esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Airport Pavement Integrated Risk Management (APIRM) Model  
The Airport Pavement Integrated Risk Management (APIRM) model offers a holistic approach to evaluating and managing the risks associated with airport pavements within the framework of a safety management system (SMS). This innovative model is designed to enhance decision-making for both safety and maintenance priorities by integrating various assessment methods employed in an airport pavement management system (APMS).  
The key components of the APIRM model are as follows:  
1. _Integration of APMS and SMS:_ APIRM utilizes data from the APMS, which monitors and measures both the functional and structural conditions of pavements. This information helps identify potential hazards associated with airport pavements, including runways, taxiways, aprons, and aircraft parking areas. By incorporating these data into the SMS, the model enables a comprehensive evaluation of pavement-related risks and safety hazards.
2. _Multidisciplinary Decision Support:_ The model promotes collaboration among SMS experts, pavement managers, and engineers, facilitating objective decision-making regarding hazard identification, risk assessment, and prioritization of maintenance and repair actions. This multidisciplinary approach ensures that all aspects of pavement safety and performance are considered, leading to more effective management strategies.
3. _Risk Evaluation Framework:_ APIRM establishes a systematic framework for evaluating risks associated with identified hazards. This involves analyzing the potential impact of these risks on airport operations and safety. The evaluation process includes calculating risk probabilities, potential consequences, and overall risk levels, allowing for informed prioritization of interventions.
4. _Visual Representation:_ A flow diagram of the APIRM model (Figure 5) illustrates the various components and processes involved in the integrated risk management  
Figure 4: Safety risk management process [43].  
approach. This diagram serves as a visual aid for understanding how the model functions and the relationships between different elements within the system.
* 5. _Enhanced Safety and Maintenance Planning_: By aligning pavement management with safety protocols, APIRM enables airports to proactively address pavement-related hazards and reduce the likelihood of incidents that could compromise safety. The model also aids in the planning and allocation of resources for pavement maintenance and repair, ensuring that critical areas are prioritized based on risk assessments.  
Figure 5: APIRM components and processes.
## 2 Co-Administration of Two Different Antimicrobial Polymers
### Improving Antimicrobial Activity of Chitosan by Grafting Biocidal Polymers
recently disclosed an additional peptide-CS based system: [37] Self-assembled cationic nanoparticles derived from CS-graft-oliglysine (CSM5-K5) were combined with CS. This formula showed a greater perturbation in MRSA membranes as compared to the results obtained from the materials tested separately. A synergy has also been confirmed in CS-guanidine complexes prepared by reacting CS and polyhexamethylene guanidine hydrochloride (CS-PHGH) [38], and in this case, the MIC value of a CS-PHGH complex against _E. coli_ was 15.6 \(\mu\)g mL\({}^{-1}\), whereas the MIC values corresponding to pristine CS and PHGH were 1280.0 and 16.3 \(\mu\)g mL\({}^{-1}\), respectively. In another study, CS was blended with different nanocrystalline celluloses, which were previously modified with quaternary ammonium salts, such as epoxypropyltrimethylammonium chloride, \(N\),\(N\)-dimethyl-\(N\)-dodecyl-\(N\)-(1,2-epoxypropyl) ammonium chloride (DMDEPAC) and \(N\),\(N\)-dimethyl-\(N\)-octadecyl-\(N\)-(1,2-epoxypropyl) ammonium chloride (DMOEPAC). The antibacterial tests of these materials revealed good biocidal efficacy against _S. aureus_ and _E. coli_ O157:H7 and, notably, the systems modified with DMDEPAC demonstrated a 6-log reduction of the concentration of both strains within 5 min [39]. Apart from CS, the combinations of various biocidal functionalities utilizing different polymeric systems have been described. For instance, [PERSON] et al. modified polyacrylamide (PAM) with both quaternary ammonium salts (QAS) and quaternary phosphonium salts (QPS) moieties [40]. In general, QPS polymeric compounds display higher antimicrobial activity compared to the corresponding polymeric QAS [41], probably because phosphorus atoms favor the adsorption process, that is, positively charged moieties on negatively charged bacterial membranes. In this latter contribution, a triblock copolymer was prepared from acrylamide (AM) monomer and (4-penten-1-yl)-triphenylphosphonium bromide (PTPB) and diallyl dimethyl ammonium chloride (DADMAC) as cationic comonomers. The antimicrobial activity of this material with dual active groups was investigated against _E. coli_ bacterium and non-enveloped adenovirus (ADV) demonstrating that the incorporation of a 55% content of PTPB in the copolymer depresses the MIC value to 75 \(\mu\)g mL\({}^{-1}\). Therefore, this provides a high viricidal efficiency and suggests a dual-functional antibacterial/antiviral activity.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Orbital Angular Momentum Antennas
#### 3.4.7 OAM Evaluation  
OAM-waves are a very unique and state-of-the-art tool that could be used in 6G and beyond wireless communication systems. Their integration with the other advanced technologies is anticipated to be a game changer, providing stable, high-throughput communications.
