esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection  
To enhance the predictive accuracy of PM2.5 concentrations, adding pollutant factors and meteorological factors as input features to the model [25] is a common method used by  
Figure 4: The framework of TF-BiLSTM-attention model.  
researchers. Considering the spatio-temporal dependence, [PERSON] et al. [26] analyzed all PM2.5 samples from 12 sites in Beijing and calculated the PM2.5 correlation coefficients between the sites. The experimental results showed that there is a strong correlation of PM2.5 concentration between sites and that adding PM2.5 from other sites as features to the input data can improve the prediction effect of the model. Within this study, we employ the Pearson correlation coefficient [25] to quantify the relationship between the PM2.5 concentration and meteorological features. The formula of the Pearson correlation coefficient is shown in Equation (15).  
\[\rho_{x,y}=\frac{n\sum_{i=1}^{n}x_{i}y_{i}-\sum_{i=1}^{n}x_{i}\sum_{i=1}^{n}y_{ i}}{\sqrt{n\sum_{i=1}^{n}x_{i}^{2}-\left(\sum_{i=1}^{n}x_{i}\right)^{2}}\sqrt{n \sum_{i=1}^{n}y_{i}^{2}-\left(\sum_{i=1}^{n}y_{i}\right)^{2}}} \tag{15}\]  
where \(x\) and \(y\) denote the input features, and \(n\) is the number of samples in the sequence.  
Figure 5 shows a heat map of the correlation coefficients between the six pollutants at site S1 itself and a total of 17 features of PM2.5 concentration at the remaining 11 sites, where S1, S2, S3, S4, etc. represent the PM2.5 concentration of each site, respectively. As can be seen in Figure 5, the correlations of the six pollutant factors at site S1 vary considerably, with the PM2.5 concentration at site S1 having the highest correlation with the PM10 concentration at its own site, and the lowest correlation with the O3 concentration at its own site. In contrast, the concentrations of PM2.5 at all 12 sites are strongly correlated with each other (\(\rho>0.7\)). The observed strong correlation indicates a substantial spatial dependence concerning PM2.5 levels among the various monitoring sites.  
Meanwhile, [PERSON] et al. [26] conducted comparative experiments using different input variables. The findings demonstrated that, for the same model, the prediction effect of using the PM2.5 concentration at this site and the PM2.5 concentration at other sites as the input variables was better than the prediction effect of the model using the pollutant factors and meteorological factors at this site as the input variables. Therefore, in this paper, only the PM2.5 concentration at this site and the PM2.5 concentration at other sites are also used as input variables for the multivariate model without considering other pollutant factors and meteorological factors.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 1 Introduction
In addition to the validation of MODIS products, this study evaluates two other models: (i) the Surface Energy Balance Algorithm for Land (SEBAL) for ET estimates, and (ii) the [21] modeling approach for GPP estimates. SEBAL estimates ET as the residual of the surface energy balance. The main component of the algorithm is the internal process of estimating the near-surface (dT) temperature gradient, which is based on the selection of endmembers that represent the extremes of thewet (cold) and dry (hot) ET spectrums [22; 23]. The modeled GPP was based on the concept of radiation use efficiency [21]. According to [24], the Brazilian semiarid region is one of Brazil's most vulnerable regions, from a social perspective to climate change. Within this scope, it is essential to explore how the replacement of natural vegetation in the Caatinga, as well as its degradation, can cause changes in the energy, water, and carbon fluxes in this region. Different studies have been conducted to estimate energy, water, and carbon fluxes between semiarid ecosystems and the atmosphere, generally using the eddy covariance technique [1; 5; 24] and remote sensing data [6; 7; 25; 26; 27]  
The present study has the main goal of evaluating different models for estimating water and carbon fluxes in areas of Caatinga with different levels of degradation (dense and sparse) in the state of Paraiba, Brazil, using orbital remote sensing data.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
## 3 Minimum Interference Channel Assignment
### Assigning a Channel Number to All Nodes
The following two more conditions may be considered: the channel number of D is less than, or equal to, 9 (\(COND_{3}\)) or that of D is greater than, or equal to, 13 (\(COND_{4}\)). However, we cannot use 13 because our channel assumption is that we only have 11 channels from 1 to 11. Hence, a channel number of 13 is an invalid number to use, so \(COND_{4}\) will be eliminated. In order to satisfy three conditions from \(COND_{1}\) to \(COND_{3}\), MICA finally assigns a channel number of 9 to node D and puts it into \(S_{D}\). The reason why MICA cannot assign a channel number of 3 to D is that \(COND_{2}\) says the channel number of D should be greater than, or equal to, 9. This procedure continues until it covers all nodes in \(S_{N}\). Consequently, all nodes except F, G, and I, in a multicast tree are located in \(S_{D}\), which means that every node, except leaf nodes, has its own channel number of its sending interface. Figure 3 represents the multicast tree where every node has its own channel number for its sending and receiving interfaces. Leaf nodes F, G, and I have only a receiving interface because they do not need to forward packets and the source node S is simply a sending interface. In Figure 3, the black-colored number means the channel number for each node's sending interface and the red-colored number represents the channel number for each node's receiving interface, like Figure 1.
