esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Numerical Results
### Static Grid Topology Environment  
We consider a static grid topology involving 100 UAV nodes. Figure 5 shows the 100-UAV-node grid topology, where the distance between two neighboring UAVs is 2 km.  
Figure 6 shows the coalition structure once it has finally converged. In the end, the network is divided into 14 coalitions. The maximum number of coalition members is 10. Figure 7 gives the member number of each coalition. We find that the number of coalition members decreases with the coalition sequence, because the UAV nodes in the front join the coalition first. The UAV nodes in coalition 1 achieve 10 transmitting slots in a repetition interval.  
The utilities of every UAV node are given in Figure 8. The average utility of DCFG-TDMA proposed in this paper is 7.9 slots. The max utility and the min utility of DCF-TDMA are 10 and 3 slots, respectively. Compared to that of the Traditional TDMA and USAP, the average utility is improved significantly. This is because, although the USAP algorithm is capable of reusing time slot resources, the distributed selection process still encounters numerous conflicts, making it impossible to achieve an optimal time slot allocation outcome.  
Figure 5: Grid topology involving 100 UAV nodes.  
Figure 6: The final coalition structure for the 100-UAV-node grid topology.  
The utilities of four UAV nodes (UAV-1, UAV-41, UAV-81, UAV-91) versus the iteration step are shown in Figure 9. UAV-1 and UAV-91 achieve the maximum utility at step 93, UAV-41 achieves the maximum utility at step 77, and UAV-81 achieve the max utility at step 100.  
The convergence speed of the algorithm versus network size is given in Figure 10. The number of iterations is linearly related to the network size for the grid topology, which is a regular topology in which each UAV node achieves stable partitioning within one iteration.  
Figure 8: The utilities of every UAV node.  
Figure 7: The number of members in each coalition.  
Figure 9: The utilities vs. iteration steps.
# Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach
## 5 Results
### Comparison to Other Approaches  
This section compares the proposed approach to other approaches for road traffic anomaly detection. While there are many tensor-based approaches focused on congestion estimation, missing data imputation, and event detection, there are only a few specialized in anomaly detection on urban roads. Table 4 shows several tensor-based approaches for the anomaly detection of road traffic networks. Most of the authors are using O-D matrices that show the number of the vehicle (volume) traveling between two points in the traffic network [9; 18; 46]. Regarding the potentially large spatial distance between the O-D pairs, those approaches extract the patterns and detect global anomalies related to traffic fluctuations [47]. Therefore, most of the authors focus on the detection of the general events that are related to traffic movements like tourist attractions or other social events [46; 48].  
This paper focuses on detecting the anomalies that affect the traffic flow on micro-locations (transitions). The proposed approach is more suitable for detecting anomalies that could potentially lead to traffic accidents like sudden braking or fast acceleration. Therefore, this approach can be applied in real-time traffic accident detection and prediction.  
Alongside the possible applications of the proposed methodology, some drawbacks of the method must be addressed in the further research. The anomaly detection method is based on the speed, which can be lead to false anomaly detection on a short road segments bounded with the non-synchronised intersections. This is the reason why the short road segments with the speed limit less than 50 kmph was excluded from this research. Secondly, more narrow time intervals like 5-, 15-, or 30 min are used in most of the road traffic related research. Narrower time intervals could provide more informative results with the possibilities of implementing the method on a real-time case study especially in the environment with the mixed traffic flows [49]. Further improvements of the method could also include the analysis of the interactions between the consecutive cells. The modeling process of the STM should also be addressed. The important parameters of the STM like size, discretization period, and cell size should be analyzed for optimization purposes.