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
###### Abstract  
An optical fiber sensing system integrating a fiber Bragg grating (FBG) sensor, a long-period fiber grating (LPFG) optical filter and a photodetector is presented to monitor the dynamic response of a structure subjected to base excitation and impact loading. The FBG sensor is attached to a test specimen and connected to an LPFG filter. As the light reflected from the FBG sensor is transmitted through the long-period fiber grating filter, the intensity of the light is modulated by the wavelength, which is affected by the strain of the FBG. By measuring the intensity of the light using a photodetector, the wavelength reflected from the FBG sensor can be demodulated, thus leading to the determination of the strain in the structure. To demonstrate its effectiveness, the proposed sensing system was employed to measure the dynamic strain of a beam subjected to mechanical testing. The mechanical tests comprised three load scenarios: base excitation by a shaker at resonant frequency, impact loading by a hammer and shock test on a drop table. To monitor the dynamic strain during the test and validate the accuracy of the measurement of the FBG sensor, strain gauge was used as reference. Experimental results show good correlation between the measurements of FBG sensor and strain gauge. The present work provides a fast response and easy-to-implement optical fiber sensing system for structural health monitoring based on real-time dynamic strain measurements.  
fiber Bragg grating; long period fiber grating; dynamic response; impact Article
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Optimization Criteria
Consequently, the crosswind model is described as  
\[c_{w1}=\sum_{n}^{N_{\mathcal{D}}-1}\left(\mathcal{N}\left(\Delta_{n}^{\chi}, \frac{\pi}{2},\frac{\pi}{6}\right)+\mathcal{N}\left(\Delta_{n}^{\chi},\frac{ \pi}{2},\frac{\pi}{6}\right)\right), \tag{17}\]  
where \(\sigma=\frac{\pi}{6}\) was selected empirically, and \(N_{\mathcal{D}}\) represents the number of waypoints spaced along the Dubins path \(\mathcal{D}\). The cost related to crosswind effects remains constant with respect to UAV behavior. Headwind is modeled using a normal distribution \(\mathcal{N}(\cdot)\) centered at the nose of the UAV, corresponding to an angle of \(0^{\circ}\). The headwind model is defined as  
\[c_{w2}=\sum_{n}^{N_{\mathcal{D}}-1}\left(\mathcal{N}\left(\Delta_{n}^{\chi},0, \frac{\pi}{6}\right)\left(\frac{\pi}{4}-\text{atan}2\left(\Delta_{n}^{v}, \Delta_{n}^{h}\right)\right)\right), \tag{18}\]  
where \(\Delta_{n}^{v}\) and \(\Delta_{n}^{h}\) denote the vertical and horizontal distances, respectively, between waypoints \(\mathbf{w}n^{\mathcal{D}}\) and \(\mathbf{w}n+1^{\mathcal{D}}\). Tailwind, the last component, is modeled as  
(19)  
Ultimately, wind influence is modeled by heuristic rules defined as  
\[c_{w}=s_{fw}(c_{w1}+c_{w2}+c_{w3}), \tag{20}\]  
where \(s_{fw}\) denotes a scaling factor. A similar methodology, involving the summation of multiple Gaussian distributions, was utilized by [PERSON] and [PERSON] in their ACO\({}_{\mathbb{R}}\) algorithm. However, they applied a weighted sum approach, referred to as a Gaussian kernel [36].