# Arc Modeling and Kurtosis Detection of Fault with Arc in Power Distribution Networks
## 4 Fault Detection and Power System Simulations
### Power System Simulation  
The simulations were conducted to evaluate the fault detection method developed in the study. The test system is shown in Figure 10. It models a 22.9 kV distribution feeder from the general test system of Korean Electric Power Corporation. The feeder included a 154-22.9 kV transformer that connected the utility and feeder. The fundamental frequency and sampling frequency corresponded to 60 Hz and 6 kHz, respectively. Therefore, \(N\) is set to 100. Subsequently, \(K_{Gaussian}\) was defined as 1.5 to calculate excess kurtosis. Furthermore, \(EK_{th}\) was set to 0.4 for fault detection. Four RTUs were used to obtain phase currents and kurtosis indexes at different locations. In general, electric arcs were generated after a certain amount of time elapsed after the phase opening. Thus, in the simulation, a fault scenario was constructed in which the fault occurred 1 s after the simulation commenced and then load switching was produced for \(L6\) of the system load at 1.3 s, finally the fault arc occurred after 1.5 s.  
Figure 11 shows phase currents and excess kurtosis indices. As shown in Figure (a)a, the magnitude of the phase current decreases as the distance from an overhead feeder of power system increases. We observe that the phase current at RTU 4 is nearly zero after 1 s, as shown in Figure (b)b because of the infinite load impedance value when the fault occurs immediately behind RTU 4 as shown in Figure 10. Conversely, although the phase currents were reduced in other RTUs, it was confirmed that the phase currents did not correspond to 0. This is due to the fact that the load impedances at each RTU exhibited finite values although the load impedances changed. After 1.5 s, the shoulder phenomenon, which is a typical fault with arc, was observed in RTU 4. After 1.3 s, the phase current at RTU 3 is nearly zero because the load switching was produced for \(L6\). Figure (c)c shows excess kurtosis indices. After 1.5 s, the change in excess kurtosis index was observed when the arc occurred, and it was observed that the distances from the fault point to the RTU were inversely proportional to the values of the excess kurtosis index. Since the load switching was produced, variation of EK value was briefly observed during 0.05 s after 1.3 s. Additionally, EK was set to 0.4 for fault detection as a threshold value of excess kurtosis index.  
Figure 11: _Cont._  
Figure 10: Test system.  
Figure 12 shows fault detection performance at each RTU. Faults were detected in RTU 3 and RTU 4, which were relatively close to the fault location. However, they were not detected in RTU 1 and RTU 2. Even if it can be seen that the EK is not zero by an effect on the load switching from 1.3 s to 1.35 s, ED values calculated from all RTUs are continuously zero before the arc ignition due to inconsistency of the EK value.  
Figure 11: Phase currents and excess kurtosis indices. (**a**) Phase current. (**b**) Phase current (zoomed in x-axis). (**c**) Excess kurtosis.  
