esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cyclic Voltammetric (CV) Experiments  
The cyclic voltammetry measurements were performed at room temperature in hydrophilic and lipophilic extracts mixed with 0.2 M sodium acetate-acetic buffer (pH 4.5) at the ratio of 1:1 (\(v/v\)) as described recently [14]. The applied electrochemical system was based on (1) a 3 mm diameter glassy carbon working electrode (BAS MF-2012), (2) an Ag/AgCl electrode as a reference one, and (3) a platinum electrode as a counter electrode. Exactly 100 uL of the extract (100 mg/mL) and 100 uL of buffer were mixed in the voltammetric apparatus cell. The cyclic voltammograms were recorded by scanning the potential from \(-\)100 to +1200 mV at a scanning rate of 100 mV s\({}^{-1}\). A potentiostat/galvanostat G 750 (Gamry Ins., Warminster, PA, USA) was used for voltammetric experiments. The total charge below the anodic wave curve of the CV measurements. voltammogram reflecting the area under the curve (AUC) was used to calculate the antioxidant capacity expressed as umol Trolox/g. To this end, the cyclic voltammograms of the 80% methanol and hexane/methanol solutions of Trolox within the concentration range of 0.10-2.5 mM were recorded. The total antioxidant/reducing capacity was calculated as total values provided for hydrophilic and lipophilic fractions.
## 2 Materials and Methods
Kruskal-Wallis one-way analysis of variance (ANOVA) and [PERSON]'s multiple comparison post-hoc test were used to assess differences in AA concentrations across the six mineral water groups. Statistical significance was assumed at probability \(<\)0.05. Projection to latent structures by means of partial least squares (PLS) analysis was used to assess the influence of the different variables on the AA concentration. PLS is the regression extension of the principal component analysis (PCA), which constructs new predictor variables, known as components, as linear combinations of the original predictor variables. The PLS analysis is suitable to model a response variable when the predictor variables are highly correlated or even collinear. Before the analysis, data were pre-treated by auto-centering and unit variance scaling. The software Graph Pad Prism Version 8.0.0 and Simca-P 11.0 package (Umetrics AB, Umea, Sweden) were used. To simulate a possible effect of the agitation that samples receive during transportation on the AA concentrations in the samples, we have analysed a number of samples of natural and sparkling water, before and after controlled shaking for five days. The samples were placed in a shaker at 21 \({}^{\circ}\)C, with 35 agitations per minute. They were analysed before and after this treatment. To estimate human exposure to AA, we used data provided by the former National Research Institute for Food and Nutrition (INRAN) [27] on bottled water consumption by the Italian population. The average daily dose of AA was calculated by multiplying the maximum concentrations of AA found in our samples by the corresponding daily consumption divided by the reference body weight. For risk assessment, we applied the European Food Safety Authority (EFSA)'s margin of exposure (MOE) approach recommended by the Scientific Committee [28], that considers safety concern arising from the presence in food of substances which are both genotoxic and carcinogenic [29]. The MOE is the ratio of the no-observed-adverse-effect level (NOAEL) or the lower 95% confidence limit of the benchmark dose (BMDL) (29,30), to the theoretical or estimated dose or concentration of human exposure. In this study, the MOE calculation considered a BMDL of 56 mg/kg BW per day, corresponding to a 10% increase in cancer incidence, as in experimental studies [30]. To simulate a worst-case scenario, we considered a daily consumption of bottled water equal to the 99 th percentile for consumers only (1.85 L/day of bottled water), the highest concentration of AA found in this study and the conservative value for body weight of 60 kg.