## 2 Data and Methodology
### MAX-DOAS Observations
Along with the NO\({}_{2}\) cross-sections at 298 K and 220 K ([PERSON] et al., 2018), the following trace gas cross-sections are included in the DOAS analysis: O\({}_{3}\) at 223 K ([PERSON] et al., 2018), O\({}_{4}\) at 293 K ([PERSON] et al., 2018) and H\({}_{2}\)O at 296 K ([PERSON] et al., 2018). A ring spectrum, calculated by QDOAS according to ([PERSON] et al., 2018), was also included, and a fifth-order polynomial is used to remove the broadband spectral features. The MAX-DOAS system performs elevation scans at four different azimuth viewing directions, i.e., at 142\({}^{\circ}\) (SE), 185\({}^{\circ}\) (S), 220\({}^{\circ}\) (SW) and 255\({}^{\circ}\) (W) (Figure 1). The two former directions (SE and S) cross the city center and point to urban and suburban areas, while the other two directions (SW and W) cross a small portion of the city (about 1 km) and point towards the sea. The NO\({}_{2}\) vertical profile for each elevation scan is retrieved by applying the Mexican MAX-DOAS Fit (MMF) v2020_04 ([PERSON] et al., 2018) inversion algorithm on the measured dSCDs, which is based on the Optimal Estimation Method (OEM). The NO\({}_{2}\) Vertical Column Densities (VCDs) are calculated by integrating the retrieved vertical profiles, while the \"surface concentration\" is the average concentration of the lowermost 200 m of the troposphere. MMF includes a quality flagging algorithm that is based on multiple criteria, and, in this study, the data that are flagged as erroneous were discarded from the analysis. In ([PERSON] et al., 2018), more details are given about the instrumentation, the settings that are used for the retrieval of NO\({}_{2}\) dSCDs and the input parameters for the inversion algorithm that is applied for the retrieval of NO\({}_{2}\) vertical profiles.
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Observation of Tower-Based Solar-Induced Chlorophyll Fluorescence  
The automatic spectral observation system AUTOSIF-1 (Bergsun Inc., Beijing, China) was set up on the base platform, 10 m above the canopy of the closed forest, and the optical fibers were 5-6 m away from the tower. The observation system uses a QE65 Pro spectrometer, with a spectral resolution of 0.31 nm, a sampling interval of 0.155 nm and a spectral range of 650-850 nm. The system is equipped with two optical fibers, in which the vertically up-facing fiber, with CC-3 cosine corrector (Ocean Optics Inc., Dunedin, FL, USA), was used to capture the downward solar irradiance, and the vertically down-facing bare fiber (field of view, 25\({}^{\circ}\)) was used to measure the upward radiance reflected from the canopy. The spectral automatic observation system measured the spectral data of descending irradiance and ascending radiance for approximately 3 min. The time interval between the measurement of irradiance spectrum and radiance spectrum was less than 10 s. The spectral fitting method (SFM) algorithm was built into the system to calculate the SIF value [55], and the reflectance of the canopy was continuously monitored during the growing season of Chinese cork oak--between March and November. Daily SIF were calculated as the mean of the observations between 6:00 and 19:30. The instrument was calibrated regularly and the original data were radiometrically calibrated before analysis. Due to the transmission path of radiation in the atmosphere in this study is short, we only carried out radiometric calibration and did not consider atmospheric correction [56].
## 3 Results and Discussion  
### Antioxidant Activity and Total Phenolic Content  
Ozone treatment applied to _Kalanchoe daigremontiana_ plants affected the contents of bioactive compounds. An increase in the contents of bioactive compounds, measured one day after the treatment, was observed in the case of each dose of ozone applied. The findings show that samples treated with ozone at a rate to 10 ppm for 1 min had the highest antioxidant potentials, assayed using ABTS (2,2\({}^{\prime}\)-Azino-bis-(3-ethylbenzothazoline-6-sulfonic acid) and DPPH (2,2-Diphenyl-1-picrylhydrazyl) (Figure 1). An assessment based on the ABTS method showed an increase of 54% compared to the antioxidant potential of the control samples (untreated alligator plants). A similar increase of 65% was identified in a DPPH assay. This change most possibly was an effect of oxidative burst in the apoplastic space of cells in the ozone-treated plants. This led to a greater activation of the mechanisms involved in the biosynthesis of low-molecular-weight antioxidants easily reacting with free radicals [32]. Increased antioxidant potential in plant materials exposed to gaseous ozone was also observed by other researchers [15]. Importantly, once a certain dose of ozone applied to plant materials is exceeded, the gas starts to induce catabolism which eventually leads to a significant decrease in antioxidant potential. In the case of alligator plants, thiseffect was also observed when a dose of 10 ppm was applied; when the duration of the ozone treatment exceeded 1 min, antioxidant potential was considerably lower, and in samples treated for 10 min its value was similar to that in the control samples (untreated). On the other hand, when ozone was applied at a rate of 5 ppm, no significant effect of the treatment on the antioxidant potential of alligator plants was observed, irrespective of the duration of the treatment.  
