esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The prediction of adhesive forces in asphalt, modified with Antistripping Agents (ASAs) and polymers, was studied in this research work. The fresh asphalt samples were  
Figure 11: Scatterplot for ensemble of aged samples.  
Figure 10: Scatterplot for ensemble of moisture-damaged samples.  
mixed with polymers and Antistripping Agents to investigate the moisture damage and aging perspectives. The test results obtained from Atomic Force Microscopy (AFM) laboratory testing were used to model ANN to predict the adhesive forces. Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used. The conclusions drawn from the current study are summarized as follows.  
Hierarchical modeling proved to be a convenient approach for predicting asphalt adhesion force, although it was found to be less accurate, as the CC and MAPE values were close to those of MLP models for moisture-damaged and aged samples. This could be attributed to the lower complexity of samples in the moisture-damaged and aged datasets.  
Compared to MLP, RBFNN was found to be more suitable for developing hierarchical models, with a decrease in error of 8% for the test dataset.  
However, MLP was found generally more suitable than RBFNN for the prediction of adhesion force in moisture-damaged and aged asphalt samples. In particular, for the test dataset of aged asphalt samples, the MLP model showed an increase of 0.26 in CC, a decrease in MAPE by 3%, and a decrease in RMSE by 7 nN, as compared to the RBFNN model.  
The application of ensemble learning, using the simple average of both ANN models, was found to be useful in improving prediction accuracy. The ensemble showed an approximate increase in CC value of 0.03, a decrease in MAPE by approximately 1%, and a decrease in RMSE by 8 nN for the test dataset of the hierarchical model. It improved the prediction accuracy of the moisture-damaged and aged samples model by reducing MAPE approximately 3% and RMSE approximately 6 nN in the test dataset.  
On the basis of the findings of this study, it is recommended to use the ensemble hierarchical model to design longer-lasting pavements with modified asphalt.  
Conceptualization, U.G., M.S.A., M.A., M.K.I. and F.I.S.; methodology and software, U.G. and M.S.A.; validation, M.K.I. and F.I.S.; formal analysis, U.G.; resources and data curation, M.A.; writing--original draft preparation, review and editing, M.K.I. and U.G.; supervision, F.I.S.; project administration, M.K.I.; funding acquisition, M.K.I. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Deanship of Scientific Research (Nasher Track) in the King Faisal University, Saudi Arabia, grant number: NA000172. And the APC also funded by the same grant.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON]. [PERSON], upon reasonable request.  
The authors declare no conflict of interest.
## 3 Wireless Communication Technologies
### Long-Range Communication Technologies
#### 3.2.1 WiMAX (IEEE 802.16)  
WiMAX is a technology standard that aims to deliver broadband access over long distances in a variety of ways, ranging from point-to-point links to full mobile cellular type access. This technology is designed to accommodate both fixed and mobile broadband applications. It supports a data rate of up to 75 Mbps for the fixed applications (20 to 30 Mbps per subscriber) and for mobile applications, the data rate stretches to 30 Mbps (3 to 5 Mbps per subscriber) [16]. WiMAX is developed to provide high-quality voice and video streaming while maintaining the desired quality of service (QoS). With reference to UAV, WiMAX is considered the most appropriate technology for UAV-based rescue systems in hostile environments [17]. In [18], the authors provided methodology for network planning in terms of the number and position of UAVs. Simulation results demonstrate the likelihood of calculating the dimensions, i.e., the position and altitude, of each UAV and guarantee a certain QoS using WiMAX.
# Knowledge Sharing in Security-Sensitive Communities
## 2 Literature Review
### Analyzing Text-Based Interactions  
A standard method for analyzing text-based interactions between people converts the exchange of messages into communication graphs [75; 76; 77; 78]. This method contributes to understanding the knowledge-sharing patterns among the participants, for example, during cooperative learning in a group [75]. Another study proposes an integrated approach to evaluating textual complexity, learning strategies, and learners' collaborative contributions. It analyzed texts exchanged between learners and teachers to inform an automatic feedback model aimed at enhancing understanding [76]. Other studies analyze turn-taking sequences in discussions on online social platforms [77; 78]; identifying conflicts helps in mitigating arguments and preventing negative social processes [79].  