###### Abstract  
In recent years, various coatings based on fluorinated materials, used in a commercial application, have been created through many preparation routes. However, the techniques utilized to realize these coatings required either expensive and complex equipment, imply multiple manufacturing steps, or are time- or cost-consuming. In this paper, the major target was to develop fluorinated hybrid coatings presenting sustainable hydrophobicity and good transparency simultaneously. The sol-gel method was proposed to obtain these fluorinated hybrid coatings because it does not require expensive equipment, or the existence of stabilizing agents that reduce the storage period, it consumes less energy, and it is easy to implement. The influence of perfluoroctanoic acid, utilized in the sol-gel processing of hybrid silica materials, on the structural, morphological, and optical properties of coatings deposited on glass substrates, was evaluated. Different silane precursors (tetraethyl orthosilicate (TEOS), triethoxymethylsilane (MTES), and trimethoxyshexadecylsilane (HDTMES)) were utilized to synthesize hybrid silica materials. The properties of the obtained materials were characterized by FTIR, UV-Vis, TEM, TGA, AFM, Ellipsometry, and Contact Angle analyses. FTIR spectroscopy shows the formation of a silica network tailored with organofunctional and fluorolakyl groups. The fluorinated silica coatings presented smooth surfaces and good transparency, with a transmittance of \(\sim\)90% in the visible range. It was found that the fluorinated silica materials improved the coating's hydrophobicity (\(\sim\)110 deg in contact angle with water). These fluorinated silica materials can create multifunctional structures with antireflective and hydrophobic coatings for possible optical devices.  
sol-gel process; fluorinated materials; antireflective coatings; wettability Vol. 2023, 13, 1669. [[https://doi.org/10.3390/app13031669](https://doi.org/10.3390/app13031669)]([https://doi.org/10.3390/app13031669](https://doi.org/10.3390/app13031669))
# Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
## 2 Wind Turbine Simulator and Fault Model
The overall model of the wind turbine process is represented as a nonlinear continuous-time function \(\mathbf{f}_{wt}\) that describes the evolution of the turbine state vector \(\mathbf{x}_{wt}\) excited by the input vector \(\mathbf{u}\):  
\[\left\{ \begin{array}{lcl}\dot{\mathbf{x}}_{wt}(t)&=&\mathbf{f}_{wt}\left( \mathbf{x}_{wt},\mathbf{u}(t)\right)\\ \mathbf{y}(t)&=&\mathbf{x}_{wt}(t)\end{array} \right. \tag{1}\]  
where, in this case, the state of the system is considered equal to the outputs of the wind turbine system, i.e., the rotor speed, the generator speed, and the generated power:  
\[\mathbf{x}_{wt}(t)=\mathbf{y}(t)=\left[\omega_{g,m1},\,\omega_{g,m2},\,\omega_ {r,m1},\,\omega_{r,m2},\,P_{g,m}\right]\]  
On the other hand, the input vector,  
\[\mathbf{u}(t)=\left[\beta_{1,m1},\,\beta_{1,m2},\,\beta_{2,m1},\,\beta_{2,m2}, \,\beta_{3,m1},\,\beta_{3,m2},\,\tau_{g,m}\right]\]  
consists of the measurements of the three pitch angles from the three redundant sensors, as well as the measured torque. These signals are sampled with a sample time \(T\) in order to acquire a total of \(N\) measurements \(\mathbf{u}(k)\), \(\mathbf{y}(k)\) with \(k=1,\,\ldots,\,N\), in order to implement the data-driven fault diagnosis solutions proposed in this paper. It is worth noting that, as highlighted in Section 3, the effect of the faults considered in Table 1 is assumed to be generated by equivalent signals added to the input and output measurements. This approach was formerly proposed by the authors of [17]. Moreover, this assumption is also known as Errors-In-Variables (EIV) modeling, which is exploited in the dynamic system identification framework [18].
## 3 Results
### Time-Variant Response of Vegetation Dynamics to Climate Changes
Given that a climate factor may exert an opposite impact on the vegetation sensitivity over the investigation period, which could not be revealed in Figures 7 and 8, we further investigated the signs of the regression coefficients classified by the three land-cover types, as represented in Figure 9.  
