esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.2 MODIS Data  
The bidirectional reflectance distribution function adjustment (BRDF) product of the Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 version 6 for 2018-2020 was selected as the main driving variable for constructing the downscaling model. The BRDF includes reflectance data in four bands: red (b1), near-infrared (b2), blue (b3), and green (b4) at a resolution of 500 m. Additionally, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Near-Infrared Vegetation Index (NIRv), Optimized Soil-Adjusted Vegetation Index (OSAVI), Leaf Area Index (LAI), and Vegetation Percentage of Cover (FPAR) data of MOD15A2 with 500 m spatial resolution were selected as the important driving variables, which can represent the characteristics of the data structure of the SIF both empirically and mechanically and have been widely used in the inverse of the ecological remote sensing [41]. To ensure the consistency of spatial resolution of the above feature variables, they were resampled to 1 km, and the surface temperature (LST) data of the MOD11A2 product were introduced in this study with a resolution of 1 km to capture the physiological features of SIF more accurately [42].
## 4 Hybrid Technology
### Microbial Electrochemical Systems
Compared to MFCs, MECs can more effectively control the microbial living environment and the electrochemical parameters required for chemical reactions due to the presence of external energy, thereby improving the kinetics and thermodynamics of the reactor. Additionally, continuous electrical stimulation can provide more electrons to the microenvironment and stimulate the microbial metabolism by transferring electrons directly or indirectly to bacterial cells. The stimulated microorganisms rapidly metabolize antibiotics by secreting enzymes [140]. [PERSON] et al. studied the performance of a closed-loop anaerobic bioelectrochemical system in removing sulfamethoxazole. They found that the removal rate of sulfamethoxazole increased from 48.8% on day 80 to 68.6% on day 110. Thisindicates that the biodegradation efficiency of the antibiotic was significantly enhanced by the electrochemical system. Additionally [141], [PERSON] et al. reported that photochemical and electrochemical catalytic materials (Fe\({}_{0}\)/TiO\({}_{2}\)) effectively generated hydroxyl radicals and electrons from the bioanode under visible light, achieving the rapid degradation of tetracycline [142]. However, a single BES still has limitations, such as low electron transfer efficiency, the incomplete degradation of antibiotics, and the risk of spreading antibiotic resistance genes (ARGs); combination technology has become a research hotspot. For example, the removal rate of sulfamethoxazole by the BES-constructed wetland system increased from 71.3% to 82.4%, while also inhibiting the spread of resistance genes [143]; the BES-advanced oxidation system synergistically utilized microbial electricity to drive the Fenton reaction, achieving a tetracycline degradation rate of 90.3% while reducing energy consumption [144]; and in the BES-microalgae system, microalgae assisted the BES through adsorption and photodegradation, achieving a removal rate of over 80% for fluorobenzene within 90 h [145]. In summary, BESs demonstrate great potential in antibiotic degradation, but interdisciplinary collaboration is needed to promote their transition from laboratory to engineering applications.
## 4 Discussion on Comparison of NDT Techniques
Considering the advantages and limitations that each ultrasonic NDT technique offers, we propose a relative comparison method where the average absolute errors have been normalized according to the ultrasonic wavelengths that have been transferred inside the adhesive bond. The wavelength is an important ultrasonic parameter that describes the relationship between the ultrasonic wave velocity and the inspection frequency. The half-wavelengths are used in the ultrasonic defect detection procedures as a limit to detect the smallest defect. The average absolute error values that are normalized with respect to the wavelengths are shown in Figure 21. For each ultrasonic NDT technique, the inspection frequency and the dominant ultrasonic wave velocity have been considered (the longitudinal velocity for bulk waves, the A0 velocity for GW). The graph presented allows one to evaluate the performance of the investigated NDT techniques. In between immersion techniques and SAM, the immersion through-transmission technique has the lowest error value. The air-coupled through-transmission indicate lower error values than the guided wave (GW) inspections. GW contact-to-contact method performs better than the GW air-coupled to contact method. The investigated defects within adhesive joints might occur due to the foreign material inclusions at the manufacturing stage or the debonding caused by the fatigue in usage. Quality control of adhesive bonding must be performed both in the manufacturing line and in regular interval maintenance. Within all ultrasonic NDT techniques compared, the immersion through-transmission technique performs the best; however, in real life two-side access to the structures is very limited. On the other hand, although immersion ultrasonic techniques show a high performance, the experimental set-ups are bulky: difficult to change position and not applicable to in-situ testing. The scanning acoustic microscopy shows that the defect detection and sizing can be performed with a high precision, however, the detection systems are also bulky, and the structures inspected must be small. Moreover, where two-sided inspection is possible, air-coupled through-transmission techniques might achieve high efficiency. Finally, the guided wave inspection techniques are suitable for industrial set-ups in both manufacturing and maintenance scenarios, where the structures can be inspected in-situ. In order to evaluate bonding quality with high performance and low cost to the production/maintenance line, the structures can firstly be evaluated with guided wave inspections; then, if the anomalies are observed further, more accurate investigations might be performed using ultrasonic immersion techniques.