The current study uses the common method of analyzing text-based interactions between community members by converting the interactions into communication graphs and analyzing the structure of these graphs without analyzing the content. The next section elaborates on the research rationale.
# Health Care Accessibility Analysis Considering Behavioral Preferences for Hospital Choice
## 2 Method
### Combined 2 SFCA Method
#### 2.2.3 Accessibility Analysis  
First, the supply-to-demand ratio \(R_{j}\) is calculated for each supply point j for all demand points within a certain service area, as shown in Equation (4).  
\[R_{j}=\frac{S_{j}}{\sum_{i\in(d_{ijs}\leq d_{s},d_{ijt}\leq d_{t})}\ G_{ij}P_{i}} \tag{4}\]  
where \(S_{j}\) is the amount of supply (beds) that can be provided by supply point \(j\), and \(P_{i}\) the demand (population) quantity for demand point \(i\).  
Second, the accessibility \(A_{i}\) of each demand point \(i\) is calculated by aggregating the supply-to-demand ratio of all supply points within the service area, as shown in Equation (5).  
\[A_{i}=\sum_{j\in(d_{ij}\leq d_{s},d_{ij}\leq d_{t})}G_{ij}R_{j} \tag{5}\]
###### Abstract  
Scientists and technicians who work in contact with laboratory animals are exposed to complex biological mixtures from animals, bedding and feed. The main objective of this study was to characterize the exposures to endotoxins and animal allergens in a biomedical research institution located in Central Italy by means of air sampling in the breathing zone of the staff during daily work activities. Forty-two inhalable dust samples were collected for endotoxins and allergens analysis. Filter extracts were analyzed using a Kinetic-QCL LAL kit for endotoxins; ELISA assays were performed for Mus m 1, Rat n 1, Can f 1, Fel d 1 and Equ c 4 detection. Laboratory animal attendants (LAAs) showed endotoxin concentrations significantly higher (4.59 ng/m\({}^{3}\)) than researchers (0.57 ng/m\({}^{3}\)), researchers working only in an office (0.56 ng/m\({}^{3}\)) and technicians (0.37 ng/m\({}^{3}\)). Endotoxin concentrations exceeding the recommended occupational exposure limit proposed by the Dutch Expert Committee on Occupational Safety were found in the case of two subjects in the animal attendants category. With regards to rat and mouse allergens, a higher average dose was found for mouse than rat allergens. Also for these bio-contaminants, the LAAs are confirmed as the work category at higher risk of exposure (15.85 ng/m\({}^{3}\)), followed by technicians (10.67 ng/m\({}^{3}\)), researchers (2.73 ng/m\({}^{3}\)) and researchers in an office (0.08 ng/m\({}^{3}\)). Fel d 1 was also detected (average: 0.11 ng/m\({}^{3}\)) highlighting a passive transport between living and occupational settings. Our data could be useful to improve the control and preventive measures, ensuring lower levels of allergens and endotoxins in animal facilities.  
animal facility; allergens; endotoxins; occupational exposure; laboratory animal allergy; work category; personal air monitoring 1
# Multi-Source Data Fusion for Vehicle Maintenance Project Prediction
## 3 Method
### Prediction Analysis of New Maintenance Projects  
In this study, a new maintenance project is defined as one that has not previously been executed on the vehicle. Within the realm of vehicle maintenance, there exists a heightened interest in predicting such previously unencountered projects. By leveraging the underlying assumption that distinct maintenance projects correlate, our proposed model is expected to exhibit enhanced accuracy in predicting new maintenance projects.  