###### Abstract  
This paper provides an overview of existing theories on various modulation strategies for current-source inverters (CSI), particularly focusing on space vector modulation (SVM). The emphasis is on the development of detailed simulation models that improve understanding and allow practical application. Three important modulators are analyzed: voltage-source inverter (VSI)-derived CSI SVM modulator, direct CSI SVM modulator, and direct duty ratio CSI PWM modulator (DDPWM). These models are important for researchers and practicing engineers as they allow simulation, modification and better understanding of CSIs. This paper begins with a theoretical overview of the functionality of CSIs and presents the modulation techniques needed to develop simulation models. These modulation techniques use modular components to create complete simulation models. Application examples are provided to use the correct/valid parameters such that the operation/waveforms can be compared with the theory. Integrating established mathematical models with effective simulation tools enhances the understanding and application of CSI modulators. This method not only makes it easier to employ these CSIs instead of conventional inverter systems, but it also increases the possibility of power electronics advancements by creating better and more reliable systems.  
current-source inverter; space vector pulse-width modulation; CSI-emulated CSI; direct duty CSI PWM Article
## 2 Materials and Methods
### Radiation Shielding Properties Using MCNP-5 Code  
Monte Carlo code with continuous-energy nuclear and atomic data libraries was applied to simulate the studied samples' radiation shielding properties. The MCNP nuclear database's main sources are ENDF, ACTI, ENDL, ACTI, and T-16 files [22]. An input file was formatted to estimate the simulated average track lengths of the incoming photons in the studied glasses. This input file contained all the information about the glass samples (density, chemical composition, and dimensions), detector, source, and geometry. The geometry used in the present study is described in Figure 1, where the studied samples were placed between the radioisotopes and the F4 tally detector [23; 24; 25]. In the present study, various sources such as Th-228 (E = 0.24 MeV), Rn-222 (E = 0.51 MeV), Cs-137 (E = 0.66 MeV), Co-60 (E = 1.17 and 1.33 MeV), and Eu-152 (E = 1.40 MeV) were used. Photons emitted from the source were collimated using a lead collimator.  
After that, the shielding parameters such as linear and mass attenuation coefficients (LAC/MAC), half-value layer (HVL), transmission factor (TF), dose rate, and radiation protection efficiency (RPE) were determined using the simulated data with the help of the following equations.  
\[LAC\ \left(\text{cm}^{-1}\right)=\frac{1}{x}\ln(\frac{I_{o}}{I}) \tag{11}\]  
\[\mu_{m}=\frac{LAC\ \left(\text{cm}^{-1}\right)}{\rho\ \left(\text{g}\ \text{cm}^{-3}\right)} \tag{12}\]  
Figure 1: The simulation geometry setting.  
\[TF=\frac{I}{I_{o}}=\exp(-LAC*x) \tag{13}\]  
\[HVL=\frac{\ln(2)}{LAC\ (\text{cm}^{-1})} \tag{14}\]  
\[MFP=\frac{1}{LAC\ (\text{cm}^{-1})} \tag{15}\]  
where \(I_{o}/I\), \(x\), \(\rho\) and \(w_{i}\), represent the incoming/transmitted photon intensities, glass thickness, glass density and fractional weight of the ith constituent element, respectively.  
Equations (16) and (17) were utilized to theoretically calculate the fast neutron significant removal cross-section \(\sum\limits_{R}(\text{cm}^{2}\text{g}^{-1})\) using the atomic number of the constituent metal oxides [26].  
\[\sum\limits_{R}=0.190\ Z^{-0.743}\ (Z\leq 8) \tag{16}\]  
\[\sum\limits_{R}=0.125\ Z^{-0.565}\ (Z>8) \tag{17}\]
# Study on the Carbon Emissions in the Whole Construction Process of Prefabricated Floor Slab
## 5 Analysis of the Results
### Comparison of Total Carbon Emissions in the Whole Construction Process  
We comparatively analyzed the total PFS carbon emissions and the cast-in-place floor slab in the whole construction process. Under the conditions of fixed transportation distance and carbon emission factors, the PFS carbon emission is 578.746 kg/m\({}^{3}\), whereas, that of the cast-in-place floor slabs is 890.04 kg/m\({}^{3}\). The use of PFS during the whole construction process can reduce the amount of carbon emissions by 35%. From this angle, it is an efficient way to reduce carbon emissions and promote the development of low-carbon buildings by increasing the use of PFS in buildings.  
