esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Although the double-layer pavement structure with a top layer of stone mastic asphalt concrete (SMAC) and a bottom layer of epoxy asphalt concrete (EAC) has been confirmed to have excellent overall performance in the laboratory, there is a lack of comparison and verification in practical projects. Hence, the utilization of the SMAC + EAC structure in this steel bridge deck pavement (SBDP) practical project and the clarification of its service performance are of significant importance for facilitating the promotion and application of this novel structure. This study relied on an SBDP reconstruction project in Ningbo, China. Indoor performance tests were used to determine the appropriate material compositions for SMAC and EAC. Subsequently, both ERS and SMAC + EAC pavement structures were paved in the project, and the service conditions of the different pavements after one year of operation were tested and compared. The results indicated that the epoxy SBS asphalt (ESA) binder prepared by substituting SBS-modified asphalt binder for the base binder, exhibited improved mechanical strength and toughness. The variation of modifier content significantly affected the high-temperature stability, low-temperature crack resistance, and moisture damage resistance of epoxy SBS asphalt concrete (ESAC) and high-viscosity SBS asphalt concrete (HSAC), while the gradation mainly influenced the skid resistance. The optimal contents of modifiers in ESA and HAS binders were finalized at 45 wt% and 11 wt%. After one year of operation on the trial road, the pavement performance of the SMAC + EAC structure had significant advantages over the ERS system, with all lanes having an SBDP quality index (SDPGI) above 90 and an excellent service condition. The successful application of the SMAC + EAC structure validated its applicability and feasibility in SBDP, which provided strong evidence for the further promotion of this structure.  
steel bridge deck pavement; SMAC + EAC; material optimization; performance evaluation; case study +
Footnote †: journal: Applied Sciences
# Searching Open-Source Vulnerability Function Based on Software Modularization
## 4 Results and Analysis
### Open-Source Vulnerability Function Detection and Analysis of Binary Modules  
Open-source components reuse may introduce security vulnerabilities into the binary program. The vulnerability function detection results for the top 10 frequently reused components in dataset II are shown in Figure 4.  
As seen from the figure, we find that only Zlib, Libpng, and unrar don't contain vulnerabilities in the last three years (Libtiff: 2015-2017). The number of vulnerability functions is relatively high in FreeType and SQLite, reaching 19 and 11, respectively. In software development, once these component codes containing a large number of vulnerability functions are reused, the program will face potential security risks.  
Therefore, it is necessary to detect vulnerabilities caused by the reuse of vulnerable components. We detect vulnerability functions of binary programs reusing vulnerable components on Dataset I. The vulnerability function results are shown in Table 7. The fourth and fifth columns are the source code and binary vulnerability functions detected by BVMul. A \"\(V\)\" means the vulnerability function is reused in the binary.  
By analyzing the results in the table, based on the software component analysis, we search vulnerability functions in the binary modules that reuse components containing vulnerabilities. We find specific reused vulnerability functions in OpenVPN, Lzbench, and Redis-server, which are no longer limited to detecting potential vulnerabilities. In addition, compared with the detection of the whole binary program file, vulnerability function detection in the module reduces the amount of function matching and avoids comparison analysis of binary function unused components.  
Figure 4: Frequently reused components vulnerabilities.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 2 Related Work
### Advancements in Traffic Prediction Innovations and Solutions Using Graph Convolution Network
By analyzing real-time data from various sources, including traffic cameras and IoT devices, GCNs facilitate efficient routing algorithms that optimize traffic flow and reduce congestion. Additionally, GCNs can assist in the identification of critical areas for infrastructure investment, enabling urban planners to make data-driven decisions. This capability not only contributes to improved traffic management but also promotes sustainability by encouraging the use of public transportation and reducing carbon emissions. Overall, GCNs play a transformative role in smart city initiatives, fostering enhanced connectivity, operational efficiency, and an improved urban living environment.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 5 Simulation Results
### Network Performance Simulation
#### 5.2.1 Network Saturation Performance Simulation  
We investigated cases with 2, 5, 10, and 20 stations. The bandwidth utilization decreased as the bit error rate (BER) gradually increased. The bandwidth utilization for a BER greater than 0.0004 approached zero (Figure 13).  
Figure 11: Node simulation results for the average hop count. (**a**) Average hop count for node[0]; (**b**) average hop count for node[2]; and (**c**) average hop count for node[8].  
