esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stationary Sensor Network  
### Low Power Persistent Sensing  
Reliable communication with mobile MAV agents demands a stationary wireless network, which routes information to and from MAV agents, while serving as a persistent sensing platform to process information about its surrounding environment. While MAV agents are deployed for short-term mission-specific purposes, this stationary network is expected to have deployment lifetimes on the order of days to months. As such, low power consumption is key for maximizing battery and deployment lifetime. Compared to the requirements imposed by mobile MAV agents, the data throughput requirements of stationary network nodes are expected to be low, generating packets on the order of seconds to minutes. For example, in mission critical settings the stationary network would be responsible for environmental-sensing tasks such as proximity sensing or footstep detection.
###### Abstract  
Nitrogen-containing wastewater is an important issue in optoelectronic and semiconductor industries. Wastewater containing nitrogen compounds such as ammonium, monoethanolamine (MEA), and tetramethylammonium hydroxide (TMAH) must be properly treated due to concerns about health and environmental effects. MnCe-GAC (granular activated carbon) processes were developed in this study for the treatment of TMAH-contaminated wastewater in high-tech industries. The MnCe-GAC processes could effectively remove ammonium, MEA, and TMAH from aqueous solutions. The removal efficiencies of ammonium and MEA by these processes were better than observed for TMAH. Parameters affecting TMAH removal such as type of process, type of wastewater (synthetic or real), pH, salts, and t-butanol were investigated. In general, removal efficiencies of TMAH by various processes were in the following order: MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) > MnCe-GAC/O\({}_{3}\) > MnCe-GAC/H\({}_{2}\)O\({}_{2}\) > MnCe-GAC > GAC. The negative effect of sulfate and nitrate on pollutant removal might be due to the salting-out effect. Based on t-butanol experiments, the main degradation mechanisms of TMAH by the MnCe-GAC/O\({}_{3}\)/H\({}_{2}\)O\({}_{2}\) process likely involved hydroxyl radicals. The process proposed in this study could be an effective alternative method for the treatment of high-tech industrial wastewater to meet the new TMAH discharge limit.  
TMAH; MEA; MnCe-GAC; high-tech; wastewater Article Assessment of an MnCe-GAC Treatment Process for Tetramethylammonium-Contaminated Wastewater from Optoelectronic Industries
## 3 Speech-to-Text Transcriptor
### Database of Alphabet Models
#### 3.2.3 Angular Conversion  
For the inverse kinematics, \(\theta_{1}\) and \(\theta_{2}\) are assumed to be in radians. However, the angular position of the Bioloid's servomechanisms is set in units within the range of 0-1024 [29; 35]. In order to properly apply the kinematic models to the Bioloid's servomechanisms, an angular conversion from radians to the 0-1024 range was performed.  
For this purpose, the first step was to track the valid angular positions of the servomechanisms for each of the Bioloid's arms and to identify their respective reference axes for \(\theta_{1}\) and \(\theta_{2}\) (the axes associated with zero radians). In Figure 9, the angular positions within the 0-1024 range for the servomechanisms of both arms and their reference axes are presented.  
Figure 9: Positions of the Bioloid’s servomechanisms and reference axes.  
A linear regression approach was considered to perform the angular conversion of the servomechanisms' axes from the radians domain to the 0-1024 range. The following linear equations were derived for each arm's servomechanisms, where \(V_{1}\) and \(V_{2}\) are the angular positions within the 0-1024 range, and \(\theta_{1}\) and \(\theta_{2}\) are the angular positions in radians:  
(a) For the right arm:  
\[\text{Dynamixel 3 (dyn3)}\to V_{1}=-190.985\times\theta_{1}+500.\] (10) \[\text{Dynamixel 5 (dyn5)}\to V_{2}=-190.985\times\theta_{2}+500.\] (11) (b) For the left arm:  
\[\text{Dynamixel 4 (dyn4)}\to V_{1}=-190.985\times\theta_{1}+1100. \tag{12}\] \[\text{Dynamixel 6 (dyn6)}\to V_{2}=-190.985\times\theta_{2}+500. \tag{13}\]  
Figure 10: Pseudo-code of the word definition scheme, adjustment of radius, and execution of kinematics for both Bioloidý arms.