In general, the boxplots in Figure 9a show that solar radiation exerted a negative impact on vegetation in BSVs and grasslands, while it exerted a positive impact on vegetation in forests. Figure 9b illustrates that temperature generally had a negative impact on all three land-cover types. By contrast, water availability displayed a positive impact on BSVs and grasslands (Figure 9c). Nevertheless, no obvious sign of the regression coefficients could be determined for forests. Taking all four vegetation proxies into consideration, we further summarized the trend of vegetation sensitivity illustrated in Figure 8 and corresponding signs of the regression coefficients displayed in Figure 9. The conclusions are shown in Table 1.  
Figure 10: RGB composites of the fraction values that describe the appearance frequency of the dominant climate factors over the investigation period (temperature, red; water availability, blue; and solar radiation, green). (**–d**) present the results derived from the LAI, EVI, NDVI, and SIF products, respectively. The dominant climate factor denotes the climate factor with the maximum impact on vegetation sensitivity. The dominant climate factor that exerted the largest impact on vegetation sensitivity was revealed by comparing the absolute values of the regression coefficients for the three climatic driving factors for each year. For a given pixel in a given year, the climate factor with the maximum regression coefficient was regarded as the dominant factor. The fraction values describing the appearance frequency for each climate factor over the whole investigation period were calculated and were RGB-composited, as shown in Figure 10.  
A distinct spatial pattern was observed for the dominant climatic factor with respect to regulation of the time-variant vegetation sensitivity shown in Figure 10, which was strongly consistent with the spatial pattern of the time-invariant vegetation sensitivity shown in Figure 6. LAI exhibited a relatively different spatial pattern, as solar radiation was found to exert the maximum impact on vegetation sensitivity in the southeastern QTP. Conversely, EVI, NDVI, and SIF jointly revealed that temperature and water availability controlled the vegetation sensitivity in the southeastern QTP. In general, temperature was the dominant factor for pixels located in the central to eastern QTP. Water availability was the dominant factor for pixels located in the northeastern QTP and forest areas in the southern QTP, which findings were different in comparison with the time-invariant vegetation sensitivity shown in Figure 6. The spatial pattern of solar radiation was relatively scattered, and the corresponding pixels were mostly distributed around the spatial distribution of temperature factors.
## 2 Model Identification of 3-SRR/SRU
### Existing Control Strategy  
In each limb, let \(l_{1}=\left\|\overrightarrow{A_{i}}C_{i}\right\|\) and \(l_{2}=\left\|\overrightarrow{C_{i}}B_{i}\right\|\), (\(i=1,\cdots,4\)). [PERSON] et al. [14] designed a kinematic control strategy that consists of calculating the desired angles \(\theta_{id}=\widehat{B_{i}C_{i}A_{i}}\), (\(i=1,\ 2,\ 4\)) at the actuated joints, for which the moving plate returns to its initial position with every change in the ship motion. Using the cosine law, angles \(\theta_{id}\) are formulated as follows:  
\[\theta_{id}=cos^{-1}\Bigg{[}\frac{{l_{1}}^{2}+{l_{2}}^{2}-\left[(x_{Ai}-x_{Bi} )^{2}+(y_{Ai}-y_{B_{i}})^{2}+(z_{Ai}-z_{Bi})^{2}\right]}{2l_{1}l_{2}}\Bigg{]}, \quad(i=1,2,4) \tag{3}\]
## 3 Feature Extraction
### Time Domain Feature Extraction
#### 3.2.3 slope-Sign Change (SSC)  
Another feature that can offer insight into frequency content is the count of instances where the slope of the waveform changes its sign. Once more, an appropriate threshold must be selected to mitigate noise-induced fluctuations in slope.  
\[\begin{split} SSC=&\frac{1}{N}\sum_{i=2}^{N-1}f((x_ {i+1}-x_{i})(x_{i}-x_{i-1}))\\ \\ f(x)=&\begin{cases}1,&ifx>Th\\ 0,&otherwise\end{cases}\end{split} \tag{5}\]  
Considering three consecutive samples, \(x_{i-1}\), \(x_{i}\), and \(x_{i+1}\), the slope sign change count, denoted as SC, is incremented if \(x>Th\), \(f(x)=1\). This process is applied to three sequential segments using the threshold function Th to mitigate background noise in the EMG signal. This method captures frequency information from the time domain.