Figure 12: Hop versus statistic hop count. (**a**) Hop versus statistic count for node[0]; (**b**) hop versus statistic count for node[2]; and (**c**) hop versus statistic count for node[8].  
Figure 14 depicts the relationship between the bandwidth utilization and the number of stations for a payload of 128 B. When \(r=1\), the bandwidth utilization decreased by 5.9% for a BER of 0.00005 compared to \(BER=0\). When \(r=3\), the bandwidth utilization decreased by 9.6% at a BER of 0.00008 compared to \(BER=0\). The bandwidth utilization improved by 1.8% at \(r=3\) and \(BER=0.00008\), relative to its utilization at \(r=1\) and \(BER=0.00005\). The bandwidth utilization by the improved adaptive \(p\)-CSMA decreased by 3.5% and 1.8% at \(r=1,3\) compared to the adaptive \(p\)-CSMA.  
Figure 15 depicts the bandwidth utilization results for a payload of 512 B. At \(r=1\), the bandwidth utilization decreased by 34% at a BER of 0.0001 relative to a BER of 0. Similarly, when the BER was zero, the bandwidth utilization improved by 1.1% at \(r=3\) relative to \(r=1\). The bandwidth utilization decreased by 19% between \(r=3,BER=0.00005\) and \(r=1,BER=0\). The bandwidth utilization by the improved adaptive \(p\)-CSMA decreased by 1.9% and 1% for \(r=1,3\) compared to the adaptive \(p\)-CSMA. The bandwidth utilization increased by 7% and 16.2% at \(r=1,3\) compared to a payload of 128 B.  
Figure 14: 128 B payload bandwidth utilization.  
Figure 13: Throughput versus BER.  
Figure 16 depicts the relationship between the access delays and the station numbers for 128 and 512 B payloads. The access delays for a payload of 128 B were increased by 9.4%, which was 2.57 times by the improved adaptive \(p\)-CSMA at \(r=1,3\) relative to the adaptive \(p\)-CSMA. The delays for a payload of 512 B increased by 0.75% and 36.2% at \(r=1,3\). Compared to the results for a payload of 128 B, the access delays were improved by 2.12% and 29.6% at \(r=1,3\).
## 4 Representative Machine Learning Models for IONM-Related Research
### Neural Networks
#### 4.1.1 Artificial Neural Networks  
An artificial neural network (ANN) is an ML algorithm imitating the human brain [39]. Neurons in the human brain combine signals (stimuli) from multiple upstream neurons; when the combined stimulus exceeds the threshold, the neuron relays the resulting signals to the downstream neurons. ANNs were designed according to this principle. A neural network consists of an input layer that receives data from multiple inputs, an output layer that produces the output results, and one or more hidden layers between them [24]. Each layer comprises multiple neurons and contains learning parameters, such as connection weights (\(w_{ij}\)) and biases (\(b_{j}\)), where \(i\) and \(j\) are the indices for the input and output neurons, respectively. Given an input vector,  
\[x=(x_{1},x_{2},\dots,x_{n})\]  
each layer computes the output,  
\[y_{j}=(y_{1},y_{2},\dots,y_{m})\]  
via a linear combination followed by a nonlinearity; for instance, the output may be calculated as follows:  
\[y_{j}=f(\sum_{i}w_{ij}x_{i}+b_{j})\]  
where \(f(\cdot)\) denotes a nonlinear activation function. The behavior of a neural network depends on the values of the connection weights and biases. The learning algorithm determines the optimal values of these parameters for the target task by minimizing the loss function on the training data.  
The neural network learns to map an input into the desired output. An ANN composed of multiple layers with nonlinear activation functions can approximate complex maps [27]. However, the training of a large-scale neural network requires a large amount of training data and computational power [40]. In particular, when trained with a limited number of samples, ANNs often suffer from overfitting and are not generalizable to different input data.
## 3 Results
### Applications Across Industrial Domains
#### 3.3.4 Power Generation and Distribution  
For transformers, SVMs trained on data from \(16,000\) units achieved over 95% accuracy and reduced costs by 13% [60]. In hydroelectric plants, RFs predicted failures within 12-48 h at 98% accuracy [61]. LSTM autoencoders identified thermal faults in generators with 99% accuracy [62], while Gaussian process classifiers combined with IoT sensors achieved 99.56% accuracy in monitoring electrical panels [63].
