esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Road Damage Detection, Assessment, and Classification  
Assessing road infrastructure damage is critical for disaster response and recovery. Numerous deep learning-based approaches have been proposed in recent years, which can be broadly classified based on the type of data used and the architectural frameworks employed.  
[PERSON] et al. [28] developed a multi-layer perceptron model that integrates post-earthquake Synthetic Aperture Radar (SAR) imagery and field-based International Roughness Index (IRI) data from the 2016 Kumamoto earthquake in Japan, achieving a detection accuracy of 87.1%. [PERSON] et al. [29] applied a segmentation model based on the Segment Anything Model (SAM) to high-resolution satellite images and reported a pixel-level F1-score of 76.09% using the precisely annotated CAU-RoadDamage dataset.  
[PERSON] et al. [20] quantified bridge damage in Ukraine after disaster events using remote sensing and multi-layer deep learning models, while [PERSON] et al. [30] proposed a hybrid CNN model with Gabor filters for the simultaneous detection of damage to both roads and buildings. [PERSON] et al. [31] leveraged the RDD2022 dataset, which comprises road damage images from six countries, and applied a YOLO ensemble model to achieve a maximum F1-score of 76.9%. [PERSON] et al. [27] and [PERSON] [32] utilized CNN models to detect and classify road damage from satellite and smartphone images, yielding improved efficiency and accuracy. [PERSON] and [PERSON] [33], [PERSON] and [PERSON] [34], and [PERSON] and [PERSON] [35] proposed enhanced YOLO-based models, achieving higher F1-scores up to 0.636 and improved mAP (mean average precision) by 2.5%.  
[PERSON] [36] exploited elevation data to identify flooded road segments. [PERSON] and [PERSON] [3] investigated road continuity, and [PERSON] and [PERSON] [37] supported the deep learning process with optimization techniques, resulting in more robust identification of road damage from disasters.  
However, existing studies exhibit two primary limitations: (i) the lack of explicit change detection mechanisms within model architectures, and (ii) optimization for specific object types (e.g., buildings or roads), which hinders generalization across other infrastructure types. To address these gaps, this study introduces a change detection-centered framework utilizing temporally aligned satellite imagery to quantitatively evaluate road damage.
# Influence of Monomer Sequence on the Cyclization Behavior of Poly(acrylonitrile-_co_-acrylamide)
## 2 Materials and Methods
The molecular weights were recalculated for poly(acrylonitrile) using known coefficients of the Mark-Houwink equation K\({}_{1}\)M\({}_{1}\)\({}^{\alpha 1}\) = K\({}_{2}\)M\({}_{2}\)\({}^{\alpha 2}\), where K = 39.4 \(\times\) 10\({}^{-4}\), \(\alpha\) = 0.75 for PAN, and K = 17.7 \(\times\) 10\({}^{-4}\), \(\alpha\) = 0.62 for PMMA [36]. FTIR spectroscopy in ATR mode (diamond crystal) was recorded using Spectrum Two FT-IR Spectrometer (PerkinElmer) in the range of 4000-400 cm\({}^{-1}\). For quantitative analysis of the composition of the films of the synthesized copolymers, the calibration curve was used (Figure S1). The calibration curve was obtained from the mixtures of the monomers of the known molar ratio. The ratio of the intensities, A of the characteristics bands assigned to \(\
u_{\mathrm{CO}}\) = 1690 cm\({}^{-1}\) (AAM) and \(\
u_{\mathrm{CN}}\) = 2229 cm\({}^{-1}\) (AN) was used [37]. The dependence of A\({}_{\mathrm{CO}}\)/A\({}_{\mathrm{CN}}\) on the molar ratio [AAM]/[AN] was linear. The copolymers of the known composition were used to confirm the validity of the calibration curves. \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra of copolymers were recorded using pulse Fourier spectrometer Bruker DPX-500 operating at 500 MHz in DMSO-d\({}_{6}\). Tetramethyl silane was used as an internal standard. Differential scanning calorimetry was performed on a Netzsch DSC 204 (Netzsch, Germany) in the atmosphere of dry gas (air, argon) at flow rate of 100 mL\(\cdot\)min\({}^{-1}\) in the range of 30-500 \({}^{\circ}\)C and heating rate from 5 to 30 K\(\cdot\)min\({}^{-1}\) in air and 10 K\(\cdot\)min\({}^{-1}\) in argon. The activation energy of the reactions was determined by Kissinger method [38]:  