## 3 Hydrological Applications
### Micro-topography and latent heat flux  
Topography plays an important role in the distribution and flux of water and energy within the natural landscape. Surface runoff, evaporation and infiltration are hydrologic processes that take place at the ground-atmosphere interface. Quantitative assessment of these processes depends on topographic configuration of the landscape, which is one of several controlling boundary conditions.  
Wetness index (WI) provides a description of the spatial distribution of the soil moisture using topographic information. WI is computed as,  
\[WI=Ln\bigg{(}\frac{A}{S}\bigg{)} \tag{4}\]  
where \(A\) and \(S\) are the specific drainage area (flow accumulation) and slope, respectively.  
As specific drainage area increases and gradient decreases, _WI_ and soil moisture content increase. Wetness index takes into account both a local slope geometry and site location in the landscape, combining data on gradient and specific drainage area. This can lead to higher correlations of soil moisture with _WI,_ hence evapotranspiration, than with specific drainage area and gradient.  
Figure 7: Percent impervious surface area of South Florida from MODIS data.  
Wetness index controls flow accumulation, soil moisture, distribution of saturation zones, depth of water table, evapotranspiration, thickness of soil horizons, organic matter, pH, silt and sand content, plant cover distribution ([PERSON] _et al._, 1995; [PERSON], 2000).  
A study to understand the relationship between latent heat flux and microtopography (1m resolution) of a wheat field was studied using remote sensing-based latent heat flux. The latent heat flux estimation technique is based on the surface energy balance approach using remotely-sensed data ([PERSON], 1998). The detailed procedure on estimating grid-based latent heat flux for the study area is shown in [PERSON] (2005) and [PERSON] (2006). The relationship between WI (Figure 8) and latent heat flux for a wheat field is strong, since the topography influences the flux and distribution of water. Grids with higher values of WI are areas receiving most of the flow (higher flow accumulation) and lower gradient. These areas have higher soil moisture, hence higher rate of evaporation, than areas with lower values of WI. It is also indicated that when water is a limiting factor, plants on higher WI areas grow well with good canopy cover than plants in other zones of the field. This increases the transpiration (vegetation latent heat) of the crops. Figure 9 shows the scattergram of WI vs. latent heat. The latent heat seems to increase at higher rate at lower values of WI than at higher values of WI. This is attributed to the limited available water for evaporation proportional to WI.  
Figure 8: Wetness index of a wheat field in North Dakota as mapped from microtopography data.
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
###### Abstract
Most of the remaining studies have not used cost minimization as an objective function or energy management system. A limited number of studies used cost minimization as an objective function or energy management system but with different optimization algorithms [1; 8; 9; 11; 12; 13; 15; 16; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. This paper presents cost minimization as an objective function. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy management system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy management system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy management system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy management system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy management system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for the energy storage system in a hybrid energy system. The optimization of the energy storage system in a hybrid energy system is based on the fact that the PSO algorithm is not optimal for function by finding both optimum PV and BESS sizes and proposes a new optimal energy management method for a grid-connected MG. It is applied to a grid-connected microgrid that consists of a PV system with battery storage. MG is allowed to import energy from the grid with penalty. Thus, by allowing a limited amount of energy to be taken from the grid, it provides a more optimum structure by minimizing the effects of possible instantaneous high power demands. This paper focuses on determining the optimum PV and BESS sizes when the MG supplies energy as much as possible to its loads. The purpose is to create self-sufficient MG with limited grid support by considering cost minimization and defining optimum BESS and PV sizes. Studies are carried out for two different scenarios. In addition, the proposed energy management system with a PSO-based method is compared with GA, which is a well-known optimization algorithms. The results show that the proposed algorithm can achieve optimum PV and BESS size with minimum cost by using the new energy management method with a PSO algorithm. The proposed energy management method provides more flexibility to system designers for various system constraints. This can be accomplished by its configurable parameters. Thus, the new energy management method with PSO can be applied for various systems.
