance using value added. Finally, we contribute to the estimation rigor using panel autoregressive distributed lag (ARDL) to estimate the short- and long-run effects of information and communication technologies on agricultural production, including the direction of causality with important policy implications for Africa. The objective of our paper, therefore, is to explore the impact of information and communication technologies on agricultural production in Africa. Our study is novel because it considered the impacts of information and communication on both crop and livestock production in Africa using other covariates, such as rural population, per capita income, and credit to the private sector. There is a dearth of empirical evidence on the impact of ICTs on livestock and crop production with an African scope. The rest of this article is organized as follows. Section 2 reviews the relevant literature pertinent to the study, while Section 3 deals with the methodology. Section 4 presentsthe results, including the discussions, Section 5 concludes with policy implications and recommendations, and the last section presents the limitations and future research direction.
## 3 Results
### Cytotoxicity Evaluation  
In order to assess the cytotoxicity of FSE and SE from _S. aromaticum_ L., two assays were used to assess the impact of the tested samples on the viability and proliferative capacity of skin cells in vitro. The first one (Alamar blue) enables the assessment of the possibility of resazurin reduction, while the second one (neutral red) assesses the possibility of the incorporation of the neutral red dye in cell lysosomes. Two types of skin cells were used in the analyses: keratinocytes (HaCaTs) and fibroblasts (HDFs). The results of the tests performed are presented in Figures 7-10.  
Figure 8: The effect of _S. aromaticum_ L. fermented stems extract (FSE) and stems extract (SE) in the concentration range of 0.1–10.0%, on the reduction in resazurin in fibroblasts (HDF) after 24-h exposure. Control cells were fibroblasts untreated with the tested samples for which viability was assumed to be 100%. Data are the means \(\pm\) SD of three independent experiments. each consisting of three replicates per test group. \(\texttt{{}^{***}}\)\(p\)\(<\) 0.0001, \(\texttt{{}^{***}}\)\(p\)\(=\) 0.0006, \(\texttt{{}^{**}}\)\(p\)\(<\) 0.01.  
Figure 10: The effect of _S. aromaticum_ L. fermented stems extract (FSE) and stems extract (SE), in the concentration range of 0.1–10.0%, on the neutral red dye uptake in fibroblasts (HDFs) after 24 h of exposure. Control cells (green) were keratinocytes untreated with the tested samples for which viability was assumed to be 100%. Data are the means \(\pm\) SD of three independent experiments, each consisting of three replicates per test group. \(\texttt{{}^{**}}\)\(p\)\(<\) 0.01, \(\texttt{{}^{**}}\)\(p\)\(=\) 0.0121.  
Figure 9: The effect of _S. aromaticum_ L. fermented stems extract (FSE) and stems extract, in the concentration range of 0.1–10.0%, on the neutral red dye uptake in keratinocytes (HaCaTs) after 24 h of exposure. Control cells (green) were keratinocytes untreated with the tested samples for which viability was assumed to be 100%. Data are the means \(\pm\) SD of three independent experiments, each consisting of three replicates per test group. \(\texttt{{}^{***}}\)\(p\)\(<\) 0.0001, \(\texttt{{}^{***}}\)\(p\)\(=\) 0.0006, \(\texttt{{}^{**}}\)\(p\)\(=\) 0.0015, \(\texttt{{}^{*}}\)\(p\)\(=\) 0.01.  
The cytotoxicity of plant extracts is closely dependent on the part of the plant being extracted and the phytochemicals contained in that part of the plant material [58; 59]. In vitro cytotoxicity studies have shown that both unfermented and fermented extracts of _S. aromaticum_ L. (used in appropriate concentrations) can improve the metabolism and viability of skin cells (Figures 7-10).
## 5 Conclusions and Prospect  
This paper proposes a dehazing algorithm based on joint polarimetric transmittance estimation via multi-scale segmentation and fusion and demonstrates robust dehazing performance across diverse multi-target scenarios. First, by fusing polarization cues and intensity cues, we achieve highly accurate sky region localization, establishing a critical foundation for subsequent processing. Second, haze-occluded regions and haze-free regions are identified through joint intensity-polarization threshold segmentation. Spatially adaptive transmittance estimation is then performed for each region using corresponding algorithms. This framework enables high-quality dehazing while maximizing structural similarity preservation. Despite the algorithm's effectiveness in haze removal and image enhancement, certain limitations remain. Future work will focus on addressing these constraints to further refine performance.  
* This method depends on selecting sky regions to estimate the atmospheric polarization degree (\(p_{A}\)) and atmospheric light intensity at infinity (\(A_{\infty}\)). For sky-free images, we are forced to rely on empirical priors for estimation, which may introduce significant estimation errors in specific scenarios.
* This approach heavily relies on the accuracy of image segmentation and fusion. In certain scenarios, visibly unnatural boundaries may emerge during segmentation and fusion, or inaccurate region classification may occur, leading to distorted boundaries. Future enhancements may involve employing semantic segmentation and salient object detection methodologies to further improve segmentation diversity and accuracy, thereby strengthening the algorithm's robustness.
* There is still room for optimization in determining the boundary values of the target light in this article in order to make the estimation of transmittance more accurate and the dehazing effect better.  
Conceptualization, Z.Z. and Z.W.; methodology, Z.W.; software, Z.W.; validation, Z.Z. and X.C.; formal analysis, Z.W.; investigation, X.C.; resources, X.C.; data curation, Z.W.; writing--original draft preparation, Z.W.; writing--review and editing, Z.Z., X.C. and Z.W.; visualization, Z.W.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 15: Intensity images and dehazing results under different scenarios using various methods: (**a**): Intensity image; (**b**): Histogram equalization; (**c**): Traditional polarization-based dehazing; (**d**): Proposed algorithm.  
Data Availability Statement: The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
## 3 Results  
### Dataset and Evaluation Metrics  
A multi-source dataset of 515 images which was taken from PH2[28], ISBI 2016 and ISBI 2017 [29; 30], has been used for experimentation in this article. The dataset consists of 304 benign and 211 melanoma lesions with annotated ground truths. In the initial stage, pre-processing of the images is performed for the removal of dermoscopic hair and dark corners. The algorithms have been implemented using MATLAB 2016(r). Three metrics viz., Sensitivity (SE), Specificity (SP) and Accuracy (ACC) have been used for the detection of ROI and lesion classification. In addition to this the overlap error (between the ground truth and segmented mask) is also computed for evaluation of lesion segmentation. The null hypothesis testing has been performed using the Wilcoxon Rank Sum statistics which is a non-parametric test. The null hypothesis is stated below:  
**H\({}_{\mathbf{0}}\).**_The extracted features for benign and malignant lesions have equal medians._  
The testing of the null hypothesis against the alternative hypothesis is performed. The alternate hypothesis states that the features extracted do not have equal medians and hence are statistically significant at 5% significance level. Among the 48 \(p\)-values, 34 \(p\)-values satisfied the alternative hypothesis and hence were used for classification. A hold-out set of 25% is used for testing. The classification metrics are computed by repeating the training and test procedures ten times by stratifying the training and test sets.