\[-\frac{\mathrm{E_{a}}}{\mathrm{R}}\ =\ \frac{\mathrm{d}\left[\mathrm{ln}\!\left( \frac{\mathrm{g}}{\mathrm{T_{P}^{2}}}\right)\right]}{\mathrm{d}\left(\frac{ \mathrm{l}}{\mathrm{T_{P}^{2}}}\right)} \tag{1}\]  
where R--universal gas constant, T\({}_{\mathrm{P}}\)--peak temperature (K), and \(\phi\)--heating rate (K\(\cdot\)min\({}^{-1}\)). FTIR spectra in ATR mode (diamond crystal) of the films subjected to thermal treating in argon at 200 and 225 \({}^{\circ}\)C and in air at 200 \({}^{\circ}\)C were recorded using Spectrum Two FT-IR Spectrometer (PerkinElmer, Waltham, MA, USA) in the range of 4000-400 cm\({}^{-1}\). The proportion of the unreacted nitrile groups \(\phi_{\mathrm{CN}}\)[39] and stabilization index E\({}_{\mathrm{s}}\) were determined according to the equations [40]:  
\[\mathrm{\varphi_{CN}}\ =\ \frac{A_{2240\ \mathrm{cm}^{-1}}}{A_{2240\ \mathrm{cm}^{-1}}+fA_{1590\ \mathrm{cm}^{-1}}} \tag{2}\]  
\[\mathrm{E_{s}}\ =\ \frac{A_{1590\ \mathrm{cm}^{-1}}}{A_{2240\ \mathrm{cm}^{-1}}} \tag{3}\]  
where \(A_{2240\ \mathrm{cm}^{-1}}\)--intensity of the band assigned to -C\(\equiv\)N group, \(A_{1590\ \mathrm{cm}^{-1}}\)--intensity of the band assigned to -C=N\(-\) group, and \(f\)--the ratio of the extinction equal to 0.29.
# Green Approaches to Carbon Nanostructure-Based Biomaterials
## 2 Green Routes to Prepare Carbon Nanomaterials for Biomedical Applications
### Graphene-Based Materials  
The class of graphene-based materials is vast and diverse, thus it is important to be aware of the specific type of structure under study, and how it may differ relative to existing literature on the topic [191]. Graphene has captured scientists' imagination for a variety of uses in medicine, yet there are still unsolved challenges to its wide implementation on a global industrial scale to widely reach the market, although steady progress is being made in this direction [192]. Among the many applications, those related to antibacterial properties are highly studied [193]. The unique properties due to the 2D nature of graphene-based materials are of particular importance for uses in sensing [194]. These materials have been widely studied for their biocompatibility and the data gathered thus far is promising, although, given the wide diversity of graphene-based materials, it is desirable to move from descriptive to predictive toxicology [5]. In particular, formation of a biomolecular corona on graphene-based materials and its consequences on biodistribution and cytocompatibility has been recently reviewed [39].  
Graphene can be produced in a variety of ways, of which the most popular include graphite exfoliation in the presence of various dispersants (Figure 8). However, to ensure good water dispersibility, graphene is typically oxidized to graphene oxide (GO), in a process for which green alternatives are continually sought [195]. In particular, electrochemical oxidation of graphite in acidic water was efficient within seconds to yield GO-based conductive materials [76]. The process could be further enhanced by exploiting synergy with photochemistry [77].  
GO is often reduced for enhanced electronic properties, and quite a few green approaches for this step have been recently reported. For example, GO was efficiently reduced by natural polysaccharides extracted from an edible mushroom to yield nanosheets that displayed good biocompatibility up to 0.1 mg/mL [78]. The amino acid cysteine was also effective at reducing GO in ethanol/water solutions with a sonochemical treatment, to yield rGO that was then embedded in a hydrogel for drug delivery [79]. Ascorbic acid provided another example of green reductant for GO to yield nanostructures that were investigated for applications in neuroscience [80]. Once GO or rGO is produced, the material can be further functionalized as described above for the other oxidized carbon nanomorphologies, covalently or non-covalently, as shown for instance for the bioconjugation of a peptide that benefited from enhanced antibacterial activity and reduced hemolytic effects [196]. Green routes towards inclusion of GO into (bio)composites are also receiving attention [197].  