## 2 System Overview
### VLC Receiver  
The receiver, which is composed of the receiver front end, the signal shaping module, and the decision module, deals with the subsequent detection for further signal processing such as converting the visible light signal into an electrical signal, amplifying and filtering it to improve the signal-to-noise ratio, and obtaining the transformation of digital and analog signals. Finally, the receiver inputs the signal to the FPGA for subsequent decoding processing.  
At the receiver, the modulated light is collected by a Fresnel lens, which is conducive to realizing UWVLC with a large field of view. Then, it converts the modulated light into an electronic current signal by a PIN photodiode, followed by a simple transimpedance amplifier (TIA) [20] to convert the current signal into a voltage signal. The \(-3\) db cutoff frequency of the TIA is 5.05 MHz. In order to improve the modulation bandwidth of the VLC system, a first-order passive high-pass filter is inserted after the TIA, where the \(-3\) dB cutoff frequency is \(f_{c}\approx 1.064\) Mhz. After the filter, an amplifier circuit was designed to  
Figure 3: (**a**) LED wavelength characteristics. (**b**) Spatial distribution curve of radiation intensity.  
Figure 2: (**a**) The LED driver circuit. (**b**) Physical diagram of the transmitting circuit.  
amplify the filtered signal. The overall circuit design of the receiving part is shown in Figure 4.  
To match the TTL level standard of the IO input of the FPGA, a hysteresis comparator was designed at the last part of the VLC receiver, whose function is to convert the analog electrical signal into a digital rectangular wave signal. Compared with the open-loop comparator, the advantage of the hysteretic comparator is that it can shield the interference of the swing amplitude between the upper and lower threshold voltages. When the input signal varies between the lower threshold voltage \(V_{IL}\) and the upper threshold voltage \(V_{IH}\), it will not cause the inversion of the output signal \(U_{O}\); when the input signal is above \(V_{IH}\) or below \(V_{IL}\), the positive feedback introduced accelerates the transition of \(U_{O}\) and saturates the comparator output. The threshold voltage can be calculated according to Equations (1) and (2).  
\[V_{IH}=\frac{R_{1}}{R_{1}+R_{2}}V_{OH} \tag{1}\]  
\[V_{IL}=\frac{R_{1}}{R_{1}+R_{2}}V_{OL} \tag{2}\]  
Decoupling capacitors are added to the power supply pins of all the operational amplifiers and comparator chips in the receiver to reduce the influence of the power supply noise on the signal. The final circuit diagram of the PCB is shown in Figure 5.  
Figure 4: Receiver circuit diagram.  
Figure 5: Visible light receiving circuit physical model.
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 6 Simulation Results
### Effect of Uncertainties on Solar and Wind Generation  
The uncertainties due to the renewable energy sources affect the system security as it makes the gap between generation and demand. Therefore, in this paper, the beta distribution method is used for modeling the uncertainties of solar and wind generation [48; 49]. The solar generation data with respect to time and different scenarios are shown in Figure 15. The solar generation data considering uncertainties for three different scenarios are shown in Figure 16.  
Figure 14: Comparison of the convergence characteristic of the objective function with different algorithms  
Figure 15: Solar generation data for three different cases with time.  
It can be summarized from the graph that due to the uncertainties, the scheduled generation from the PV and WT is less and this can be met by purchasing the power from the grid based on the dynamic pricing of the grid or by paying the penalty to the consumers in the real-time market.  