The antioxidant potential of plant material is a combined effect produced by the activity of a few groups of chemical compounds, most importantly including polyphenols. In the case of alligator plants, there was a strong relationship between total polyphenol contents and total antioxidant potential (Figure 2). The highest polyphenol contents were observed in plants exposed to gaseous ozone at a rate of 10 ppm for 1 min. The value was 79% higher than the total polyphenol contents identified in the control plants (0 ppm, 0 min).
## 1 Introduction
Recently, the influence of some more complex and irregular shapes of pores are considered in generating the porous models [15; 16; 18; 31; 32; 33; 34; 35], and some particular schemes, say hole forming agent method and space-holder technique, are developed simultaneously to reconstruct the porous model more efficiently. In Ref. [23], a robust numerical approach is developed for generating 3D representative volume element models of bicontinuous nanoporous microstructures using [PERSON]'s method [36] to generate the Gaussian random field and the sinusoidal wave of fixed wavelength but random in direction and phase. To be noted microstructures of porous materials of practical existence consist of randomly distributed pores of various sizes and different shapes and particularly, the distribution mode of these pores also obviously affects the overall elasticity of materials [13; 14; 15; 16; 17; 18; 31; 35]. Hence, this paper aims to elaborate a novel computational strategy for generating porous microstructures accounting for the actual statistical pore-size, -shape and distribution-mode information simultaneously framed within the finite element method. To alleviate the difficulty in discretizing irregularly-shaped pores of various eccentricities from the CAD model and facilitate the pores of different arrangements generation, a generalized explicit mathematical description to characterize the actual pore shapes, which covers the models applied in the open literature [13; 14; 17; 18; 27] as special cases, is introduced, and an element-based algorithm is developed to produce porous microstructures of actual porous materials. This paper is organized as follows. In the next section, the preliminaries of reconstructing porous microstructure considering the influence of pore size, shape and distribution-mode is first introduced and an element-based approach is then developed to generate porous models based on the statistical information observed experimentally. Section 3 is dedicated to introducing a computational scheme to evaluate the overall isotropic moduli of porous materials based on the micro-mechanics. In Section 4, both the experimental and theoretical data from practical materials are used to verify the accuracy of our model and to demonstrate the correctness of our computational scheme. Finally, the relationship between the effective moduli of the porous materials and the pore characteristics is analyzed in detail.
## 3 Results
### Influencing Factor Analysis of the Network Structure of UAs in the Context of COVID-19
#### 3.3.2 QAP Correlation Analysis  
By employing the QAP for correlation analysis encompassing 5000 random permutations, we acquired outcomes related to the evaluation of factors impacting the UA network structures in China before and after COVID-19 (Figure 5). In this framework, a significance level of \(\geq\)0.1 suggests that the influencing factors failed to achieve statistical significance, while a significance level of \(\leq\)0.1 attests that the influencing factors met the criteria for statistical significance, indicating a meaningful correlation with network structure.  
Note: In the table, areas shaded in light red signify that the results did not pass the significance level test, indicating an absence of discernible correlation in the variable matrix. Conversely, green shading indicates that the variable successfully passes the significance level test, with the depth of color indicating the magnitude of correlation, wherein darker hues signify higher correlation levels.  