Table 6 displays the experimental results detailing each model's predictive capabilities regarding new maintenance projects. Notably, our proposed model surpasses all baseline models, attesting to its exceptional proficiency in predicting these previously unencountered maintenance projects. These results validate our model's effectiveness in predicting new maintenance projects.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Types** & **Model** & \(\mathbf{w}\)-\(\mathbf{F_{1}}\) & **R@3** & **R@5** & **R@7** \\ \hline \multirow{8}{*}{Feature Level} & MsDFN-B\({}_{1}\) & 34.34 \(\pm\) 0.15 & 40.25 \(\pm\) 0.12 & 47.01 \(\pm\) 0.15 & 52.06 \(\pm\) 0.11 \\  & MsDFN-B\({}_{2}\) & 34.15 \(\pm\) 0.25 & 40.01 \(\pm\) 0.18 & 46.90 \(\pm\) 0.80 & 51.75 \(\pm\) 0.72 \\  & MsDFN-B\({}_{3}\) & 33.89 \(\pm\) 0.33 & 40.13 \(\pm\) 0.31 & 46.88 \(\pm\) 0.14 & 51.72 \(\pm\) 0.45 \\  & MsDFN-B\({}_{4}\) & 33.97 \(\pm\) 0.11 & 40.30 \(\pm\) 0.24 & 47.21 \(\pm\) 0.52 & 52.15 \(\pm\) 0.46 \\  & MsDFN-B\({}_{5}\) & 34.03 \(\pm\) 0.40 & 40.33 \(\pm\) 0.34 & 46.97 \(\pm\) 0.74 & 51.78 \(\pm\) 0.29 \\  & MsDFN-B\({}_{6}\) & 34.09 \(\pm\) 0.36 & 40.17 \(\pm\) 0.33 & 46.86 \(\pm\) 0.71 & 51.77 \(\pm\) 0.55 \\  & MsDFN-B\({}_{7}\) & 34.10 \(\pm\) 0.07 & 40.32 \(\pm\) 0.37 & 47.22 \(\pm\) 0.77 & 52.14 \(\pm\) 0.35 \\  & MsDFN-B\({}_{8}\) & 34.12 \(\pm\) 0.21 & 40.19 \(\pm\) 0.36 & 47.05 \(\pm\) 0.70 & 51.86 \(\pm\) 0.50 \\ \hline \multirow{8}{*}{Module Level} & MsDFN-B & 32.24 \(\pm\) 0.31 & 40.11 \(\pm\) 0.27 & 46.74 \(\pm\) 0.32 & 51.20 \(\pm\) 0.34 \\  & MsDFN-M & 33.82 \(\pm\) 0.18 & 40.31 \(\pm\) 0.25 & 47.19 \(\pm\) 0.42 & 52.06 \(\pm\) 0.54 \\  & MsDFN-Co & 33.77 \(\pm\) 0.29 & 40.35 \(\pm\) 0.30 & 47.11 \(\pm\) 0.78 & 52.03 \(\pm\) 0.67 \\ \hline \multirow{8}{*}{\(\cdots\)} & MsDFN & 34.62 \(\pm\) 0.15 & 40.39 \(\pm\) 0.12 & 47.29 \(\pm\) 0.16 & 52.18 \(\pm\) 0.17 \\ \cline{1-1} \cline{2-5}  & & & & & \\ \end{tabular}
\end{table}
Table 5: Ablation study of vehicle maintenance project prediction.
###### Abstract  
This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry's need for real-time, user-friendly solutions.  
Keywords:autoencoders; anomaly detection; predictive maintenance; digital twin; induction motor; Mahalanobis statistical distance +
Footnote †: journal: Applied Sciences
# Corrosion Behavior of Pre-Rusted Rebars in Cement Mortar Exposed to Harsh Environments
## 3 Results
### Corrosion Current  
The corrosion current values measured over 120 days are shown in Figure 4. The difference in the corrosion current was not significant, regardless of the presence of pre-rust, from day 1 to 13 after the start of the experiment. However, the corrosion current of the group C specimens showed a tendency to increase from day 15. The corrosion current value increased gradually from day 30 for the group B specimens and from day 45 for the group A specimens. The average corrosion current over 120 days was 19% higher in group B and 137% higher in group C than in group A.  
Figure 4: Corrosion current vs. time.  
Figure 3: Polarization curve: (**a**) specimen A, (**b**) specimen B, and (**c**) specimen C.  
The corrosion rate was calculated using the corrosion current value measured on day 120. We found that the corrosion rate of the group C specimens (0.003 mm/year) was 1.6 times that of the group B specimens and 3.8 times that of the group A specimens.
## 2 Materials and Methods
### Characterization of Plasma-Activated Water  
The pH and ORP levels of the activated water were measured with the instrument (Hach 5465010 Sension 156 PH). The accuracy of pH and ORP meters were \(\pm\)0.002 and 0.2 mV, respectively.  