Figure 12: _Cont._
## 1 Introduction
Since the Wagner model was only suitable for impact loads before the flow separation, the vibration response of the wedge after the flow separation was not modelled. [PERSON] et al. [28] performed a decoupled study on the impact response of a 3D wedge which consisted of two stiffened plates. The hydrodynamic impact loads were solved using the 2D matched asymptotic theory, and the structural response was predicted using the finite element method. As structural response was based on the decoupled framework, the effect of the added mass on the vibration response of the wedge was not considered. [PERSON] et al. [29] also performed a coupled study on the dynamic response of a free-fall 3D wedge with the commercial code LS-DYNA. The numerical results were in good agreement with their experimental results. Due to the shallow penetration of the wedge, the vibration response after the flow separation was not presented. Although the water entry of 2D rigid or elastic wedges has been widely studied, the dynamic response of 3D wedges subject to the water impact still needs further investigation. The water surrounding the wedge will move together with the wedge during the water entry. However, to the authors' knowledge, the effect of added mass on the dynamic response has not been investigated, especially in the stage after the flow separation. Moreover, most of the previous investigations on the dynamic response of elastic wedges were carried out without considering structural damping. To the authors' knowledge, there is still no reasonable analysis on the effect of the structural damping. Therefore, a dynamic model for the 3D elastic wedge impacting on water with a constant velocity is established in the present study by taking into account the added mass, the structural damping, and the impact loads after the flow separation. Effects of the added mass and the structural damping are investigated. The 3D effect of the fluid domain is usually neglected in the studies of the ship slamming [27; 28], due to the slender features of the ship structure. This premise is also adopted in the present work and the hydrodynamic loads of the 3D wedge are calculated using the 2D method. This paper is organized as follows. In Section 2, the 2D water entry of the wedge is defined. Section 3 presents the structural dynamic equation based on the normal mode method. In Section 4, the expressions of general forces of 2D wedge sections are derived, including the generalized excitation force and general hydrodynamic coefficients. In Section 5, the dynamic model of the 3D elastic wedge impacting on water with a constant velocity is established. Section 6 presents the results and discussions, including validations of the present model, and investigations of the added mass and the structural damping effects. In Section 7, main conclusions of the present study are summarized. Appendix A presents the derivation of the semi-analytical model reported by [PERSON] et al. [7]. Appendix B shows the modal results of the stiffened plate. Appendix C presents the modal convergence study.
## 3 DA-Based Turbo Equalization  
### Structure of DA-Based Turbo Equalization  
Figure 4 depicts the procedure of DA-based turbo equalization for the signal \(r^{\alpha,m}(nT)\). Referring to Figure 4, interference cancellation and soft information-based ADFE can take advantage of the symbol probabilities \(p_{\text{in}}(s^{\alpha,m}(nT))\) and \(p_{\text{in}}(s^{\gamma,m}(nT))\), \(\gamma\
eq\alpha\) to undo the effect of residual CCI and ISI. The decoder exploits log likelihood ratios (LLRs) of coded bits \(\Lambda_{\text{in}}(c^{\alpha,m}(nT_{b}))\) to correct error bits caused by noise, the demapper and deinterleaver convert the symbol probabilities \(p_{\text{out}}(s^{\alpha,m}(nT))\) to LLRs of coded bits \(\widehat{\Lambda}_{\text{in}}(c^{\alpha,m}(nT_{b}))\), the mapper and the interleaver convert LLRs of coded bits \(\Lambda_{\text{out}}(c^{\alpha,m}(nT_{b}))\) to the symbol probabilities \(p_{\text{in}}(s^{\alpha,m}(nT))\), and the subtractor is used to ensure that only extrinsic information is passed to the decoder. The soft information exchange and interference suppression are proceeded successively and iteratively until the communication performance is stable. In the following sections, the symbol interval \(T\) and the bit interval \(T_{b}\) are omitted for concise expression.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat
#### 3.2.1 Listeria Monocytogenes  
_L. monocytogenes_ has been accounted for in a few nations, and its rate relies upon dietary patterns, cooking techniques, utilization of refrigeration, and food importation. In light of openly accessible data at the time of the cases, the halfway expense of the listeriosis outbreak seen in South Africa (_L. monocytogenes_ arrangement type 6 (ST-6) in 2017-2018 because of a colony was assessed at least USD 2.3 billion) added up to 0.82% of the Gross Domestic Product (GDP), with government reaction costing ZAR 65.5 million; in any case, the general effect figures are probably going to be lower considering a developing assemblage of better information quality [25].