# Ten Years of TerraSAR-X--Scientific Results
## 1 Introduction  
We commemorated the 10 th anniversary of the TerraSAR-X mission on June 2017. TerraSAR-X is a German SAR satellite operating in X-Band. Its twin satellite TanDEM-X was launched three years later in June 2010. Both satellites serve the TerraSAR-X and the TanDEM-X missions and have provided images of superb quality since then. For the TerraSAR-X mission just one of the satellites is employed while the TanDEM-X mission always supplies a simultaneously acquired image pair, serving the goal of a global digital elevation model. The satellites were developed in the scope of a public-private partnership between the German Aerospace Center (DLR) and Airbus Defense and Space.  
This special issue addresses the scientific utilization of data acquired in the course of the TerraSAR-X mission.
## 5 Performance Evaluation  
In this section, we present the performance of OSCAR. We compare the performance of OSCAR with Orchestra using metrics that we will describe in this section. To evaluate our proposed solution, we have developed and considered two different scenarios:  
* The first scenario is to modify the position of the nodes to see the impact that the location has on performance.
* The second scenario aims to measure the impact of the traffic load on the performance of OSCAR.  
The following results were carried out on a test bench. The performance of the different metrics in a network consisting of 10 nodes and a root are shown below.
# Low-Cost Biochar Adsorbents for Water Purification Including Microplastics Removal
## 3 Results
### Sorption Experiments
#### 3.3.2 Cation Exchange Capacity (CEC)  
The cation exchange capacity (CEC) was determined using six cations, but the quantity of released phosphorus was also measured as it is one of the leading causes of eutrophication in natural waters. The quantities of extracted cations in mg/g are presented in Figure 5. The total quantity of exchangeable cations on spruce bark is approximately twice that of the pine bark. Still, of the exchangeable cations, only the amount of exchangeable calcium is substantially greater for spruce. The exchangeable Ca\({}^{2+}\) result correlates with the much higher ash content of the spruce (12-15 wt-% spruce AC vs. 5-6 wt-% pine AC), as calcium oxides form a significant fraction of the ash components present in wood-based biochar [31]. XRF measurements also confirmed a clear difference between Ca\({}^{2+}\) contents of the biomasses (data not shown). The CECs, corresponding to the relative quantities of charged surface sites, are greater for pine bark ACs (16-24 mmol/kg for pine, 9-11 mmol/kg for spruce) as these ACs had a relatively larger surface area compared with spruce ACs and, therefore, more surface groups for cations adsorption. The greater CEC of pine bark is due to the higher surface areas of the ACs.  
Figure 5: The amounts of exchangeable cations in the activated bark biochars.  
A comparison of the individual cations showed K and Na concentrations in the same range for both biomasses. The levels of Mg, Al, and Mn were slightly higher with pine bark, whereas P concentration was higher for spruce. All Fe concentrations were below the detection limit of 0.015 mg/L. These results are consistent with other CEC studies performed for different biochar adsorbents (e.g., [32]).
# Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning
## 2 Model Validation
### Statistical Validation in Automotive Domain  
The review of [PERSON] et al. [1] analyses more than 30 validation frameworks in the automotive domain from 1990 to 2020. It concludes that the analysed frameworks agree with the validation concept of [PERSON]. Since validation in the automotive domain only uses conventional tolerance-based metrics, four key insufficiencies arise:  
_The negligence of uncertainty:_ Conventional validation assumes that the data utilised in the process are valid. In practice, the data used for validation are not fully known, or may even be completely unknown. It is the law of nature that there will be a discrepancy. If valid input and output data, parameters and boundary conditions are assumed, errors are neglected. These uncertainties are underestimated and may become uncontrollably large when they accumulate ([21], p. 385).  