Due to the rough construction management of cast-in-place floor slabs in the construction site, more energy is consumed due to construction with more machinery than PFS and a greater waste of materials due to on-site processing of raw materials, thus increasing the carbon emissions during the construction process.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 3 System Architecture
### Protocol Layer
#### Decision Process
The warehouse agent's pool offer decision process, shown in Figure 5, uses inputs including new pool offers from the capacity pool with parameters such as start time, end time, modified price, and expiry time, the status of previously forwarded direct offers (accepted or pending), and the agent's internal state, encompassing current capacity and financial objectives. The outputs include a decision to terminate if no new or accepted pool offer exists, acceptance of a pool offer with a scheduled storage service if it is deemed profitable, or processing of an accepted direct offer from the pool, finalizing the service schedule. If a new pool offer is detected, the agent evaluates the offer, considering whether it is in line with its current capacity and financial objectives. After evaluation, the agent checks whether to accept the pool offer. If the agent accepts the pool offer, the storage service is scheduled and the process is completed. If the pool offer is not accepted, the process terminates without making any changes to the agent's schedule. This decision process allows the warehouse agent to handle offers in the capacity pool, enabling the agent to maximize its revenue by selectively accepting profitable offers or processing their own forwarded offers. The warehouse agent's objective is to maximize revenue from storage services while managing available capacity. The warehouse agent can earn revenue through direct storage services and by forwarding offers with added fees to the capacity pool. The objective function for the warehouse agent is defined as shown in Equation (37). Each agent follows a specific decision-making process, leveraging the actions available to them in their action space.
# RF Exposure Assessment for Various Poses of Patient Assistant in Open MRI Environment
## 2 Materials and Methods
### Numerical Modeling of RF Coils for Open MRI Systems
#### 2.1.2 RF Body Coil  
An Alderman-Grant and Helmholtz combination was modeled for the RF body coil, as shown in Figure 3. Because an Alderman-Grant coil also creates a B\({}_{1}\) field in the x-direction, it can be combined with a Helmholtz pair, thereby creating a B\({}_{1}\) field in the y-direction, and producing a CP B\({}_{1}\) field. An Alderman-Grant and Helmholtz combination is known to achieve a better B\({}_{1}\) field homogeneity within a larger imaging space, which is needed for body scans [22]. As indicated in Figure 3, the dimensions of the body coil were 444 mm in width, 260 mm in height, and 352 mm in length. The coil driving conditions, including the resonance frequency, constant current source, and wave propagation, were all the same as in the head coil. The steady-state wave propagation in the x- and y-directions were also observed at the same magnitude but with a 90-degree phase delay, indicating the correct formation of the CP B\({}_{1}\) field in the xy-plane, as shown in Figure 4. In the figure, the B\({}_{1}\) fields are under an unloaded condition. We also confirmed the driving of CP-mode under a loaded condition by observing the magnitude and 90-degree phase delay.  
Figure 1: Geometry and dimensions of the numerically modeled head RF coil in a 0.3 Tesla open MRI system.  
Figure 3: Geometry and dimensions of the numerically modeled body RF coil in a 0.3 Tesla open MRI system.  
Figure 2: Circularly polarized (CP) excitation magnetic field (B\({}_{1}\) in Tesla) of the head RF coil model under the unloaded condition.