Based on the findings presented in Figure 5, UAs in the primary stage of development exhibit a greater number of factors that did not achieve statistical significance in the significance level test. Consequently, these factors are deemed statistically insignificant for subsequent regression analysis. In contrast, UAs in other developmental stages displayed more pronounced results in the correlation analysis, qualifying them for inclusion in the regression analysis built upon the outcomes of the QAP correlation analysis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & **Variables** & **Description of Variables** \\ \hline \multirow{2}{*}{Economic Development Level} & Gross Domestic Product & \multirow{2}{*}{Matrix of differences in GDP} \\  & (GDP) (\(\mathcal{N}_{\text{GDP}}\)) & \\ \hline \multirow{2}{*}{Medical Facility Capacity} & Physicians per & \multirow{2}{*}{Matrix of differences in physicians per} \\  & 10,000 population (\(\mathcal{N}_{\text{MP}}\)) & \\ \hline \multirow{2}{*}{Transportation Infrastructure Capacity} & \multirow{2}{*}{Miles of city roads (\(\mathcal{N}_{\text{TM}}\))} & Matrix of differences in miles \\  & & of city roads \\ \hline \multirow{4}{*}{Spatial Proximity} & Geographic distance from city & Matrix of differences in \\  & to city (\(\mathcal{N}_{\text{S}}\)) & geographic distance \\ \cline{2-3}  & Number of cross-province & 0 for inter-province links, 1 for \\ \cline{1-1} \cline{2-3}  & links (\(\mathcal{N}_{\text{K}}\)) & cross-province links \\ \hline \multirow{2}{*}{Innovation Accessibility} & University education per & Matrix of differences in \\  & 10,000 population (\(\mathcal{N}_{\text{G}}\)) & university education per \\ \cline{1-1}  & & 10,000 population \\ \hline \hline \end{tabular}
\end{table}
Table 2: The explanatory variables influencing the network structure of UAs in the context of COVID-19.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 6 Field Test of Fifth Wushui Bridge, China  
### Introduction to Field Test  
Field tests were conducted on an approach span of the Fifth Wushui Bridge, located in Huaihua, Hunan province, China. The test span is a simply supported 40 m long bridge constructed from ten parallel T-girders and carries four lanes of traffic--Figure 8. The main strain sensors used for inferring vehicle weights, numbered 1 to 10 in the figure, are located at mid-span on the soffits of Girder Nos. B1 to B10. Strain sensor Nos. 11 to 18 are used to detect the presence, location and speed of the axles in each lane, a key requirement of a BWIM system. The latter are known as free-of-axle-detector (FAD) sensors as they replace the road surface sensors used in early BWIM systems. This test uses the KD4001 strain sensor manufactured by Yangzhoukedong Co., Ltd., Jiangsu, China. The sensor uses a full-bridge circuit consisting of an electrical resistance foil strain gauge, which is connected to a heat-treated metal material to measure strain. This type of sensor can work in a harsh environment with temperature from \(-20^{\circ}\) to \(80^{\circ}\), but with a low level of sensitivity of 1.5 \(\mu\)k. The measurement range is 0-1000 \(\mu\)k, and the bridge resistance is 350 \(\Omega\). All sensors are connected to a DC-204R collector, manufactured by Tokyo Sokki Kenkyujo Co., Ltd., Tokyo, Japan. The scanning frequency was 200 Hz. In the field test, two 2-axle rigid trucks, labelled A and B, passed the instrumented bridge repeatedly in each lane. Their characteristics are listed in Table 2.  
Figure 8: Wushui test bridge and sensor positions.  
Figure 7: Correlation coefficient for different mother wavelet functions.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 5 Numerical and Simulation Results
### Required GWs and Minimum Contentions  
Here, a desired contention level is specified for each given number of SNs. Then, the minimum required number of GWs to reach the desired contention level is determined, using the PGL algorithm, as shown in Figure 16a,b. Among the three desired average contention levels, 10, 30 and 50, the contention of 10 is a more strict requirement in 500 SNs than that of 50, which requires five GWs for 500 SNs compared to two GWs for the same number of SNs with capture and IC. On the other hand, with capture only, 15 GWs are required to get desired average contention levels of 10 with 500 SNs compared to five GWs at the same contention levels for capture and IC.  