_Binary, low-information validation result:_ The conventional validation is binary ([38], p. 6). A model can be valid or invalid. This can never be proven to be true, because the philosophy of science tells us that absolute evidence is impossible. Validation should ask the question of the intended purpose and consider the degree of correctness [28; 37].  
_Extrapolation capability of model reliability:_ Since an extrapolation of validity is not anticipated in conventional validation, almost no knowledge of the M+S process is gained beforehand, which makes extrapolation almost impossible. Conclusions outside the scope of the measured validation range are mere suppositions [14]. Conventional validation must re-validate the model if the conditions or the use case changes.  
_Application domain size:_ As validation is time-consuming and expensive, a simulation model can often only be validated by measurements of a few points. Since conventional validation does not foresee extrapolation, the application domain is restricted to the validation measurements. To close this gap, statistical validation allows extrapolation when information about the uncertainty of former validations is accessible [39].  
Those four insufficiencies prevent validation from meeting the new key requirements. As analysed in [PERSON] et al. [1], there are attempts to address these problems by integrating statistical methods. They conclude that the problem has not been solved because the analysed authors did not consistently integrate statistical approaches. To meet the new key requirements, [PERSON] et al. [1] propose a consistent statistical validation method, which has a high potential to solve those four key insufficiencies [1; 4].
## 1 Review of Flow Control for Gust Load Alleviation
###### Abstract  
Effective control of aerodynamic loads, such as maneuvering load and gust load, allows for reduced structural weight and therefore greater aerodynamic efficiency. After a basic introduction in the types of gusts and the current gust load control strategies for aircraft, we outline the conventional gust load alleviation techniques using trailing-edge flaps and spoilers. As these devices also function as high-lift devices or inflight speed brakes, they are often too heavy for high-frequency activations such as control surfaces. Non-conventional active control devices via fluidic actuators have attracted some attention recently from researchers to explore more effective gust load alleviation techniques against traditional flaps for future aircraft design. Research progress of flow control using fluidic actuators, including surface jet blowing and circulation control (CC) for gust load alleviation, is reviewed in detail here. Their load control capabilities in terms of lift force modulations are outlined and compared. Also reviewed are the flow control performances of these fluidic actuators under gust conditions. Experiments and numerical efforts indicated that both CC and surface jet blowing demonstrate fast response characteristics, capable for timely adaptive gust load controls.  
gust load alleviation; active flow control; blowing jet control; circulation control
## 4 Health Benefits of Blackcurrant Anthocyanins and Related Molecular Mechanisms
### Antioxidant and Anti-Inflammatory Activities  
BCA was widely studied for its antioxidant and anti-inflammatory activities. BCA was shown to alleviate the reactive oxidative species (ROS) production induced by both menadione and H2O2 in SH-SYSY human neuroblastoma cells [54, 55]. In addition, a clinical trial showed that consumption of BCA prior to exercise facilitated recovery from exercise-induced oxidative stress [56].  
BCA exerts cellular antioxidant activity, at least partly, through activating the nuclear factor erythroid 2-related factor 2 (NRF2) pathway. As a transcription factor, NRF2 regulates cellular resistance to oxidants in addition to drug metabolism. It controls the basal and induced expression of a group of antioxidant response element (ARE)-dependent genes, including several drug-metabolizing enzymes (e.g., glutathione S-transferase, NAD(P)H, and quinone oxidoreductase) and a group of antioxidant defense genes (e.g., heme oxygenase 1) [57]. The elevated expressions of NRF2 by BCA treatment was observed in rats with diethylnitrosamine (DENA)-initiated hepatocarcinoma [58]. Another study that used bone marrow-derived macrophages (BMM) from NRF2\({}^{+/+}\) mice and NRF2\({}^{-/-}\) mice showed that BCA significantly decreased the elevated cellular ROS level in lipopolysaccharideaccharides (LPS)-stimulated NRF2\({}^{+/+}\) BMM but not NRF2\({}^{-/-}\) BMM [59]. This study illustrated the critical role NRF2 plays in the antioxidant capacity of BCA. Besides the antioxidant activity, BCA also exerted an anti-inflammatory effect in various models. Pretreatment with BCA or cyanidin-3-O-glucose inhibited the LPS-induced secretion of interleukin-6 by U937, a human monoblastic leukemia cell line [15]. A similar inhibition effect of BCA on LPS-stimulated cytokine secretion was also observed in THP-1 cells, another human monocytic cell line [60]. A commercially available BCA product attenuated ovalbumin-induced inflammation in a mouse model of acute allergic lung inflammation [61]. Another commercialized BCA product prevented inflammation in diet-induced obese mice, especially macrophage infiltration in the adipose tissue [62].  