Figure 8: Graphene dispersed in water can be obtained from graphite through exfoliation with a variety of dispersing agents. Reproduced from [192], published by the Royal Society of Chemistry.
## 5 Guidance of Traffic Parameters for Signal Control Optimization
### Guidance for Coordination Control Subarea Division  
Regional coordination control represents an important approach for improving overall network operational efficiency, while the scientific division of coordination control subareas  
Figure 18: Average travel times for each link.  
constitutes a prerequisite for achieving effective coordination. Multi-dimensional traffic parameters extracted from trajectory data provide a quantitative basis for subarea division.  
Through a comprehensive analysis of parameters including link flows, intersection spacing, average travel times, and vehicle OD volumes between intersections, traffic correlation between intersections can be accurately quantified. When flow from an upstream node into a downstream node approaches or exceeds the downstream node's approach capacity, greater flows or larger ratios of average queue length at the downstream node approach to link length between upstream and downstream nodes indicate a stronger correlation between the two nodes. Additionally, more OD paths passing through two nodes and greater OD path flows also strengthen inter-node correlation.  
Based on the above principles and combined with extracted traffic parameter analysis, the study network can be divided into four coordination control subareas:  
1. Intersections 1, 9, 17 subarea: forming an east-west arterial coordination system
2. Intersections 2-8 subarea: constituting a north-south major arterial coordination system
3. Intersections 10-16 subarea: covering the central area network
4. Intersections 18-22 subarea: controlling peripheral area traffic flows This subarea division method, based on actual traffic flow characteristics, compared with traditional methods based on geometric distance or administrative boundaries, better reflects inherent traffic flow relationships and helps improve coordination control effectiveness. Particularly during morning peak periods, this division can effectively address the special requirements of tidal traffic flows.
## 2 Theoretical Background
### Acoustic Performance of EoLHM in the Literature
#### 2.3.3 Plastic Bottles and Metal Cans  
Plastic bottles of different sizes (500 mL, 750 mL, 1 L, 1.25 L and 2 L) can be easily found on the market [18]. In the literature, bottles have been analyzed mainly from the structural point of view when incorporated in the construction of walls since, if compared to ceramic and concrete blocks, they are faster to build, require less water and cement and do not produce waste [44]. For these reasons, plastic bottles are continuously investigated. According to [45], polyethylene terephthalate-based material shows good sound-absorbing characteristics, especially at high frequencies. Panels made of recycled PET and sheep wool showed an absorption coefficient \(\alpha\) higher than 0.7 in the range of 50-5700 Hz regardless of the humidity content [46]. The sound absorption of light-soft-plastic bottles with net capacity from 7 to 2000 mL is affected by the capacity in the range of 100 and 1000 Hz [47]. Plastic bottles are often used to hold materials that lack structural strength. For example, a slightly lower \(TL\) than those of traditional construction materials was measured for PET bottles filled with plastic bags [18]. End-of-life PET bottles were incorporated in a wall 12 cm thick and tests showed a reduction between 29.8 dB and 55.8 dB than the wall without bottles [48]. Additionally, the acoustic properties of aluminum cans were investigated [49]: a sandwich panel made of polystyrene, pressed aluminum cans, rockwood and corrugated cardboard showed a better acoustic performance than gypsum panels, but lower than panels made of rock-wool and egg-boxes.
## 3 Results
### EPR Study
#### 3.1.2 Features of EPR Spectra of Horse Chestnut Extracts  
The spectra of non-irradiated PS and SS horse chestnut extracts exhibit a narrow asymmetric EPR signal at g = 2.00 and \(\upmu\)H = 0.359 mT, whose intensity slightly decreases after irradiation, while the value of the g-factor did not change. The same signal was observed in other cellulose-containing products from plant origin, like rosehip seeds [19]. According to the literature data, this g-value is very close to those of carbon-centered radicals [20]. Since _Aesculus hippocampal_ L. contains compounds having antioxidant activity, this suggests the possibility that the recorded signal is due to radicals formed as a result of scavenging activities [21]. Lignin, which is a heterogeneous, natural polymer involved in the construction of cell walls in plants could react with a DPPH\(\bullet\) molecule (used to detect antiradical activity). It is supposed that lignin captures and stabilizes DPPH radicals [22], and during the process, stable C-centered radicals are being formed.  
