esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationships between CUP Onset and C Fluxes  
The CUP onset was negatively correlated with annual C fluxes for all sites (Figure 4). Comparable correlations were observed between CUP onset and GPP, with R\({}^{2}\) values of 0.53 (\(p=0.027\)), 0.44 (\(p=0.049\)), and 0.53 (\(p=0.017\)) for SK-OA, SK-OBS, and SK-OJP, respectively. These correlations indicate that delayed CUP onset would reduce annual GPP. CUP onset showed limited potential as a proxy for annual NEP, as a significant correlation was found only at the SK-OBS site (R\({}^{2}=0.48\), \(p=0.038\)).  
Substantial differences were noted in the sensitivity of C fluxes to the CUP onset. For the SK-OA site, the regression slopes indicated that for each day of delayed CUP onset, annual GPP would decrease by 10.9 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\) (y \(=-10.9\)x \(+\) 2619.3). In contrast, the evergreen sites showed lower sensitivity to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Sites**} & \multicolumn{2}{c}{**CUP Onset (DOY)**} & \multicolumn{2}{c}{**Ta\_Spring (\(\cdot\)C)**} & \multicolumn{2}{c}{**EVI\_Spring**} \\ \hline \multirow{4}{*}{SK-OA} & Minimum & 132 & \(-\)2.5 & 0.291 \\  & Maximum & 161 & 3.8 & 0.422 \\  & Average & 145 & 1.6 & 0.331 \\  & SD & 9.0 & 2.0 & 0.051 \\ \hline \multirow{4}{*}{SK-OBS} & Minimum & 90 & \(-\)3.2 & 0.232 \\  & Maximum & 120 & 3.3 & 0.263 \\  & Average & 106 & 0.7 & 0.245 \\  & SD & 10.6 & 2.1 & 0.012 \\ \hline \multirow{4}{*}{SK-OJP} & Minimum & 95 & \(-\)2.7 & 0.225 \\  & Maximum & 124 & 3.9 & 0.254 \\ \cline{1-1}  & Average & 108 & 1.4 & 0.237 \\ \cline{1-1}  & SD & 9.7 & 2.0 & 0.011 \\ \hline \hline \end{tabular} Note: DOY and SD represent day of year and standard deviation, respectively.  
\end{table}
Table 1: Comparison of the three phenological indicators for all sites in this study.  
single-day changes in CUP onset, with annual GPP changing at rates of 2.9 (y = \(-\)2.9x + 1093.7) and 2.6 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)y\({}^{-1}\) (y = \(-\)2.6x + 859.3) for SK-OBS and SK-OJP, respectively.  
Figure 3: Relationships between spring EVI and (**a**) spring temperature (Ta_spring) for SK-OA, (**b**) carbon-uptake period (CUP) onset for SK-OA, (**c**) spring temperature (Ta_spring) SK-OBS, (**d**) CUP onset for SK-OBS, (**e**) spring temperature (Ta_spring) SK-OJP, and (**f**) CUP onset for SK-OJP. NS indicates no significant correlation. The solid and dashed lines represent regression and its 95% confidence of mean prediction.
## 3 Results and Discussion  
Bacterial Cell Viability of P. aeruginosa Under the Influence of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\)  
It is well known that tannins have antibacterial activity. This potential can be connected with the decrease in bacterial viability. Therefore, the experiments were carried out using INT test to estimate the impact of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) on the viability of _P. aeruginosa_. This colorimetric test is based on the ability of bacteria to change in INT color from yellow to pink product, whose absorbance is analyzed. As we determined previously, the minimum inhibitory concentration (MIC) of C\({}_{55}\)H\({}_{40}\)O\({}_{34}\) against the _P. aeruginosa_ was 250 uM [23]. Therefore, we used 250 uM concentration (as 1 MIC) and lower ones in these studies. Studies of _P. aeruginosa_ viability demonstrated that at a concentration of 250 uM (equal 1 MIC), the compound reduced bacterial cell viability by 70% in comparison to control (taken as 100%) (Figure 1A). In order to calculate IC\({}_{50}\) value results, they are presented as logistic functions (Figure 1B), and the designated IC\({}_{50}\) value was 78.89 \(\pm\) 4.44 uM.  
