esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of VOCs on Cell Wall and Plasma Membrane of B. cinerea  
To evaluate the effect of VOCs emitted by _Naganishia_ sp. on the cell wall of _B. cinerea_, a Calcolfluor White stain was used. This stain binds to polysaccharides such as chitin, which is present in the cell wall, allowing the visualization of fluorescent hyphae [31]. Results showed a decrease in fluorescence intensity in VOC-treated hyphae compared to the untreated control, similar to the positive control treated with lytic cell wall enzymes (Figure 6). A 50% decrease in fluorescence intensity was observed when hyphae were treated with VOCs compared to the negative control (Figure 6), indicating that VOCs emitted by _Naganishia_ sp. cause damage to the _B. cinerea_ cell wall.  
Additionally, the integrity of the plasma membrane was assessed using SYTOX Green (Figure 7). In the presence of VOCs, green fluorescent nuclei in _B. cinerea__hypha_ can be observed, similar to the positive control, while no fluorescence was observed in the negative  
Figure 6: Effect of VOCs emitted by _Naganishia_ sp. on the cell wall integrity of _B. cinerea_. The cell wall was stained with Calcolfluor white. (**A**,**B**) Negative control. (**C**,**D**) Positive control (lysing enzymes). (**E**,**F**) Treatment with VOCs emitted by _Naganishia_ sp. after 16 h of incubation. (**A**,**C**,**E**) visualized by light microscopy. (**B**,**D**,**F**) visualized through fluorescence microscopy. (**G**) Quantification of the fluorescence intensity. The effect was measured as relative fluorescence intensity compared to maximum fluorescence (negative control). Values represent the mean of three biological replicates \(\pm\) SD. For statistical analysis, a Kruskal–Wallis test, and [PERSON]’s post-test was carried out. Different letters represent significant differences with \(p\) < 0.05.  
control. This suggests that VOCs emitted by _Naganishia_ sp. also damage the cell membrane of _B. cinerea_.
## 2 A Brief Introduction to Laser-Scanning 3D Model Reconstruction Technology
### A Brief Description of the Laser-Scanning 3D Model Reconstruction Process
#### 2.2.1 On-Site Preparation  
Before initiating the scanning process, it is crucial to conduct on-site reconnaissance and review relevant documentation to preliminarily plan the positions of scanning stations on the drawings. It is noteworthy that the point cloud data acquired with the 3D laser scanner are based on relative coordinates. This implies that data from different scanning stations exist in distinct coordinate systems, resulting in errors during the merging of point cloud data from adjacent stations. Multiple-station alignments can introduce error propagation and accumulation. To ensure the subsequent alignment of point cloud data in a unified coordinate system and to minimize error accumulation, establishing a control network for coordinate transformation and error control is imperative [28]. Common control networks include the national control network or a locally established regional control network. Due to the extensive control area and the relatively sparse distribution of control points within the national control network, this can potentially result in substantial absolute errors. Consequently, to guarantee the accuracy of the point cloud and minimize subsequent modeling errors, a locally established regional control network is typically employed for precise control of the point clouds. The principle of a locally established regional control network involves using a total station to measure the coordinates of each control point and target point in the coordinate system of the control network. The control points and target points are strategically positioned to form a coordinate control network, effectively managing the overall scanned point cloud coordinates within the scanning area  
Figure 2: Flow chart of the laser-scanning 3D model reconstruction process.  
and unifying the coordinate system. A schematic diagram illustrating the principle of the regional control network is presented in Figure 3.  
In the establishment of a regional control network, it is crucial to uniformly distribute control points considering the range of the scanning area and on-site conditions. These control points should be positioned at elevated locations, providing a fixed and unobstructed line of sight, and secured using bolted connections. This methodology ensures optimal visibility during measurement tasks and assures stability, with the points remaining securely in place for over five years without detachment. The strategic positioning of control points not only improves point cloud accuracy but also establishes a robust foundation for subsequent tasks, including the supplementation and adjustments of the point cloud data.