# Compaction Characteristics and Mechanical Response of Gravel-Glass-Rubber Mixtures +
###### Abstract  
From a geotechnical engineering viewpoint, recycling and reuse of crushed glass and tire rubber can significantly help reduce the demand for natural resources (i.e., sand and gravel aggregates). Following an earlier study by the authors aimed at characterizing gravel-rubber mixtures (GRM), this paper focuses on the geotechnical assessment of gravel-glass-rubber mixtures (GGRM) made of recycled crushed green glass bottles and recycled granulated tire rubber. Specifically, the compaction, one-dimensional compressibility, and shear strength characteristics of GGRM prepared at 40% and 55% rubber content by volume (\(R_{\text{B}}\)) with varying glass content by volume (\(G_{\text{L}}\)) are investigated. It is found that compacted GGRM possesses high strength (i.e., friction angle \(\geq 30^{\circ}\)) and adequate compressibility, making it a suitable general and structural fill material for use in eco-friendly geotechnical applications.  
recycled green glass; recycled tire rubber; gravel; compaction; strength; energy absorption; mixtures Article
###### Abstract  
Road and rail networks provide critical support for society, yet they can be degraded by seasonal soil movements. Currently, few transport network operators monitor small-scale soil movement, but understanding the conditions contributing to infrastructure failure can improve network resilience. Persistent Scatterers Interferometry (PSI) is a remote sensing technique offering the potential for near real-time ground movement monitoring over wide areas. This study tests the use of PSI for monitoring the response of major roads, minor roads, and railways to ground movement across six study sites in England, using Sentinel 1 data in VV polarisation in ascending orbit. Some soils are more stable than others--a national soil map was used to quantify the relationships between infrastructure movement and major soil groups. Vertical movement of transport infrastructure is a function of engineering design, soil properties, and traffic loading. Roads and railways built on soil groups prone to seasonal water-logging (Ground-water Gley soils, Surface-water Gley soils, Pelosols, and Brown soils) demonstrated seasonal subsidence and heave, associated with an increased risk of infrastructure degradation. Roads and railways over Podzolic soils demonstrated relative stability. Railways on Peat soils exhibited the most extreme continual subsidence of up to 7.5 mm year\({}^{-1}\). Limitations of this study include the short observation period (~13 months, due to satellite data availability) and the regional scale of the soil map--mapping units contain multiple soil types with different ground movement potentials. Future use of a higher resolution soil map over a longer period will advance this research. Nevertheless, this study demonstrates the viability of PSI as a technique for measuring both seasonal soil-related ground movement and the associated impacts on road and rail infrastructure.  
Persistent Scatterers Interferometry; Sentinel 1; synthetic aperture radar; infrastructure monitoring; soil movement; soil compression; shrink swell; environmental risk; road; railway +
Footnote †: journal: Remote Sens.