The literature reports the antimicrobial activity of extracts from _Rhus_ sp. against _P. aeruginosa_. [PERSON] et al. [27] demonstrated that _R. typhimurium_ fruit extract inhibited the growth of _P. aeruginosa_. This result was also confirmed by [PERSON] et al. [28], using a 50% ethanolic extract from the fruit of the same species. [PERSON] et al. [29] also described the antimicrobial activity of the aqueous and ethanolic extract of _Rhus coriaria_ on three strains of _P. aeruginosa_ isolated from human epithelial cells. These researchers used extracts containing multiple biologically active compounds. The results obtained in the present study are consistent with the literature data, but at the same time, indicate the strong antibacterial properties of one type of extract molecules, i.e., the tested 3,6-bis-O-di-O-galloil-1,2,4-tri\(O\)-galloil-\(\beta\)-D-glucose (C\({}_{55}\)H\({}_{40}\)O\({}_{34}\)). It should be emphasized that the antibacterial activity used in our studies is very strong. [PERSON]'s team [30] investigated the effect of tannin from a tree of the _Anacardiaceae_ family (_Myracrodrun urundeura_) on _P. aeruginosa_ and showed that the MIC was 4 mg/mL. In our studies, the MIC was 250\(\mu\)M (equal 0.3 mg/mL).
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 3 The MOTSO Model in SC
### The Ranking Strategy Algorithm
#### 3.1.1 Task Execution Duration (TED) and Ranked Task Execution Duration (RTED)  
The TED refers to the required duration for the worker to arrive at the task's location and to complete it. The TED can be computed by summing the execution duration (task duration) \(d_{t_{j}}\) with traveling duration \(TC_{ij}\), while the traveling duration can be computed by dividing the distance between the worker and the task by the speed of worker, as per the following equation:  
\[TC_{ij}=\ \left(\frac{cost\big{(}w_{i},t_{j}\big{)}}{s_{w_{i}}}\right),\ t_{j}\in T\ and\ w_{i}\in W \tag{1}\]  
Figure 3: The procedure of the multi-objective task scheduling optimization (MOTSO) model. TED = task execution duration table; TE = task entropy table; RTED = ranked TED; RTE = ranked TED; w1 = first worker; w2 = second worker; w3 = third worker; t1 = first task; t2 = second task.  
where \(cost\big{(}w_{i},t_{j}\big{)}\) is the travel cost between worker \(w_{i}\) and task \(t_{j}\), divided by the speed of the worker \(s_{w_{i}}\), where the speed of the worker parameter is application-dependent.  
\[TED\ (i,\ j)=\ d_{t_{j}}+\ TC_{ij},\ t_{j}\in T\ and\ w_{i}\in W \tag{2}\]  
where \(d_{t_{j}}\) is the duration time of the task \(t_{j}\), and \(\ TC_{ij}\) is the traveling duration spent by the worker \(w_{i}\) to arrive at the task's location \(t_{j}\).  
Practically, the TED table is represented as an \(i\times j\) matrix, where \(i\) and \(j\) indicate the number of tasks and the number of workers, respectively. The TED is computed between each worker and task using Equation (2). Then, we ranked the TED values for all of the tasks according to the least value of each row \(i\) (task) by sorting the workers into ascending order to select a suitable worker for each task, as presented in Tables 2 and 3. The reason behind sorting the workers in an ascending order is to correspond the travel cost minimization objective. Thus, the allocation of a worker to a certain task is based on a smaller TED value, which will help to improve and minimize the travel costs. In other words, the best worker to execute a specific task is the closest one to the task's location and the faster one in terms of task execution duration. Further, we need to sort the task's entropies because they affect the task execution [12]. More details about the ranking of tasks entropy are provided in the next section.