The comparison of different optimization approaches with the meta-heuristic BAS algorithm is shown in Table 10.  
The comparison of the proposed method is tested with other existing techniques of the deterministic method, stochastic method, robust method, SARO method and HO method is tabulated in the Table 11. The proposed BAS algorithm yields almost the same profit as the hybrid optimization method. This shows that the proposed method can be applied to a large-scale microgrid.
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 4 NDN Forwarding with Reinforcement Learning Using the RNN
On the arrival of an Interest NACK in response to a request, the CR will first try to resend the Interest using the routing layer preferences. If retransmission is impossible, an Interest NACK is returned on all the expecting interfaces, and the RNN for this prefix returns to the initial state. To reduce the inefficiencies that could be introduced by out-of-date information and for efficient use of resources, when an RNN receives no feedback for \(T_{r}\) time units, it is deleted. As a result, an RNN will remain in the system only if it is considered active according to the above \(T_{r}\) condition. This is necessary to manage the limited resources at each CR. Furthermore, a limit could be set for the number of RNNs at a node depending on the available network resources, such that new Interests that arrive after this limit is reached will be sent using the routing layer options without any attempt for a local search.
## 3 Modified PROS with Local Search Mechanism
### Triangular-Distributed Random Orthogonal Search (TROS)  
In this section, we present our first proposed algorithm called Triangular-Distributed Random Orthogonal Search (TROS). The TROS algorithm (Algorithm 2) is presented as follows:  
```
input :nil output : the best solution vector \(\mathbf{x}^{(t)}\) found by the algorithm \(t\gets 0\);  Initialize \(\mathbf{x}^{(t)}=(x_{1},x_{2},\ldots,x_{D})\) randomly from \(\Omega=[a_{1},b_{1}]\times[a_{2},b_{2}]\times\cdots\times[a_{D},b_{D}]\); repeat  Sample a random integer \(j\in\{1,\ldots,D\}\);  Sample a random real number \(r\sim\mathcal{T}(a_{j},b_{j},x_{j}^{(t)})\) ; \(\mathbf{y}\leftarrow\mathbf{x}^{(t)}\); \(y_{j}\gets r\); if\(f(\mathbf{y})<f(\mathbf{x}^{(t)})\)then \(\mathbf{x}^{(t+1)}\leftarrow\mathbf{y}\); else \(\mathbf{x}^{(t+1)}\leftarrow\mathbf{x}^{(t)}\);  end if \(t\gets t+1\); until a termination criterion is met; return\(\mathbf{x}^{(t)}\)
```  
**Algorithm 2**Triangular-Distributed Random Orthogonal Search (TROS)  
Compared with PROS, TROS has one change in line 5 of Algorithm 1. Instead of sampling the next point of the \(j\)-th decision variable using uniform distribution, the triangular distribution \(\mathcal{T}\) is used in the TROS algorithm. The probability density function of the triangular distribution \(\mathcal{T}(a,b,c)\) is  
\[f_{\mathcal{T}}(x)=\begin{cases}\frac{2}{b-a}\frac{x-a}{c-a}&,a<x\leq c\\ \frac{2}{b-a}\frac{b-x}{b-c}&,c<x<b\\ 0&,x\leq a\text{ or }x\geq b\end{cases} \tag{2}\]  
where \(a\), \(b\) and \(c\) are the parameters of the distribution that represents the lower limit of \(x\), upper limit of \(x\) and mode of \(x\), respectively. The triangular distribution is illustrated in Figure 1.  
In each iteration of TROS, the next point is sampled using the triangular distribution with the settings \(a=a_{j},b=b_{j},c=x_{j}^{(t)}\) where \(j\in\{1,2,\ldots,D\}\) is the randomly chosen decision variable for the current iteration, \(a_{j}\) and \(b_{j}\) are the lower bound and upper bound of the \(j\)-th decision variable, and \(x_{j}^{(t)}\) is the value of the \(j\)-th decision variable of the current best solution vector. With this distribution, there is a higher chance to draw a sample that is near to the current position \(x_{j}^{(t)}\) than is far from the current position. As a result, the algorithm performs exploitation (that is, encourages local search) on the \(j\)-th decision variable.