# Photo-Transformation of Effluent Organic Matter by ZnO-Based Sunlight Irradiation
## Materials and Methods
### Photocatalytic Degradation Experiments  
Photocatalytic experiments were carried out using a solar simulator and a 100-mL Pyrex reactor with a double-layer jacket (Figure S1) [11]. A 300-W Xenon arc lamp (Ushio Inc., Tokyo, Japan) was installed in the solar simulator (SLB300B, Scienetech Inc., London, Canada). The Xenon lamp emitted a visible light with a wavelength range of 350-2000 nm. The irradiation intensity on the surface of the solutions was \(3.15\pm 0.05\) mW/cm\({}^{2}\), as measured using a radiation intensity meter (UV-340C, Custom, Tokyo, Japan). This intensity reading was equivalent to the natural solar irradiance that was measured on Chung-Ang University campus (Seoul, South Korea) at noon during November 2019. The distance between the light source and samples was maintained at \(\sim\)5 cm. Zinc oxide (MF: ZnO, MW: 81.39 g/mol) that was purchased from Sigma-Aldrich (Saint Louis, MO, USA) was used as the photocatalyst. The photocatalyst dosage was set at 0.2 g/L. The initial pH of the solution was set at pH 7 by adjusting the solution with 1 M sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) and 1 M sodium hydroxide (NaOH). The H\({}_{2}\)SO\({}_{4}\) (purity \(\geq\) 96%) was obtained from Kanto Chemicals (Tokyo, Japan), and the NaOH was obtained from Daejung Chemicals (Busan, South Korea). The samples (100 mL) were placed in the reactor and irradiated for 120 min at room temperature (\(23\pm 1\)\({}^{\circ}\)C). The solution in the reactor was gently stirred with a magnetic bar for uniform mixing during the experiment. At regular time intervals (0, 30, 60, 90, and 120 min), the samples were removed and then immediately filtered through a 0.45-um polytetrafluoroethylene (PTFE) syringe filter in order to separate the ZnO powder from the solution.
## 3 Results and Discussion
### Comparison with Existing Designs  
Table 6 presents a comparative analysis of various microstrip antennas designed for the 28/38 GHz frequency range. This table synthesizes data from recent studies, offering a comprehensive overview of the bandwidth, antenna configurations, physical dimensions, S\({}_{11}\) parameters, gain, and efficiency values. The first two columns list each antenna's operational frequency range (in GHz) and corresponding bandwidth (in GHz). The proposed design operates within the 27.63-28.75 GHz and 37.33-38.60 GHz ranges, achieving bandwidths of 1.12 GHz and 1.27 GHz, respectively. These values position the design competitively, though some existing works report larger bandwidths, up to 7.35 GHz, demonstrating the potential to balance bandwidth with gain. The antenna configurations can vary from single-element designs to complex multi-element arrays. The proposed design, measuring 27 \(\times\) 33 mm\({}^{2}\), is larger than certain single-element antennas, like those presented in [32], which measure as small as 4.62 \(\times\) 4.49 mm\({}^{2}\). The physical dimensions are pivotal, as they influence the antenna's performance, particularly the gain and efficiency. The S\({}_{11}\) values, which indicate the return loss and the quality of impedance matching, are critical metrics for antenna performance. The proposed design exhibits remarkably low S\({}_{11}\) values of \(-\)53.04 dB at 28 GHz and \(-\)83.65 dB at 38 GHz, indicating excellent reflection minimization and optimal power transfer. In contrast, other studies reported S\({}_{11}\) values between \(-\)17 dB and \(-\)34.5 dB, highlighting the superior return loss achieved by the proposed design.
# Investigation on Reference Frames and Time Systems in Multi-GNSS
## 4 Results of the Positioning Analyses  
Daily analysis has been performed on the 31 stations listed in Table 1 and shown in Figure 1 starting from 1 January 2014 and is currently ongoing. Figure 8 shows the time series of GLGP, GPGA, BDGP and QZGP, distinguishing between the five types of receiver.  
In the GLGP time series a very large misalignment of about \(-400\) ns from the beginning of the analyses until mid-August 2014 can be seen. Then a re-aligning procedure started and was completed at the end of 2014. From the beginning of 2015 the GLONASS time scale is generally kept aligned with GPS, but several short-term variations with amplitudes ranging from tens to about one hundred ns are evident. The largest variation observed happened from July to December of 2016, and has a sinusoidal shape with an amplitude of about 100 ns.  
BDGP varies continuously, with an oscillating trend and amplitude of about 70 ns. Furthermore the oscillations have a positive mean, which results in an average positive offset of the BeiDou time scale with respect to GPS. Considering a time series of more than three years it is possible to notice a slow trend toward zero, but it is not clear if this long-term variation is effectively a re-alignment procedure of the BeiDou time scale to GPS.  