## 4 Discussion
Moreover, it may be possible to improve classification performance further by using other types of EEG features that have been proven useful for distinguishing between healthy and depressed individuals in prior studies, such as nonlinear features based on fractal dimension (FD) analysis (e.g., [PERSON]'s FD [6], correlation dimension [10], [PERSON]' FD [46]), and the spectral-spatial features based on kernel eigen-filter-bank common spatial pattern (KEFB-CSP) proposed in our recent work [12]. All these features may also perform better in the positive emotion-induction state than in the resting state. Although discussion of these other features is beyond the scope of the current study, they certainly merit attention in future work. Finally, the clinical implications of this work deserve consideration. The diagnosis of MDD is currently assigned based on diagnostic interviews and self-reports. This is problematic, because individuals typically underreport their depressive symptoms [47]. This is especially true for men relative to women [48; 49] and for individuals from Asian as opposed to Western cultures [50; 51]. Consequently, MDD is often underdiagnosed in such participants, which results in needless suffering. This important issue could be resolved by application of the EEG-based method described here, because it does not rely on self-reporting for detecting depression. For this possibility to materialize, however, the method will need to be validated in such participants. If this idea is pursued further, it would be valuable to simultaneously try to identify EEG signals that can predict which individuals will respond to which specific treatment. Matching treatments to patients is currently based on a trial-and-error approach that is inefficient: many individuals must cycle through several treatments before finding one that works for them. To combat this problem, recent studies have identified specific variables--measured behaviorally or via self-reports--that can be used to identify the optimal treatment for individuals on the first try [52; 53]. MDD, however, is a heterogeneous disorder that likely involves multiple distinct pathophysiologies. These pathophysiologies may be difficult to tease apart behaviorally or by self-reporting, but they may respond very differently to various treatments. Because EEG is a direct measure of brain activity, it is presumably closer to the pathophysiological process, and so, it may be possible to identify EEG-based signals that could be used for treatment prediction (e.g., [54]). In short, an important next step will be to use EEG signals not only to distinguish between individuals with MDD and healthy controls, but to identify the particular intervention that is likely to work best for one depressed participant versus another.
# Isolation and Properties of _Enterobacter_ sp. LX3
## 1 Introduction  
Plants and microbes produce indoleacetic acid (IAA), which regulates plant growth [1]. IAA application promotes both root and shoot development and elongation, directs and controls cell division, expansion, differentiation, regulates enzyme activity and gene expression [2,3]. Thus, microbes that produce IAA could have value as biofertilizers [4]. Such studies are in progress to develop IAA-producing microbes to promote plant growth and protect sustainable agriculture [3].  
Inoculation of maize plants with IAA-producing _Candidata tropicalis_ reduced the amount of chemical fertilizer application and increased maize growth and yield performance [5]. _Paenibacillus polymya_ could be developed to control pathogens and increase crop yield by producing IAA in field applications [6]. Additionally, inoculation of _Funneliformis mosseae_ with trifoliate orange resulted in greater root-hair growth in density, length and diameter of seedling roots [7]. Overproduction of IAA improved nitrogen-fixing performances of endophyticic microbes in culture or inoculated host plants [8].  
Biochar can elongate plant roots and increase crop production by improving rhizospheric microbial community structure [9]. Therefore, rhizospheric soil was collected from a maize field amended with biochar, and one strain was obtained that could maximally produce 200 mg/mL of IAA. Under axenic conditions, strain LX3 promoted growth and increased the chlorophyll content of barley. In this paper, the newly isolated strain LX3 was identified as _Enterobacter_ sp. According to phenotypic characteristics and phylogenetic analysis, LX3 showed strong potential as a biofertilizer for agriculture use.
## 4 System Design and Analysis
### Synergetic Software-defined Sampling and Detection Approach  
Synergetic software-defined sampling and detection is the foundation of Sonum. The emergence of a Software-Defined Networks makes software traffic collection possible. Compared to hardware traffic collection, software solutions are more flexible to deploy and can dynamically adjust sampling rate as needed. The disadvantage of software solutions is that performance is often not good. On the basis of existing network topology and transmission mode, through collaborative optimization, using a lower sampling rate to achieve a better sampling effect is the goal of software traffic information collection. The responsibility for synergetic software-defined sampling and detection is to effectively detect long flow using a lower sampling rate and a lower communication cost between controller and switch. Therefore, we designed three mechanisms to achive this goal: Synergetic Sampling, Self-Adaption Sampling and Lifecycle Management.  