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
## 2 Network Architecture
### Multimodal Access Interconnection in MMNET  
In order to better support multi-mode heterogeneous communication and use multi-mode communication technology to support applications, MMNET adopts a standard hardware interface and SDR to realize physical layer and MAC layer; the standardized hardware interface is to better support different hardware front ends. SDR aims to support differentiated communication technology, on the basis of the same hardware architecture, and provide a transparent communication interface for the whole network.  
In MMNET, different hardware front ends are connected to general hardware through a standard hardware interface (such as serial port, bus interface, etc.); the standard hardware interface is conducive to supporting more hardware devices. New communication hardware can be supported on the whole network as long as it meets the requirements of the hardware interface. When the communication hardware is used, SDR technology can be used to realize the software implementation of the new communication mode. On the premise of not replacing the hardware front end, it can provide updated and more communication technology support, reduce the cost of network use, improve the performance of the whole network and provide more communication support for applications.  
Using SDR technology, MMNET encapsulates different communication technologies into communication services by software and provides a unified communication interface for the network. When new communication technology is needed, new communication equipment is connected on the basis of general hardware, and the communication method, MAC protocol and service interface are realized by software. After the transparent network communication service interface is implemented, the network communication service interface will be registered in the network  
Figure 1: The architecture of MMNET (multimodal network). MMNET uses software defined radio (SDR) technology and standard hardware interface to realize multi-mode communication, including multimedia access (MAC) layer and physical layer, and uses network function virtualization (NFV) technology to organize application (APPs) layer, transmission layer (Trans) and routing layer.  
management program and the new communication technology will be supported in the current network. This function of MMNET, as shown in the Figure 2, can enhance the communication ability of the underwater network and improve the performance of the whole network by using different underwater communication equipment, such as MI, optical harmony and Orthogonal Frequency Division Multiplexing (OFDM) technology.
# Biosynthesis of Polyhydroxyalkanoates from Defatted _Chlorella_ Biomass as an Inexpensive Substrate
## 3 Results and Discussion
### Characterization of PHA Polymers
#### 3.2.2 \({}^{1}\)H NMR  
The \({}^{1}\)H NMR spectra of PHA polymers accumulated by _B. megaterium_ ALA2 and _C. reactor_ KCTC 2649 showed three groups of peaks characteristic of PHB homopolymer, including the CH\({}_{3}\) group at 1.29 ppm, the CH\({}_{2}\) group at 2.57 ppm, and the CH group at 5.27 ppm (Figure 5a,b). Similar \({}^{1}\)H NMR patterns were previously recorded for PHB produced by either _B. megaterium_ ALA2 or _C. reactor_ KCTC 2649 when using brown algae (_Sargassum_ sp.) hydrolysate and glucose as carbon sources, respectively [8; 30].  
Figure 5: The \({}^{1}\)H NMR spectra of PHA produced by **(a)**_Bacillus megaterium_ ALA2, **(b)**_Cupriaridus necator_ KCTC 2649, and **(c)**_Haloferax mediterranei_ DSM 1411 using 0.3 N HCl DCB hydrolysate as the carbon source.  
With respect to PHA polymers produced by _H. mediterranei_ DSM 1411, the \({}^{1}\)H NMR spectrum clearly shows two additional peaks at 0.9 ppm and 1.6 ppm respectively corresponding to the methyl and methylene groups of the valerate monomer, denoting that the produced PHA is a P (3 HB-_co_-3 HV) copolymer (Figure 5c). Similar peaks were identified in the \({}^{1}\)H NMR spectra of the P (3 HB-_co_-3 HV) copolymer produced from macroalgal biomass hydrolysis [31]. The 3 HV molar fraction in the P (3 HB-_co_-3 HV) copolymer was calculated from the area ratio of methyl group peaks corresponding to 3 HB and 3 HV units (1.27 and 0.9 ppm, respectively). Based on this calculation, the 3 HV molar fraction was present in a ratio of 10.5 mol%. This 3 HV ratio was higher than that recorded for the P (3 HB-_co_-3 HV) copolymer accumulated by _H. mediterranei_ from different carbon sources, including _Ulwa sp_. macroalgae hydrolysate [7], hydrolyzed whey permeate [32], and glucose and yeast extract [33]. Likewise, the 3 HV content obtained in this study was 68.8% higher than that recorded in our previous work when the same carbon source (DCB hydrolysate) was used by _Paracoccus_ sp. LL1 [10]. Comparable 3 HV content was previously recorded for the P (3 HB-_co_-3 HV) copolymer accumulated by _Paracoccus_ sp. LL1 cultivated in a minimal medium supplemented with glucose [10]. These results underline the ability of _H. mediterranei_ to use DCB hydrolysate as an inexpensive and sustainable carbon source for the accumulation of P (3 HB-_co_-3 HV) copolymers without any precursor supplementation.