## 3 Results and Discussion
### Analysis of Thematic Data on Territorial Heritage
The records were spatialized using latitude and longitude coordinates, allowing the generation of a spatial model of the app, the user's activities, and their dynamics with the heritage buildings (Figure 7). Thus, each location value is grouped into specific areas which indicate density patterns that correlate with the heritage values (heritage quality) of the buildings represented by the MAR-MPN app. In addition, it is essential to establish that the conditions established by the territory (presence of green areas, street width, pedestrian situation) determine the grouping of each location record. The model was analysed using the ArcGIS Pro 3.4 hotspot analysis tool to determine spatial patterns and their correlation with the heritage quality buildings. Using the Gi* statistic, the values represented by the model in the dataset were made to correspond to a z-score. In this spatial model, positive z-scores are statistically significant: the higher the z-score obtained, the more intense the clustering of high values. For negative z-scores that are statistically significant, the smaller the z-score, the more intense the clustering of low values. The coefficient was applied from the measurement points, and the number of 492 hexagons demonstrated some level of optimisation achieved by the indicator (Figure 8). In this way, 52 hot spots are established, with 99% confidence in their grouping, which are \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Rout** & **MCE** & **Surface Weighted MCE** & **Number of Milestones** & **Hectares** \\ \hline A: Eastern sector of the city & 75 & 6.11 & 5 & 29.99 \\ B: Saint Lucia and surroundings & 86 & 13.17 & 8 & 2.62 \\ C: Museum of Fine Arts and surroundings & 106 & 7.87 & 10 & 32.17 \\ D: Northern sector of the city & 87 & 4.51 & 5 & 22.69 \\ E: Southern sector of the city. Civic Quarter & 155 & 19.80 & 10 & 39.83 \\ F: Central sector. Plaza de Armas and surroundings & 53 & 1.55 & 11 & 22.48 \\ G: Courts of Justice and surroundings & 126 & 42.40 & 8 & 19.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of MCE values by route. Santiago de Chile. correlated with buildings with a high heritage level, such as La Moneda Palace, Intendencia Metropolitana de Santiago Building, Las Agustinas Church, and to a lesser extent, the San Agustin Church. On the other hand, some buildings obtain polygons that are not significant due to elements such as their extension or heritage quality (Rivas Palace, Civic District Bulnes, Santiago Stock Exchange, and former hotel building Mundial).
# Regional Geomagnetic Field Modeling Based on Associated Legendre Polynomials
###### Abstract  
Global geomagnetic field models typically have low spatial resolution, whereas regional models are constrained by boundary effects and limited truncation levels. To address these limitations, this study introduces a novel regional geomagnetic anomaly field model called the regional associated Legendre polynomials magnetic model (R\(-\)ALPOLM). This model employs the associated Legendre polynomials method, which combines the QR decomposition approach and a comprehensive evaluation index formula to enhance the computational efficiency of parameter estimation. In addition, it allows for scientific and intuitive determination of the optimal truncation level of the model. The overall prediction accuracy of the model is significantly enhanced by identifying and re-predicting outliers using the exponential moving average approach. The results indicate that the degree 83 R\(-\)ALPOLM achieves a root mean square error (RMSE) of 3.21 nT. Compared to traditional models, the proposed model exhibits lower error rates, highlighting its superior efficiency and predictive accuracy. This underscores the potential value of the proposed model in both scientific research and practical applications.  
associated legendre polynomials; regional geomagnetic field modeling; boundary effect; truncation level; anomaly detection Article
# Designing a Reliable UAV Architecture Operating in a Real Environment
## 4 Collision Avoidance Algorithm
### Modifications to the Collision Avoidance Algorithm
#### 4.3.3 Multiple Obstacle Scenario  
Precise analysis of multiple obstacle scenario is outside of the scope of this article. An exemplary approach to such situation is presented in an article by [PERSON] et al. [19]. When the collision is detected with more than one object there might be 2 cases:  
1. Only one object has overlap with initial path and possible dodge manoeuvre--a single dodge manoeuvre is needed in order to avoid safe area violation.
2. Two or more objects overlap the initial path--authors propose the expansion of the collision zone for new, single virtual target.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
###### Abstract  
The safe operation of high-speed railways (HSRs) is significantly challenged by strong winds. Accurate wind speed prediction along HSRs is crucial for ensuring the safety of train operations. However, existing research primarily focuses on designing and improving data-driven models, with limited attention given to the characteristics of wind speed specific to HSR environments. To address this gap, this study analyzes the wind speed characteristics of weather stations (WSs) and railway stations (RSs) along an HSR. These characteristics are explored from multiple perspectives, including wind speed variability, amplitude, correlation, wind speed distribution, and turbulence across different time scales. Additionally, the normalized cumulative periodogram (NCP) and Bartlett's test are employed to quantify wind speed predictability. A wind speed prediction model is then developed based on predictability analysis. The findings reveal that RS wind speeds differ significantly from WS wind speeds, exhibiting higher volatility. The predictability of wind speed is influenced by the sampling interval: as the sampling time increases, the predictability and length of the predictable historical wind speed period decrease. By establishing a prediction model grounded in wind speed predictability analysis, irrelevant historical wind speed data can be excluded, improving the model's prediction accuracy. Predictability analysis thus provides a robust foundation for forecasting strong winds along HSRs, ultimately enhancing train operation safety.  