Figure 1: EPR spectra of chestnut peeled seeds irradiated by different doses of \(\gamma\)-rays: (**a**) 3 days after irradiation; (**b**) 250 days after irradiation; (**c**) “satellite” lines due to “cellulose” liked spectrum; (\(\rightarrow\)) pair of lines due to “carbohydrate” spectrum.  
Figure 2: Time stability of the central line in the EPR spectra of irradiated with 1, 5 and 10 kGy horse chestnuts: (**a**) peeled seeds; (**b**) shell seeds.
## 2 Reliability Evaluation Technique for NDT
### Mathematical Model for POD  
The POD represents the probability of detecting a flaw as a function of the flaw size. Mathematically, the detection probability for flaw size \(a\) is expressed as POD(\(a\)). For a certain distribution, by using the expressions for the detection probability (\(p\)) according to the flaw size (\(a\)) and the density function of the detection probability, i.e., \(f_{a}(p)\), POD(\(a\)) can be expressed as follows:  
\[\text{POD}(a)=\int_{0}^{1}pf_{a}(p)dp \tag{1}\]  
As indicated by Equation (1), POD(\(a\)) is a continuous function of the average detection probability at flaw size \(a\). To estimate the corresponding regression model, a logistic model reported by [PERSON] as the optimal model for nondestructive evaluation was applied. The logistic model provides values close to the cumulative normal distribution and allows easy interpretation of the result. It is expressed as follows:  
\[\text{POD}(a)=\frac{exp(\alpha+\beta ln\alpha)}{1+exp(\alpha+\beta ln\alpha)} \tag{2}\]  
Here, \(\alpha\) and \(\beta\) can be obtained through general regression analysis or the method of maximum likelihood estimation (MLE). The logistic model is widely applied for reliability analysis of NDT results.
## 3 Results and Discussion
### Calibration Models Using Optimal Wavelengths
Based on the results, prediction maps for visualization of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid content in fresh and dry _Chrysanthemum morifolium_ flowers were formed. In general, ELM models using full spectra obtained better results for fresh and dry flowers. Thus, ELM models using full spectra were applied to construct the visualization maps for chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in _Chrysanthemum morifolium_ flowers. Based on the established models, these three chemical compositions content of each pixel within one flower could be obtained. Prediction maps by knowing pixelwise chemical composition content could be formed. However, for harvest, trade, and consumption, entire flowers were used rather than parts of each flower; knowing the average chemical composition content of all pixels within one flower would be more intuitive than knowing the distribution of pixelwise chemical composition content within one flower. Thus, average chemical composition content of each fresh and dry flower was visualized and the differences among flowers could be visualized. Figures 2 and 3 show the prediction maps of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid in fresh and dry _Chrysanthemum morifolium_ flowers, respectively. As shown in Figures 2 and 3, the size of flowers reduced after drying. With chemical compositions content presented by color, it would be possible to develop online detection systems or portable devices for rapid and accurate determination of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid as well as other chemical compositions in fresh and dry _Chrysanthemum morifolium_ flowers. The results would benefit the harvest, processing, and consumption of _Chrysanthemum morifolium_. Figure 2: Pseudo image of (**a**) fresh _Chrysanthemum morifolium_ flowers and prediction maps of chlorogenic acid (**b**), luteolin-7-O-glucose (**c**), and 3,5-O-dicaffeoylquinic acid (**d**). (Unit: % DW, DW means dry weight.)
###### Abstract  
This paper presents a thorough review and detailed analysis of nanosensors for structural health monitoring (SHM) in the context of bridge maintenance. With rapid advancements in nanotechnology, nanosensors have emerged as promising tools for detecting and assessing the structural integrity of bridges. The objective of this review is to provide a comprehensive understanding of the various types of nanosensors utilized in bridge maintenance, their operating principles, fabrication techniques, and integration strategies. Furthermore, this paper explores the challenges associated with nanosensor deployment, such as signal processing, power supply, and data interpretation. Finally, the review concludes with an outlook on future developments in the field of nanosensors for SHM in bridge maintenance.  
nanosensors; structural health monitoring; bridge maintenance; operating principles; fabrication techniques +
Footnote †: journal: Applied sciences  
offer high sensitivity, fast response, and multi-parameter detection capabilities, allowing for comprehensive monitoring of bridge conditions [9].  