## 2 Materials and Methods
### Analysis
#### 2.2.2 Volatile Analysis  
Volatile compound analysis was performed using a Shimadzu GC-MS system (GCMS-TQ8050 NX, Shimadzu Corporation, Kyoto, Japan), equipped with a Shimadzu AOC-6000 Plus Autosampler. A 50/30 \(\upmu\)m DVB/CAR/PDMS fiber (Supelco, Bellefonte, PA, USA) was employed for SPME. Samples were incubated at 65 \({}^{\circ}\)C for 10 min, extracted under the same conditions for an additional 10 min, and thermally desorbed for 3 min at 240 \({}^{\circ}\)C in splitless mode. The inlet temperature was maintained at 240 \({}^{\circ}\)C, with helium used as the carrier gas.  
The GC oven was programmed to start at 35 \({}^{\circ}\)C with a 5 min hold and increase to 100 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min, then to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then to 160 \({}^{\circ}\)C at 8 \({}^{\circ}\)C/min, and finally to 250 \({}^{\circ}\)C at 25 \({}^{\circ}\)C/min with a final hold of 5 min. The total runtime was 39.52 min. Separation was achieved using an HP-5 MS UI column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m; Agilent J&W GC columns, Santa Clara, CA, USA). The MS source temperature was set to 200 \({}^{\circ}\)C, and the interface transfer line was held at 280 \({}^{\circ}\)C.  
Prior to quantitative analysis, standards were subjected to a full scan (\(m/z\) 40-400) followed by a product ion scan to optimize collision energy for achieving optimal transitions. The MRM method was established using the full scan as a template. For precise measurements, analytes were grouped into 11 major events, each consisting of multiple transitions specific to individual compounds, with a loop time of 0.0550 s per event. The detector was operated at an absolute voltage of 1.3 kV, with the ion source and interface temperatures maintained at 200 \({}^{\circ}\)C and 280 \({}^{\circ}\)C, respectively, while all other parameters, including oven programming and injection, mirrored those of the full scan method.  
For quantification, an internal standard-based MRM calibration curve comprising 10 calibration points (10-10,000 \(\upmu\)g/L) was prepared using a standard mixture of 41 compounds (purity > 95%, Sigma-Aldrich, St. Louis, MO, USA) spanning various chemical classes (esters, aldehydes, terpenes, terpene alcohols). Calibration solutions included 500 \(\upmu\)g/L of five internal standards (hexanal-d\({}_{12}\), ethyl hexanoate-d\({}_{11}\), phenylacetaldehyde-d\({}_{5}\), linalool-d\({}_{5}\), and beta-myrcene-d\({}_{6}\); purity > 94%, AromaLAB GmbH, Planegg, Germany), each representing a distinct compound class. All analytes demonstrated linear calibration curves with R\({}^{2}\) > 0.999. Following calibration, unknown compounds in the samples were quantified relative to the internal standards assigned to their respective classes.
## 5 Conclusions and Closing Remarks  
The Canary Islands are frequently affected by Saharan dust intrusions, owing to their proximity to North Africa, making them an ideal location for atmospheric dust sampling. Culturable microorganisms and their mineral-bacteria interactions were studied for the first time in Saharan dust collected during three calima events (2021-2022) in Gran Canary Island (close to Morocco). Iberulites--a singular type of giant quasi-spherical particle generated in the troposphere, with a diameter between 50 and 200 --were identified during the events. Bacterial colonies and biofilm were observed in the mineral particle surfaces and in the iberulites. The atmospheric dust constitutes an authentic medium for bacterial growth, a novel finding evinced in the SEM images.  
For the first time, a process of neoformation of nanometric kaolinite crystals mediated by microorganisms has been described in the atmosphere, which has been limited to laboratory studies until now.  
Twenty-three culturable microorganisms were isolated and identified by 16S rRNA sequencing. Members of the phyla _Pseudomonasota_, _Bacilota_ and _Actinomycetota_ have been found. Some species have shown beneficial effects in plants (_Peribacillus frigoritolerans_), whereas others are potential pathogens for human health (_Acinetobacter lwoffii_).  