arilway line; wind characteristics; turbulence; predictability Article
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 4 Experiment Results
### IoT System Tuning  
The queueing models presented in this paper can be employed to properly configure and tune an IoT system. For instance, suppose that an IoT designer is willing to deploy occupancy counters in a smart building. Such a sensor remains inactive (sleep mode) for a certain period; right afterwards, it becomes active to count people in the covered space. IoT designers can leverage the obtained results in Section 4.1 to either configure the inactive period or to estimate the response time for a default inactive period. For example, given \(T_{\text{OFF}}=1\) min as the inactive period, \(T_{\text{ON}}=3\) s as the active period to count people and \(D=0.1\) s as the service time required to count people, IoT designers can estimate the resulting response time. Now, suppose that data flowing from IoT sources are collected via a message broker to be disseminated to interested recipients. To guarantee the freshness of the occupancy data, IoT designers can apply dropping parameters \(K\), \(\xi\), or lifetime to ensure that obsolete data are dropped. The presented methodology in Section 4.2 can be exploited to evaluate the trade-off between the resulting response time and delivery success rate, thereby guiding an IoT designer to select the most appropriate dropping mechanism. Lastly, suppose that the smart building hosts an IoT application for  
Figure 8: Response time for various classes of messages assigned with different priority parameters.  
the creation of evacuation plans in case of an emergency by leveraging occupancy data and the building's floor plans. To ensure that occupancy data flow quicker than other flows of data do, different classes with assigned priorities can be defined. The obtained results in Section 4.3 can help in this direction for evaluating the expected response times.
# Antioxidant and Antimicrobial Properties of Selected Red Seaweeds from Central Portugal
## 2 Materials and Methods
### Antimicrobial Activity
#### 2.5.2 Minimum Inhibitory Concentration (MIC) Assay  
The Minimum Inhibitory Concentration (MIC) assay is a widely common technique in pharmacological studies, which aims to test antimicrobial activity [68], and it is defined as the lowest concentration of an antimicrobial agent that prevents the visible growth of a microorganism within a defined period of time [67]. In the present work, we adapted the method of [PERSON] [69] by employing the broth dilution method in a 96-well plate to determine and compare the MIC of the aqueous seaweed extracts.  
Briefly, the stock concentration of seaweed extracts was 100 mg\(\cdot\)mL\({}^{-1}\). A preliminary screening was performed to access whether the seaweed extracts had any inhibitory activity upon _E. coli_, _B. subtilis_ and _S. cerevisiae_, by evaluating their growth in wells containing 145 \(\mu\)L of MH broth, 50 \(\mu\)L of seaweed extract and 5 \(\mu\)L of bacterial suspension (therefore corresponding to an extract concentration of 25 mg\(\cdot\)mL\({}^{-1}\)). Controls were prepared containing 195 \(\mu\)L MH broth and 5 \(\mu\)L bacterial suspension (positive control), or 200 \(\mu\)L MH broth only (negative control).  
The plate was then read at 600 nm twice (Biotek, SynergyH1), at time 0 and 24 h after incubation at 37 \({}^{\circ}\)C (_E. coli_ and _B. subtilis_) or 28 \({}^{\circ}\)C (_S. cerevisiae_). A visual assessment was performed to detect microbial growth after 24 h: the seaweed species whose extracts inhibit growth were further diluted to find the MIC. In these extracts, serial microdilutions were then performed, starting with a well containing 145 \(\upmu\)L of MH Broth and 50 \(\upmu\)L of seaweed extract to obtain a sequence of decreasing (by half) extract concentrations, ranging between 25 mg\(\cdot\)mL\({}^{-1}\) and 0.02 mg\(\cdot\)mL\({}^{-1}\) across the plate. In wells containing extract and MH Broth, 5 \(\upmu\)L of bacterial suspension was added. Again, the plate was read at 600 nm twice, at 0 h and 24 h after incubation at 37 \({}^{\circ}\)C (_E. coli_ and _B. subtilis_) or 28 \({}^{\circ}\)C (_S. cerevisiae_), and a visual assessment was also performed to determine the MIC. The MIC was considered found in the lowest concentration where the test well replicas were visually less turbid, or with a visibly smaller bacterial deposit, than those of the controls. Microbial growth was calculated according to Equation (2).  
\[\textit{Microbial Growth}\ (\%)=\frac{AbsS(t1)-AbsS(t0)}{AbsC(t1)-AbsC(t0)} \times 100 \tag{2}\]  
from where \(AbsS\) is the absorbance of the sample at 600 nm following plate inoculation (t0) and after 24 h of incubation (t1), \(AbsC\) is the absorbance of the positive control at 600 nm following plate inoculation (t0) and after 24 h of incubation (t1).