Given the potential of nanosensors in bridge maintenance, it is essential to comprehensively review and analyze the various types of nanosensors available, their fabrication techniques, integration strategies, and the challenges associated with their deployment. This paper aims to provide a detailed exploration of nanosensors for SHM in bridge maintenance, shedding light on the innovations, challenges, and future perspectives in this rapidly evolving field.
## 2 Methods
### Corrosion Model  
This study employed a nonuniform rust distribution which is expressed in terms of the rust thickness [\(T_{cl}(\theta)\)]. The rust thickness is calculated as [21]:  
\[T_{cl}(\theta) =\lambda\frac{e^{kcos(\theta-\pi)}}{2\pi\;i_{corr}(t)} \tag{1}\] \[\lambda =\alpha\;\pi\;r\;\eta\] (2) \[\eta =\frac{A_{sl,corr}}{A_{0}} \tag{3}\]  
where \(\lambda\) is a fitting parameter; \(\theta\) is the angle of the rust distribution; \(k\) is the concentration coefficient defining the level of nonuniformity of the rust distribution (=0.555 in this study based on [21]); \(i_{corr}\) is the time-dependent corrosion rate; \(t\) is the corresponding year; \(\alpha\) is the rust expansion ratio; \(r\) is the rebar radius; \(\eta\) is the relative area loss of rebar; \(A_{0}\) is the initial cross-section area of rebar; and \(A_{sl,corr}\) is the cross-section area loss of corroded rebar, which can be determined as follows [22]:  
\[A_{sl,corr} =2r^{2}(\theta-\sin\theta\cos\theta) \tag{4}\] \[\theta =arccos\left(1-\frac{dp}{2r}\right);\;2/3\pi\;\leq\;dp\;\leq\;4/3\pi\] (5) \[dp =0.0116i_{corr}\;R\;t \tag{6}\]  
where \(R\) is a pitting factor variable ranging from 4 to 8 (=5.65 in this study based on [7]), and \(dp\) is the maximum rust thickness in the radial direction [7]. The time-dependent corrosion rate was determined as follows [23]:  
\[i_{corr}(t) =0.85i_{corr}(1)\;t^{-0.29} \tag{7}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**PC-Bar and Transverse Rebar Material Properties** & \\ \hline \(f_{y}\) & PC-bar yield strength (MPa) & 1275 \\ \(f_{u}\) & PC-bar ultimate strength (MPa) & 1420 \\ \(E_{s}\) & PC-bar elastic modulus (MPa) & 196,500 \\ \(\sigma_{ini}\) & PC-bar initial stress (MPa) & 781 \\ \(f_{ys}\) & Transverse rebar yield strength (MPa) & 360 \\ \(f_{us}\) & Transverse rebar ultimate strength (MPa) & 540 \\ \(E_{ss}\) & Transverse rebar elastic modulus (MPa) & 196,500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(Cont.\)  
Figure 1: Spun pile dimensions.  
\[i_{corr}(1)=\frac{3.78\ (1\ -\frac{w}{c})^{-1.64}}{x} \tag{8}\]  
where \(i_{corr}(1)\) is the corrosion rate at the beginning of the corrosion propagation; \(x\) is the spun pile cover concrete thickness; and \(\frac{w}{c}\) is the water-cement ratio of the concrete, which was set to 0.28 based on the material specifications of the spun piles [24].  
Since the spun pile section was closed-ended, it was assumed that the irregular rust distribution spread from the surface inward. The impacts of the rust's nonuniform distribution were also considered using volumetric strain expansion in the FEA. A corrosion degree, the ratio of the corroded rebar's mass loss to the rebar's initial mass, was used to describe the material properties of the corroded spun pile. The corrosion degree might be equated to the rebar area loss if the rebar area loss was evenly distributed along the rebar. On the other hand, the corrosion rate represents the increased rate of rust thickness and can be measured in amperes per unit rebar area.