## 3 Results and Discussion
### Inhibition of Spoilage Microorganisms in Yulu after B. subtilis Addition  
The \(\alpha\) diversity and microbial composition of the microbial community in low-salt Yulu fermented with _B. subtilis_ at the end of fermentation (BS45) were compared with those without microbial starter addition (C45). The \(\alpha\) diversity can effectively reveal the degree of microbial contamination in the environment [5; 29]. As shown in Figure 3B, the Sobs, Chao, ACE indexes significantly decreased from 291, 371, and 367 to 92, 150, and 146 after the addition of _B. subtilis_, respectively. Similarly, the Shannon index significantly decreased from 2.38 to 0.39, while the Simpson index significantly increased from 0.16 to 0.87 after _B. subtilis_ addition. These results indicated that the richness and evenness of the microbial community in Yulu were all significantly lowered by _B. subtilis_ as a result of the obvious decrease in microbial contamination. Moreover, only the abundance of _Bacillus_ significantly increased from 0.04% to 93.63% after the addition of _B. subtilis_, while all the other microorganisms were all inhibited, especially spoilage microorganisms, such as _Pseudomonas_, _Achromobacter_, _Stentrophomonas_, _Cyanobium_, _Rhodococcus_, and _Brucella_ with their abundance decreasing from 31.34%, 14.80%, 12.25%, 12.46%, 9.85%, and 4.51% to 2.62%, 0.91%, 0.85%, 0.19%, 0.54%, and 0.18%, respectively (Figure 3C). These results suggested that _B. subtilis_ was more suitable to adapt to the Yulu fermentation environment than the other microorganisms, probably due to its salt-tolerant characteristic.
## 3 Model Description
### Agent  
The vehicles are classed as autonomous agents; the vehicle population is heterogeneous (every vehicle has distinguishable characteristics). These agents apply similar characteristics to real-world motor vehicles; they have four wheels, steering angle, traction, mass, and drag. Each vehicle agent applies the following set of rules during its drive cycle, refer to Algorithm 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Variable** & **Output Type** & **Example Value** \\ \hline AgentID & Integer & \(-\)38,572 \\ xAxisPos & Float & 75.94560 \\ zAxisPos & Float & 20.1927 \\ collisions & Integer & 12 \\ topSpeed(mph) & Float & 20.0 \\ currentSpeed(mph) & Float & 18.0 \\ distanceOfTravel(meters) & Float & 13.0 \\ raycastLength & Integer & 6 \\ tractionControl & Integer & 0 \\ velocityMagnitude(BETA) & Float & 0.195808 \\ vehicleMass & Integer & 1500 \\ downforce & Float & 0.1 \\ date-time & DateTime & 18 January 2021 13:05:40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model output variables, source [23].  
```
whileModelrunningdo  Drive; ifnot_adherence==truethen  accelerate to top speed [30; 45]; else  accelerate matching road speed limit;  end if ifvehicle_ahead==truethen  match speed of that vehicle; else  continue at current speed;  end if ifat_intersection==trueANDvehicle_present==falseANDright_of_way==truethen  reduce speed and drive out of intersection; elseifat_intersection==trueANDvehicle_present==trueANDright_of_way==falsethen  halt till intersection_clear==true; elseifat_intersection==trueANDvehicle_present==falseANDright_of_way==falsethen  reduce speed and drive out of intersection; else  halt till intersection_clear==true;  end if  end if
```  
**Algorithm 1**Vehicle agent rules in pseudocode.  
The rules described in Algorithm 1 allow vehicle agents to navigate the environment and collect data. Each vehicle follows the same rules. However, the features vary and depend on the input values from Table 1. These vehicle agents are a simplification of real  
Figure 1: Workflow diagram depicting processes that the Urban Traffic Simulator undergoes during run-time.  
vehicles. Therefore, it is not expected to perfectly simulate real-world vehicles but includes the fundamental features that all vehicles retain.  
If a vehicle is not adhering to speed limits, it can increase its speed between 30 to 45 mph. If vehicle \(X\) is ahead of \(Y\), \(Y\) given the rules in Algorithm 1 should decrease speed to match vehicle \(X\)'s speed. When a vehicle arrives at an intersection, if it has the right of way, i.e., on a horizontal lane and no vehicles are at the intersection, it reduces its speed to 10 mph and drives through the intersection. If the vehicle is at the intersection and does not have the right of way, it should wait until the intersection is cleared. If the vehicle is at an intersection, it does not have the right of way, and there are no vehicles at the intersection, the vehicle is free to reduce speed to 10 mph and drive through the intersection. Lastly, all vehicles that adhere to the speed limit increase or decrease speed to match the road's speed limit [23].