Figure 15: The results with GWs placed by the PGL algorithm with capture and IC and capture only both show that a given number of GWs can give a predictable contentions reduction even with more SNs. However, with capture and IC, the reduction of contentions has much more than that with capture only.
# Numerical and Experimental Investigation of Guided Wave Propagation in a Multi-Wire Cable
## 2 UGW Propagation in Multi-Wire Waveguides  
The mechanical properties of multi-wire cables are more complicated than those of individual solid cylindrical rods. The most prominent features are the complex geometry of the cross-section and the contact and friction stresses between adjacent wires, which are largely caused by the tension loads during service. The preliminary understanding of UGW propagation in a multi-wire cable was used to study the wave propagation behavior of an individual wire using the analytical Pochhammer-Chree equations [26]. An infinite number of guided wave modes, which are classified by their particle displacement, might propagate through the structure. These modes in a cylindrical waveguide are composed of the longitudinal waves \(L\)(\(m\),\(n\)) with radial and axial vibration displacement, torsional waves \(T\)(\(m\),\(n\)) with circumferential vibration displacement, and flexural waves \(F\)(\(m\),\(n\)) with radial, axial, and circumferential vibration displacement, where \(m\) {0,1,2... } denotes the circumferential order and \(n\) {1,2,3... } stands for the \(n\)th root of the characteristic equation [27].  
The performance of all possible propagating wave modes in a multi-wire cable at different frequencies could be predicted by UGW dispersion curves. However, when a guided wave was excited in one of the contact wires with a single-frequency excitation signal, it is ineffective to analyze the frequency behaviors of the wave signals for a certain mode, which could be analyzed using the CAN effect. From the perspective of UGW energy transfer, the distribution of wave energy in each layer of a cable composed of multiple layers of wires with different materials could be obtained.
## 1 Introduction
The growing consumer concern about the potential negative effects of synthetic chemicals in food has led to an increasing interest in the use of natural alternatives to synthetic additives. The high antioxidant and antimicrobial properties of selected herbs make them strong candidates as natural food antioxidants and preservatives. The use of herbs or extracts from thyme, oregano and rosemary in the preservation of bread offers a flavorful alternative to synthetic additives. Research from recent years, although scarce, seems to confirm the potential of spice plants from the _Lamiaceae_ family in the preservation of bread. A series of studies conducted by [PERSON] et al. [35] have demonstrated that the addition of dried herbs such as thyme and oregano to bread recipes has the potential to inhibit the growth of both _Penicillium_ sp. and _Aspergillus_ sp. fungi. The inhibitory effect of the aromatic plants was most pronounced against _Penicillium_ sp., with thyme identified as the most effective antifungal agent among the plants tested against both fungi. While herbs may not fully replace traditional preservatives, they can certainly enrich the preservation process, providing a more wholesome option for consumers who are wary of synthetic ingredients. Moreover, herbs are a source of antioxidants and may infuse bread with distinctive tastes and scents, thereby enhancing its flavor profile. Nevertheless, it is crucial to recognize that while herbs and herbal extracts can contribute to the prolongation of bread freshness, they may not achieve the same level of preservation efficacy as synthetic additives. The chemical composition of a specific plant can vary considerably due to the presence of different chemotypes within the same species, environmental factors and the time of harvest [36]. Furthermore, the storage conditions and the composition of the bread itself also exert a significant influence on its shelf life. This research is motivated by the potential health benefits, as well as the antibacterial and antifungal properties of selected herbs, which could lead to the development of improved functional foods. The present study investigated the effect of adding dried aromatic plants from the _Lamiaceae_ family at 1% in wheat flour on the preparation of functional tortillas. The amount of aromatic plants added was determined based on the results of sensory tests. From a sensory point of view, the 1% level on a flour basis was identified as the optimal addition, as it contributed to a favorable flavor and aroma, according to the evaluators. It is hypothesized that the addition of 1% dried aromatic herbs to the wheat tortilla recipe will affect its water activity, moisture content and texture. It may also affect color, antioxidant content and total phenolic content, making tortillas a source of bioactive compounds.