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 4 Evaluation
### Experimental Settings  
We use both testbed and simulation experiments to study the performance of the proposed edge-aided transmission. The testbed (Figure 5) contains 8\(\times\)10 sensor nodes (MSP430+CC2420), and the radio power is set to level one to form an eight-hop network. We assign three to four nodes as edge servers and set the transmission power to level two (the coverage is three times larger). Each node calculates its own transmissions and then sends the statistics to a central server via serial ports. In testbed experiments, we compare our work with the recent proposed work in [4], which considers different link features to find out the best forwarders in the process of data dissemination. We mainly study the key metrics for dissemination--transmission count, which denotes the total number of transmissions used for each packet. The number includes both transmissions from edge server and sensor nodes. Since we have additionally employed edge servers in the data transmission, the transmission count is expected to be reduced. We repeat the experiments 1000 times to obtain a stable representative result for the work.  
To further study different conditions with varying edge servers, we use the TOSSIM [37] simulator to generate sensor networks and then deploy different numbers of edge nodes to the sensor network. Each edge node covers ten sensor nodes with three hops on average, i.e., the ten nodes covered by the edge server have three hops. The data transmissions follow the framework proposed in Section 3. We tune the following parameters to comprehensively study the performance of edge-aided transmission. Each simulation with different settings is repeated 1000 times.  
* The number of edge servers deployed in the network. When there are more edge servers, there is more chance of forwarding and retransmissions from the edge servers. The overall performance is expected to be improved. When there are fewer edge servers, there is less chance of forwarding and retransmissions from the edge servers, and the overall performance is expected to be degraded;
* The number of destination nodes for each packet. We tune the number of destination nodes for each packet. When there are more destination nodes, more transmissions will be required and there will be more potential for edge-aided optimizations. Otherwise, fewer transmissions will be required and there will be less potential for edge-aided optimizations.  
With the above variations, we proceed to study two end-to-end metrics: transmission count and transmission delay.
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 2 Methodology
### UBCSAND2 Constitutive Model
#### 2.1.5 Hardening of Stress-Dilatancy  
In order to account for reducing volumetric strains with number of cycles (volumetric hardening), it is necessary to either change the stress-dilatancy equation (e.g., state-dependent dilatancy) or stiffen the plastic shear modulus. Hence, the stress-dilatancy relationship in Equation (9) is modified by the multiplication to a cyclic hardener as the following expression:  
\[\frac{d\varepsilon_{vi}^{p}}{\left|\mathrm{d}\gamma_{i}^{p}\right|}\ =\ \sin\psi_{mi}H_{c} \tag{12}\]  
\[H_{c}\ =\ \exp\left(-C_{h}\frac{\varepsilon_{vi}^{p}}{\gamma}\right) \tag{13}\]  
\[C_{h}\ =\ 8.33(D_{r})^{2}-5.0D_{r}+2.2292 \tag{14}\]  
where \(C_{h}\) is a cyclic hardening parameter that varies with relative density. From experimental data of [PERSON] and Seed [22] on crystal silica sand, and [PERSON] [23] on Fraser river sand, the value of \(C_{h}\) can be expressed as in Equation (14). Additionally, \(\gamma\) is the current shear strain.
# Early Fire Detection on Video Using LBP and Spread Ascending of Smoke
###### Abstract  
This paper proposes a methodology for early fire detection based on visual smoke characteristics such as movement, color, gray tones and dynamic texture, i.e., diverse but representative and discriminant characteristics, as well as its ascending expansion, which is sequentially processed to find the candidate smoke regions. Thus, once a region with movement is detected, the pixels inside it that are smoke color are estimated to obtain a more detailed description of the smoke candidate region. Next, to increase the system efficiency and reduce false alarms, each region is characterized using the local binary pattern, which analyzes its texture and classifies it by means of a multi-layer perceptron. Finally, the ascending expansion of the candidate region is analyzed and those smoke regions that maintain or increase their ascending growth over a time span are considered as a smoke regions, and an alarm is triggered. Evaluations were performed using two different classifiers, namely multi-Layer perceptron and the support vector machine, with a standard database smoke video. Evaluation results show that the proposed system provides fire detection accuracy of between 97.85% and 99.83%.  
smoke detection; Multi-Layer Perceptron; Artificial Neural Network; Local Binary Pattern; Support Vector Machines +
Footnote †: journal: Journal of LaTeX Templates