Calimas in the Canary Islands are natural phenomena, which are not controllable, nor is it possible to reduce them. The prevention and control of these phenomena should be approached from the novel and holistic perspective of \"One Health\", which recognises that human health is closely linked to animal and environmental health. Our work can contribute to a better understanding of their effects on the population and ecosystems. Many new microbiomes undoubtedly await description, such as the aerobiome, and its emerging study may represent one of the major challenges of the future.  
Methodology, A.M.M., R.D. and J.P.; software, A.N., J.M.M.-G. and F.M.-C.; formal analysis, A.N., I.d.P. and F.M.-C.; investigation, A.N., A.d.M., R.D., J.P., J.M.M.-G. and F.M.-C.; data curation, A.d.M., R.D., J.P., J.M.M.-G. and F.M.-C.; writing--original draft, A.N., A.d.M., R.D. and J.P.; visualization, J.M.M.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and the READY website ([[https://www.ready.noaa.gov](https://www.ready.noaa.gov)]([https://www.ready.noaa.gov](https://www.ready.noaa.gov)) (accessed on 7 May 2022)) used in this publication. Dust surface concentration images were provided by the WMO Barcelona Dust Regional Center and the partners of the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS) for Northern Africa, the Middle East and Europe.  
The authors declare no conflicts of interest.
## 5 EBCA Testbed
#### Evaluation Metrics  
Disparity maps acquired by stereo matching algorithms can be evaluated with the use of different quality metrics [10; 36]. Metrics used in the testbed and experiments presented in this paper are the following: the percentage of bad matching pixels (BMP), the percentage of bad matching pixels in background (BMB) and the coverage (COV) [2; 3; 10]. The formula for calculating BMP is presented in Equation (14).  
\[BMP=\frac{1}{N}\sum_{\mathbf{p}}(|D_{M}(\mathbf{p})-D_{T}(\mathbf{p})|>Z) \tag{14}\]  
\begin{table}
\begin{tabular}{l c c} \hline
**Set ID** & **Matching Size** & **Disparity Range** \\ \hline \(ST_{1}\) & 440 \(\times\) 380 & 1–28 \\ \(ST_{2}\) & 340 \(\times\) 325 & 19–45 \\ \(RC_{1}\) & 420 \(\times\) 370 & 31–79 \\ \(RC_{2}\) & 320 \(\times\) 295 & 11–35 \\ \(CH_{1}\) & 470 \(\times\) 380 & 40–69 \\ \(CH_{2}\) & 330 \(\times\) 310 & 27–58 \\ \hline \end{tabular}
\end{table}
Table 1: Sizes of test data sets and the range of disparities.  
where \(\mathbf{p}\) is a point, \(D_{M}(\mathbf{p})\) is the disparity in the point \(\mathbf{p}\) in the evaluated disparity map, \(D_{T}(\mathbf{p})\) is the ground truth disparity, \(N\) is the total number of points and \(Z\) is the threshold considered in the metric. If the difference between the correct disparity and disparity indicated by the stereo matching algorithm does not exceed \(Z\), then the estimation of the disparity is regarded as correct.  
BMP presented in Equation (14) estimates the quality of disparities obtained for objects visible in the foreground. These objects are visible both in a central and side images, thus it is possible to estimate their disparities. Objects located in the background can be only partially visible in different images because from points of view of some cameras these objects can be hidden behind other objects located in the foreground. Therefore, it can be impossible to match patterns corresponding to the same object in different images. The estimation of the quality of disparities for objects located in the background is the subject of the BMP metric presented in Equation (15). The metric provides information on the percentage of points for which a stereo matching algorithm set incorrect values of disparities instead of indicating that disparities in these points are unknown.  
\[BMB=\frac{1}{N_{B}}\sum_{\mathbf{p}}(D_{M}(\mathbf{p})\
eq 0\wedge D_{T}( \mathbf{p})=0) \tag{15}\]  
where \(N_{B}\) is the number of points in background and other symbols are the same as in Equation (14). A value of a disparity equal to 0 means that the disparity is unknown.  
Another considered metric is COV which corresponds to the percentage of points for which stereo matching algorithm was able to identify disparities. The formula for this metric in presented in Equation (16).  
\[COV=\frac{N_{L}}{N} \tag{16}\]  
where \(N_{L}\) is the number of points in a disparity map and \(N\) is the total number of considered points.