## 1 Introduction
They found that gradient design helps improve the deformation stability of the lattice structure and increase the energy absorption capacity of the structure. [PERSON] et al. [22] established numerical crush models for four TPMS lattice structures, and the corresponding 316L stainless steel specimens were prepared using additive manufacturing. The quasi-static compression results verified the accuracy and reliability of the numerical models, and the TPMS lattice structures were further subjected to parametric analysis and multi-objective optimization. The mechanical properties were compared with those of other porous structures, and the TPMS lattice structures were found to have excellent impact resistance. [PERSON] et al. [23] proposed a method to design thickness gradient TPMS structures and compared the mechanical properties of uniform and gradient thickness titanium alloy TPMS structures, and the results showed that the thickness gradient design can reduce the fluctuation of response force and the energy absorption capacity is also better than that of uniform thickness structures. However, the relationship between different filling forms and different orientations of functionally graded TPMS structures on energy absorption has not been fully studied. Therefore, in this paper, a functionally graded strategy was proposed to enhance energy absorption. The design of functionally graded Gyroid and Diamond TPMS structures with multiple forms was characterized, and the TPMS structures were fabricated by SLM. A finite element model was first developed and then validated by compression experiment results. The validated finite element model was used to investigate the effects of uniformity and different directional gradients on the deformation and energy absorption characteristics of the Gyroid and Diamond TPMS structure.
## 2 Brief Background of USV Technology  
This section aims to give a background of the USV technology, covering its evolution, regulatory challenges, size-based classification, its key technological components, and the various applications found in the market.
## 3 Results
### PM\({}_{2.5}\) Spatial Analysis by EOF  
In order to demonstrate spatial features of the PM\({}_{2.5}\) concentration in Beijing city and the 143 stations in Northern China, an EOF analysis is applied. Due to scattering distribution of the stations, the data were interpolated to a gridded map with a resolution of 0.75\({}^{\circ}\)\(\times\) 0.75\({}^{\circ}\). Figure 2 shows the first and second modes of EOFs and PCs. The first EOF shows a PM\({}_{2.5}\) concentration core in Hebei province to the southwest of Beijing city, accounting for 50.2% of total variance. According to the PC1 sequence, this PM\({}_{2.5}\) core shows a reasonable annual cycle, reaching its maximum in winter seasons. EOF2 shows a south-to-north dipole mode, accounting for 9% of total variance. This contrasting pattern becomes prominent in winter seasons, when the overall PM\({}_{2.5}\) concentration of the area is high.  
Figure 1: PM\({}_{2.5}\) original sequence and decomposed IMFs. IMFs 1–8 are the eight intrinsic modal functions, corresponding to high to low frequencies. Among them, the IMF-1,2 stand for daily to weekly variability in a time scale of 4.9 to 9.9 days, the IMF-3.4 for monthly variability (18.1 to 39.4 days), the IMF-5,6 for intra-seasonal to seasonal variability, the IMF-7,8 for annual cycles, and the IMF-9 (r) is the trend.  
Figure 3 shows the time-lag correlation coefficients of Beijing and the surrounding 143 stations. The first, second and third rows show, respectively, the spatial distribution of correlation coefficient of PM\({}_{2.5}\) concentrations between Beijing and the surrounding areas with a 6, 12 and 24 h time lag. The left to right panels stand for low, medium and high PM\({}_{2.5}\) concentration scenarios. For low-concentration scenarios, we see that the PM\({}_{2.5}\) of Beijing is transferred from the northwest area (1 st column). It indicates that the low concentration of Beijing PM\({}_{2.5}\) is advected by the north wind [28]. In contrast, for the high-concentration scenario in winter seasons, the PM\({}_{2.5}\) of Beijing outbreaks locally within Hebei province (third column).  
Figure 3: The time-lagged correlation coefficient map between PM\({}_{2.5}\) in Beijing and other areas in North China. The first, second and third columns, respectively, show the spatial distribution of time-lag correlation coefficient of PM\({}_{2.5}\) concentration in Beijing when the daily mean value is low, medium and high. From top to bottom, they show the correlation between Beijing and the surrounding areas in the first 6, 12 and 24 h, respectively. The black dot area indicates that the correlation has passed the 99% significance test, which was calculated by \(t\)-test and \(p\)-value.  