The reactive nitrogen species inside the activated water were characterized with nitrate and nitrite analysis. Measurements of nitrate and nitrite were conducted using an ion chromatography system (ICS, Dionex ICS-1500) with an AS4A-SC column. The standard of calibration in ICS was prepared to know the samples rely on the proper range.  
Figure 1: Schematic of the experimental setup of DBDs over water surface for plasma-activated water. The gases are injected individually via MFC.  
The concentration of hydrogen peroxide inside PAW was determined by potassium permanganate titration. This method utilizes the reduction of potassium permanganate (KMnO\({}_{4}\)) by hydrogen peroxide in sulfuric acid according to the following reaction [21]:  
\[5\text{H}_{2}\text{O}_{2}+2\text{KMnO}_{4}+3\text{H}_{2}\text{SO}_{4}\to 2 \text{MnSO}_{4}+\text{K}_{2}\text{SO}_{4}+8\text{H}_{2}\text{O}+5\text{O}_{2} \tag{1}\]  
Regarding each time point related to the treatment time, the data averaged over three runs. In addition, each time point was taken independently, without interruption, and not consecutively.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 4 Experimental Results and Evaluation
### Results of Comparison Experiments
On average, only 1.76% of \"winter wheat\" pixels were wrongly classified as \"non-winter wheat\", and 0.89% of \"non-winter wheat\" pixels were wrongly classified as \"winter wheat\". Compared with the RefineNet model, this is a reduction of 0.3% and 2.36%, respectively. Compared with the SegNet model, this is a reduction by 2.33% and 2.57%, respectively. In this study, we employed several criteria, including accuracy, precision, recall, and the Kappa coefficient, to evaluate the performance of the proposed model [52]. Table 7 shows data from the evaluation criteria of the three models, with the accuracy of the CEM model being 15.14% and 6.72% above those of SegNet and RefineNet, respectively.
## 2 Visualization of Hyperspectral Images
The accuracy of the hyperspectral sensors is typically measured in spectral resolution, which indicates the bandwidth of the spectrum that is captured. If the scanner picks up a large number of \"images\" with relatively small bandwidth, then it is possible to identify a large number of objects even if they are located in a relatively small area of pixels. The JPL (Jet Propulsion Laboratory-NASA) AVIRIS sensor has a spatial resolution that covers an area of 20 \(\times\)20 meters per pixel. The spectral components are obtained via a 12 bits ADC (Analog-to-Digital Converter). The elements of the spectrum, after the necessary calibration and geometric correction, are represented with a16-bit accuracy. Figure 2: Lunar Lake Scene 01, Band 120. Figure 4: Yellowstone Scene 11, with band 125 assigned to red component, band 70 to green and band 30 to blue. Figure 3: Lunar Lake Scene 03, with band 125 assigned to the red RGB component, band 70 to the green and band 30 to the blue.
## 2 Overview of the Study Area  
Figure 1 shows the position of a landslide above Luhu Village in Shanxi Province, China. The region around the village is a mining area, with the mine in panel A having a depth of 470 m, thickness of 6.5 m, width of 200 m, and length of 1200 m; and the mine in panel B had a depth of 390 m, thickness of 2.5 m, width of 220 m, and length 1162 m. The area investigated had a largely undulating terrain, with steep slopes. The vegetation in this area is mainly shrubs, and the area has a temperate continental climate, with annual rainfall of less than 600 mm, which is mainly concentrated in July, August, and September. The loess layer in this area is very thin and the bedrock is exposed. The strata are North China continental facies, with Carboniferous (C), Permian (P), and Quaternary (Q) outcrops. The total thickness of the coal series strata is 220-260 m.  
The initial landslide occurred in February 2017 and now forms a high-steep slope, with a height of about 100 m, an area of 11,563.7 m\({}^{2}\), and slope of more than 45\({}^{\circ}\). There is a strong potential for mining  
Figure 1: Overview of the study area and distribution of Global Navigation Satellite System (GNSS) monitoring points.  
activities to initiate secondary landslides. There is a road and river around the landslide. The river flows to the southeast and is seasonal, with water only in the rainy season. The valley is about 150 m wide, the valley floor elevation is 825-830 m, the change of gradient is about 15%, the two sides of the valley are mostly wasteland, and some bedrock is exposed. There are a small number of terraces on the west side of the valley. Luhu village is located near the road and the Luhu River, on the northwest side of the slope body, and is close to the high-steep slope.