Figure 8: Time series since 1 January 2014 of offsets of the GLONASS (**a**), Galileo (**b**), BeiDou (**c**) and QZSS (**d**) time scales relative to GPS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time Offset** & **Definition** \\ \hline GLGP & \((\text{TSC}_{\text{R}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{G}}+\text{ dT}_{\text{Rec}})\) \\ GPGA & \((\text{TSC}_{\text{C}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{E}}+\text{ dT}_{\text{Rec}})\) \\ BDGP & \((\text{TSC}_{\text{C}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{G}}+\text{ dT}_{\text{Rec}})\) \\ QZGP & \((\text{TSC}_{\text{G}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{G}}+\text{ dT}_{\text{Rec}})\) \\ NAGP & \((\text{TSC}_{\text{G}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{G}}+\text{ dT}_{\text{Rec}})\) \\ GNGP & \((\text{TSC}_{\text{N}}+\text{dT}_{\text{Rec}})-(\text{TSC}_{\text{G}}+\text{ dT}_{\text{Rec}})\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Definition of Time Offsets.  
GPGA is aligned with GPS, since the time offset series are almost constant in time and centered to zero. Two sudden variations took place in November 2015 and November-December 2016, measuring about 60 and 30 ns, respectively.  
The QZGP system time is aligned with GPS, but unfortunately for this system data from only five of the 31 stations is available. Among these five stations, Leica and Trimble receivers show higher scattered results compared to those of Septentrio and Javad, despite the global average value is very close to zero. It is important to notice that until June of 2017 there was only one satellite belonging to QZSS, so QZGP actually represents the time offset between this single satellite and GPS.
# Performance Evaluation of an Imaging Radiation Portal Monitor System
## 3 RPM Detection Performance Results
### Radiation Source Tests
#### 3.3.1 \({}^{252}\)Cf  
Table 3 summarizes the results of the measured response to the passage of a 1.25 uCi \({}^{252}\)Cf source. This relatively weak source had a neutron emission rate of approximately 5400 n/s, which corresponds to 27% of the activity of the \({}^{252}\)Cf source prescribed for RPM tests of neutron sensitive RPMs by the ANSI N42.35-2016 standard [9]. The \({}^{252}\)Cf material was embedded in a ceramic cylinder with dimensions 4.6 mm (diameter) by 6 mm and encapsulated in a double-welded stainless-steel cylinder with outer dimensions 7.8 mm (diam.) by 10.0 mm. The highest sigma multiplier factor, and hence the lowest FAR was obtained for gamma-neutron coincidence detection whereas the highest statistics and lowest false-negative probability is achieved for single-neutron counting, by far exceeding the requirement of a maximum average false negative probability of \(p_{N}=1.7\%\) specified by the ANSI N42.35-2016 industry standard. Consequently, the best overall performance of the RPM in terms of combined sensitivity and FAR was obtained from joint single-neutron and gamma-neutron coincidence counting. The last row in the table gives the ANSI N42.35-2016 specified neutron source emission rate and scaled sigma multiplier factors and false negative probabilities that would result from measurements on a source with such an emission rate.
## 3 Results and Discussion
### Cell Viability  
MTT assay was achieved to evaluate the toxicity and biocompatibility of _I. graveolens_/PCL nanofibers on Fibroblast cells (Figure 4) that shows cell viability cultures at different times 24, 48, and 72 h, respectively, of incubation. The growth rate on the scaffold of the PCL/_I. graveolens_ is higher than on the scaffold made of PCL, as is evident on the graph, and also surpassed that of the control group at the end of the third day. The results showed that nanofiber scaffolds of PCL/_I. graveolens_ exhibit no cytotoxicity on Fibroblast cells. The fabricated nanofibers of captured _I. graveolens_/PCL were obtained by Electrospum, with attractive amplified surface area and density, pore size properties, that advanced by optimization by adjusting some important variables such as the distance between needle and collector, electrical voltage, and also the amount of the polymer after being pumped. Nanofibers have gained considerable importance in biotechnology due to their promising applications due to a large surface area that could be turned into an ideal matrix for cellular activities [34]. Nanofibers form a chain of biomaterial interactions within cell cultures. Therefore, it is necessary to make use of biocompatible materials for this matter [35].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Solutions:Ratio** &
\begin{tabular}{c} **Contact Angle (\({}^{\circ}\))** \\ **(Hydrophilicity)** \\ \end{tabular} & **FR (mL/h)** & **TCD (cm)** & **Voltage (kV)** \\ \hline PCL & - & 118.4\({}^{\circ}\)\(\pm\) 2.0\({}^{\circ}\) & 0.5 & 18 & 20 \\ \hline PCL/_I. graveolens_ & 70:30 & 51.4 \(\pm\) 2.0\({}^{\circ}\) & 0.5 & 18 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples’ codes, compositions, and electrospinningnitions of the Electrospum nanofibers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Ultimate Tensile Strength** & **Contact Angle (\({}^{\circ}\))** \\  & **(MPa)** & **(Hydrophilicity)** \\ \hline PCL & 1.5 \(\pm\) 0.1 & 118.4 \(\pm\) 2.0 \\ PCL/_I. graveolens_ & 5.2 \(\pm\) 0.7 & 122.4 \(\pm\) 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of the PCL/_I. graveolens_ Electrospum nanofibers after cross-linking (\({}^{\circ}\): \(p\) \(<\) 0.05).