Figure 2: The EOF analysis of PM\({}_{2.5}\) concentration in Northern China. The left column depicts the first and second modes of EOF, and the right column depicts the corresponding PC sequences.
# Properties and Model of Pore-Scale Methane Displacing Water in Hydrate-Bearing Sediments
## 2 Geometric Model Description  
### Random Reconstruction Algorithm of Pore Structure  
In pore-filled and particle-cemented hydrate deposits, the pores between rock particles constitute the main fluid transport space. We enhanced the random distribution algorithm in MATLAB by utilizing the Mersenne rotation algorithm [24]. This was achieved by generating random numbers uniformly distributed in a rectangular range using the rand function [25]. We input basic information such as pre-studied porosity, diameter of the matrix circle, and width of the throat into the program. Then, we generate a circular matrix, with the area between the circular media serving as the flow channel. It is worth mentioning that the pore structure generated in this manner is unique. We compare the multiple geometric models generated to select the model for further study. We obtained a series of two-dimensional random coordinates based on the specified particle radius and particle density. Circular particles that can overlap and be distributed are arranged to characterize the nonhomogeneous pore structure of the hydrate sedimentary layer. The voids between the units are represented by the circular, overlapping portions, symbolizing the rock particles, while the area between the circles represents the pore medium. The pore medium was generated by entering the circular diameter (100 \(\upmu\)m) and porosity, followed by circular position adjustment and overlapping circular modification. By determining the maximum and minimum widths of the throat, we can assess if they meet the requirements of the proposed model. The model will be output in Drawing Exchange Format in AutoCAD 2014 software, and then a two-dimensional pore medium model will be constructed in COMSOL. The complete processing flow is shown in Figure 2.
# The Development of Current Collection in Micro-Tubular Solid Oxide Fuel Cells--A Review
## 6 Current Collector Shape and Structure
### Brush Type  
Brush-type interconnects are structured wire forms that are inserted into the bore of the inner electrode of a tubular cell. Patents published by AMI reveal an internal interconnect with the option for increased functionality when doped with a catalyst [129]. Nevertheless, no experimental results or analysis have been published in the literature. The design has an ordered, axially repeating floral-like cross-section. The brush loops are attached to a central core wire which emerges from the tube end for interconnection. Figure 13a,b show the AMI brush type current collector design with a labelled schematic adapted from AMI [129] shown in (c). The electron flow path is in the radial direction and is noncircumferential, as in a mesh, as the shortest path is from the electrode-interconnect joint of the loop tributary to the central core, which acts as a highway for the electrons. Increasing the number of loops in a given length will also reduce current conduction pathways in the axial dimension. Coiled wires are reported in the patent for cathode current collection and can be an extension of the anode wire from the interior or joined to a different wire of a different material.  
Materials inside the tube bore such as brushes, coil meshes and foam types will certainly impact on the fluid flow, causing turbulence and better mixing at the expense of an increase in pressure drop [130]. The aforementioned will likely enhance heat transfer within the cell, primarily from convection but also conduction when contacted to the wall [131]. An increase in radial velocity is also likely, improving mass transport through the cell. A study by [PERSON] et al. showed an increase of 41% in electrochemical performance from a polymer electrolyte membrane (PEM) fuel cell with a coil insert inside the fuel channels when compared to an empty channel [132]. They used a combined experimental and modelling approach and attributed the electrochemical improvement to an increase in tangential fluid velocity that resulted in better transport of reactant from the mainstream to the reaction zone.
# An Overview of Intelligent Transportation Systems in Europe
## 5 Key Features of ITSs
### Benefits of ITSs
#### 5.1.5 Public Health Benefits  
Congested transportation not only wastes time, but also puts people at risk due to increased exposure to hazardous emissions. Traffic congestion increases the release of fine particulate matter (PM2.5), which can affect cardiovascular and respiratory systems [56].  
Cities can improve air quality and promote healthier urban environments by reducing congestion through ITSs [57].  
ITSs have played major roles in transforming transport networks in Europe. From reducing environmental impact and increasing road safety to improving economic efficiency and public health, ITSs represent integrated solutions to current and future challenges in urban and regional mobility.