The problem of long flow detection is as follows: Given a threshold parameter \(T_{l}\) and a traffic information \(F=F_{1},F_{2},\ldots,F_{n}\), a long flow is a flow \(F_{i}\) includes more than \(T_{l}\) percent of packets since the beginning of the transmission.
# Routing Schemes in FANETs: A Survey
## 2 Background and Motivating the Need for FANETs
### What Are the Mobility Models in FANETs?
#### 2.4.1 Pure Randomized Mobility Models  
The pure randomized mobility model has been widely used in ad hoc networks. There are four common characteristics. Firstly, nodes are independent on each other. Secondly, nodes are memoryless, and so, the new speed and direction of the nodes are independent on their previous speed and direction [86]. Thirdly, nodes can move at any speed within a range (e.g., 30-460 km/h [20]). Fourthly, nodes can move freely in an unpredictable manner in a fixed space at all times. The difference among the pure randomized mobility models lies in the way in which the direction of the nodes' movement is determined.  
* Random walk, which is based on the Brownian movement, allows a node to move in any directions
* Random waypoint [87, 88] allows a node to move in different directions (i.e., either straight, left, or right, rather than backward) towards a destination [89, 90]. When the node arrives at its destination inside a fixed space, it stops for a short period of time, which helps to prevent a drastic change in order to provide smooth movement, and then, it starts to move towards another randomly chosen destination.
* Random direction (or random mobility) shares the similar way in which the direction of a node's movement is determined with random waypoint (A.2). The main difference is that a node must stop at the edge of a fixed space [91]. This means that a destination must be located at the edge of a fixed space rather than inside the space.
* Manhattan grid allows a node to move in straight \(x\)-axis and \(y\)-axis directions in a grid road layout in urban areas. Specifically, the node moves in different directions (i.e., either straight, left, or right, rather than backward) to proceed or to turn to the left or right road at an intersection [92].
# Hardware Model Checking Algorithms and Techniques
## 2 Background and Notation
### Symbolic Representation of Transition Systems
#### 2.11.2 Symbolic Representation of Transitions
With abuse of notation, hereinafter, we make no distinction among the following concepts:  
* A transition \((s,s^{\prime})\in R\), any propositional formula \(F_{s,s^{\prime}}\) that is logically equivalent to its characteristic formula \(\tilde{\zeta}_{s,s^{\prime}}\) and the only model for such formula \(\mu_{s,s^{\prime}}\). * A set of transitions \(Z\subseteq R\), any propositional formula \(F_{Z}\) that is logically equivalent to its characteristic formula \(\xi_{Z}\) and the set of models for such formula \(\text{Mods}(F_{Z})=\{\mu_{z,z^{\prime}}|(z,z^{\prime})\in Z\}\). The transition relation \(R\) is, thus, encoded as a propositional formula \(T\) over \(V\cup V^{\prime}\) such that the encoding \(\mu_{s,s^{\prime}}\) over \(V\cup V^{\prime}\) of each transition \((s,s^{\prime})\in R\) satisfies \(T\). **Example 7**. : _Given the hardware design and its transition system described in Example 1, the transition relation \(R\) will be encoded as the propositional formula \(T\) over \(V\cup V^{\prime}\):_  
\[ \begin{split} T=&(\
eg v_{0}\wedge\
eg v_{1} \wedge v^{\prime}_{0}\wedge\
eg v^{\prime}_{1})\vee\\ &(v_{0}\wedge\
eg v_{1}\wedge\
eg v^{\prime}_{0}\wedge v^{ \prime}_{1})\ \ \vee\\ &(\
eg v_{0}\wedge v_{1}\wedge\
eg v^{\prime}_{0}\wedge\
eg v^{ \prime}_{1})\vee\\ &(v_{0}\wedge v_{1}\wedge\
eg v^{\prime}_{0}\wedge v^{\prime}_{1 })\end{split}  \tag{29}\]