The widely studied anti-inflammation effect of BCA is mostly contributed to its suppression of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-\(\kappa\)B) pathway. NF-\(\kappa\)B, as a transcription factor, regulates many genes involved in inflammation and immune responses. These include chemokines, pro-inflammatory cytokines, adhesion molecules, and inducible enzymes (e.g., inducible nitric oxide synthase (iNOS) and cyclooxygenase-2) [63]. BCA attenuated LPS-induced NF-\(\kappa\)B signaling in multiple macrophage cell lines [59, 60]. A recent study showed that consumption of purified anthocyanins isolated from bilberries and blackcurrants inhibited the expressions of proinflammatory-matory genes related to the NF-\(\kappa\)B pathway in whole blood samples from human subjects with metabolic syndrome [64].
## 2 Proposed Model
### One-Dimensional Energy Band Structure of GNR  
In Appendix A, the basic structure and two-dimensional energy dispersion of a single layer of graphene have been explained. In this section, to reduce the computational complexity, a one-dimensional band structure is proposed for GNR, and by some modification is used for graphene-based channels. Figure 1 shows the schematic of GNR for Na = 15, where Na is the number of dimer lines of the lattice. In order to improve the computation cost, we consider an elementary cell containing 16 atoms, repeating along the width of graphene, by applying only the nearest neighbor approximation [19] among the _pz_ orbitals [20], reference [21] as shown in Figure 1. Additionally, similar to [15], by 2 \(\times\) 2 coupling matrices, the Hamiltonian matrix has been constructed for the elementary cell. By assuming the graphene width is large, the Hamiltonian can be further simplified as:  
\[H=\left[\begin{array}{cccc}\alpha&\beta_{1}&&\\ \beta_{1}^{t}&\alpha&\beta_{2}&\\ &\beta_{2}^{t}&\alpha&\\ &\ddots&\ddots&\ddots\end{array}\right] \tag{1}\]  
where \(\alpha\), \(\beta_{1}\), and \(\beta\) 2 are all 2 \(\times\) 2 matrices given by \[\alpha=\left[\begin{array}{cc}0&t_{c}\\ t_{c}&0\end{array}\right]\qquad\beta_{1}\,=\left[\begin{array}{cc}0&0\\ t_{y}^{t}&0\end{array}\right]\quad\beta_{2}=\left[\begin{array}{cc}0&0\\ t_{y}&0\end{array}\right] \tag{2}\]  
where \(t_{c}\) is the nearest neighbor coupling energy and \(t_{y}=t_{c}+t_{c}e^{ik_{y}\sqrt{3}t_{c-c}}\), and \(k_{y}\) is quantized according to [22] as:  
\[k_{y}=(\frac{2\pi}{3\sqrt{3}a_{c-c}}+\frac{2\pi n}{2w+\sqrt{3}a_{c-c}})\pm \frac{2\pi}{3\sqrt{3}a_{c-c}} \tag{3}\]  
where \(W\) is the width of graphene and n is an integer. The last term accounts for the \(K\) and \(\Gamma\), Dirac points, where the \(+/-\) is used when \(n\) is even/odd, respectively.