# Ontology Attention Layer for Medical Named Entity Recognition
###### Abstract  
Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the \"ontology attention layer\", that enhances the NER performance of a language model for clinical text by utilizing an ontology consisting of conceptual classes related to the target entity set. The proposed layer computes the relevance between each input token and the classes in the ontology and then fuses the encoded token vectors and the class vectors to enhance the token vectors by explicit superior knowledge. In our experiments, we apply the proposed layer to various language models for an NER task based on a Chinese clinical dataset to evaluate the performance of the layer. We also investigate the influence of the granularity of the classes utilized in the ontology attention layer. The experimental results show that the proposed ontology attention layer improved F1 scores by 0.4% to 0.5%. The results suggest that the proposed method is an effective approach to improving the NER performance of existing language models for clinical datasets.  
named entity recognition; ontology; clinical text mining; attention mechanism +
Footnote †: journal: Medical Image Analysis
tags for NER based on the estimated probabilities. Experimental results show performance improvements across three baseline models.  
The main contribution of this paper is the proposal of an approach that better captures specific domain knowledge to recognize medical entities by leveraging knowledge from the medical ontology. We also discuss the effects of different ontology design granularity and abstraction-type properties on NER tasks. Our proposed method is simple, effective, and especially useful in practice.
## 4 Results and Discussion
### Analysis of Factors Affecting the Interaction Capability Between Asphalt and Mineral Filler
#### 4.4.2 Influence of Temperature on the Interaction Capability Between Asphalt and Mineral Fillers  
The effect of temperature on the interaction capability between asphalt and mineral fillers was examined by analyzing the variation in adsorbed asphalt film thickness for three types of asphalt mastics, as illustrated in Figure 12.  
Figure 12 demonstrates that, within the investigated range of mineral filler volume fractions, the adsorbed asphalt film thickness of limestone asphalt mastic increases with rising temperature. This trend can be attributed to two factors. The primary reason is that when the temperature surpasses the softening point of asphalt (48 \({}^{\circ}\)C), asphalt transitions from a semi-solid to a viscous flow state, improving its contact and wetting ability with the mineral filler [42]. Additionally, elevated temperatures enhance the adhesion between asphalt and the alkaline mineral components of the filler. Since limestone contains 51.48%  
Figure 11: Effect of SiO\({}_{2}\) content on the thickness of the adsorbed asphalt film.  
Figure 12: Variation in adsorbed asphalt film thickness with temperature for different asphalt mastics.  
CaO, the adhesion between asphalt and CaO strengthens with increasing temperature, thereby improving the interaction between asphalt and the limestone mineral filler.  
For fly ash asphalt mastic, the adsorbed asphalt film thickness exhibits minimal variation with temperature. This is primarily due to the low CaO content in fly ash, which is only 2.8%. As a result, the adhesion between asphalt and CaO remains nearly unchanged with rising temperature. Fly ash particles possess a dense CaO-SiO\({}_{2}\)-Al\({}_{2}\)O\({}_{3}\) system, and their glassy phase forms a smooth, compact, and robust Si-O-Si and Si-O-Al network structure [43]. Therefore, even when the temperature exceeds the asphalt softening point and transitions it into a viscous flow state, the effective wetting of fly ash particles remains limited. Consequently, temperature variations exert little influence on the interaction between asphalt and fly ash.  
In coal gangue asphalt mastic, the adsorbed asphalt film thickness decreases as temperature rises. This reduction is primarily due to the high SiO\({}_{2}\) content and large specific surface area of coal gangue. With increasing temperature, asphalt more effectively contacts coal gangue; however, the acidic SiO\({}_{2}\) inhibits its interaction with the mineral filler. Even beyond the asphalt softening point, the inhibitory effect of SiO\({}_{2}\) in coal gangue surpasses the enhancement from asphalt wetting. As a result, the interaction capability continues to weaken at 52 \({}^{\circ}\)C.