## 1 Introduction
By employing fast-Fourier transform (FFT) at the receiver (Rx) side, the data signal can be retrieved even if there is strong spectral overlapping among the OFDM subcarriers. Hence, a high spectral efficiency can be achieved. In this work, we put forward and experimentally demonstrate an 8.07 Gbit/s red color LD based VLC system using two kinds of digital domain multiplexing schemes, OFDM and power domain division multiplexing (PowDM). The PowDM technique is inspired by the works [32; 33], where [32] is related to power domain multiplexing for multiple users and [33] is applied for a fiber coherent transmission system. Here, similar to OFDM, the PowDM can also enhance the total system capacity by allowing acceptable signal spectra overlapping among different power division signals to maximize the bandwidth utilization. By employing the successive interference cancellation (SIC) at the Rx side, the data signal can be retrieved. The potential applications of the proposed method can be used in machine-to-machine communication (M2M) or in areas where the employment of RF based wireless communication is forbidden. An experiment to verify and evaluate the proposed work is performed, and the results show that the PowDM signal can achieve a higher capacity than the individual signal. The modulation and demodulation of the OFDM and PowDM are discussed. The optimum power levels of the individual signals in the PowDM signal are also analyzed.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 3 Related Works
### Summary of Works in Handover Methods  
To consolidate the findings from the preceding literature review, this subsection provides a structured assessment of the current handover decision-making approaches categorized into AI-based, fuzzy logic-based, and hybrid models. Table 1 presents a comparative summary of prominent studies, highlighting their input features, methodological frameworks, key benefits, drawbacks, and classification. This tabular representation supports quick identification of commonalities and gaps, and facilitates the identification of trends across fuzzy, AI-driven, and hybrid approaches. To provide a clearer understanding of the current landscape in handover decision-making for ultra-dense 6G networks, the surveyed literature has been categorized into three main approaches: AI-based, fuzzy logic-based, and hybrid frameworks. This classification
###### Abstract  
Radiation has become an essential part in medicine and researchers are constitutionally investigating radiation shielding materials that are suitable for different medical applications. Glass, due to its properties, has been considered an excellent radiation shield for such applications. One of the most common glasses used as a radiation shield is the ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) anti-radiation glass. Heavy metal oxides have many desirable properties such as high density, transparency to visible light, stability in air and water, high interaction cross section, high infrared transparency, and good absorption of radiation, which make them desirable to be used as modifiers with anti-radiation glass. Research has been focusing on environmentally friendly shielding material which leads to non-lead modifiers such as Na\({}_{2}\)O, Al\({}_{2}\)O\({}_{3}\), MgO, TiO\({}_{2}\), SrO, Sb\({}_{2}\)O\({}_{3}\), and BaO, which have become more desired than PbO. So far, ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\)'s photon shielding properties have been studied experimentally with the addition of BaO at certain energies only. In this work, different heavy metal oxides are added as modifiers to ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) glass in order to investigate theoretically their effects on the shielding properties of the glass at a wide range of photon and neutron energies. Simulation is cost- and time-effective when it comes to investigating different compositions of glass and different modifiers with different weight percentages at any energy range for any type of radiation. Simulation could be considered the first step in order to identify the best mixture with the best weight fractions prior to any experimental investigations of other desired properties based on the needed application. In this work, the photon- and neutron-shielding capabilities of the ZnO-Bi\({}_{2}\)O\({}_{3}\)-B\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) anti-radiation glass is investigated with different weight fractions of heavy metal oxides at wide photon and neutron energy ranges. Geant4, which is a Monte Carlo-based powerful toolkit, is used to find the mass attenuation coefficients (\(\mu\)m) of photons, as well as the effective removal cross sections (\(\Sigma\)R) of neutrons, of all the investigated samples in the studied energy range.  
Geant4; anti-radiation glass; photons; neutrons; attenuation coefficients; effective removal cross sections +
Footnote †: journal: Applied Physics Letters