## 1 Introduction
The common pseudospectral methods can be further classified into the Chebyshev pseudospectral method (CPM) [44], Legendre pseudospectral method (LPM) [45], Gauss pseudospectral method (GPM) [46], and Radau pseudospectral method (RPM) [40], depending on the collation points picking method. Among them, RPM satisfies the principle of co-state mapping, and the approximation accuracy and convergence speed are better than other pseudospectral methods. To achieve rational planning of PMCU paths while satisfying kinematic and dynamical constraints as well as known global path information, this paper novelty proposes an RPM path planning method based on the probabilistic roadmap and heuristic rule (PH-RPM), which can be used for the path planning of cabin pushing operation process in the complex deck environment. The proposed PH-RPM uses probabilistic path diagrams to construct passable space and designs a path-pruning heuristic rule with reverse search. The combination of the two methods generates high-quality passable paths as initial solutions and inputs them into RPM for solving. In addition, in the process of RPM solving, the determination method based on geometric area constraints is applied to convert obstacle avoidance constraints into process constraints so as to avoid the introduction of 0-1 decision variables affecting the solution. The remainder of this paper is organized as follows: The PMCU pushing path planning model considering dynamics constraints, state and control constraints, and process obstacle constraints is constructed in Section 2. Section 3 proposes an improved RPM algorithm to solve the optimal control problem, discretizes the continuous optimal control problem into an NLP problem to solve, and designs an initial solution generation strategy based on PRM and the heuristic rule. Section 4 designs simulation experiments based on cruise ship PMCU pushing operation environment to verify the applicability and effectiveness of the algorithm proposed in this paper. Finally, the conclusions of this paper are summarized in Section 5.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 2 Preload and Rigidity of Linear Guide
### Static Radial Compressive Tests  
To assess the rigidity of the linear guides, we established a test platform for conducting the compressive experiments, as shown in Figure 2. The platform was constructed with structural steel, on which the linear rail was bolted, in accordance with the technical guidelines, to ensure mounting accuracy and parallel precision. In the experiment, a static load was gradually applied to the sliding block using a hydraulic jack with a 1.318 kN loading interval. The magnitude of the force was recorded by a load cell (LPU-5000, Transducer Technique). The vertical deflections of the block were measured using two dial gauges with a resolution of 1 \(\upmu\)m. The output information, including the load-displacement behavior of linear guides with different preloads, was obtained for comparison. The linear guides used for the compression tests were assembled with rolling balls of different diameters. These samples were categorized into three groups: light, medium, and high preloads.
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 3 Results
### RF-EMF Exposure Levels
#### 3.4.1 Exposure in Public Places
Whereas in urban and rural areas, the median and 75 th percentile values of the E-field recorded in buildings located near the RTV (within 1 km) where lower than 1.5 and 3.8 V/m (5% and 14% of the corresponding ICNIRP reference level) and 0.4 and 0.8 V/m (1.4% and 2.8% of the corresponding ICNIRP reference level), for radio FM band or for TV bands, respectively. A survey from [PERSON] et colleagues [46] on peak RF emissions in typical office environments found a peak power density equal to 54 mW/m\({}^{2}\) (4.5 V/m in terms of electric field, 7% of the ICNIRP reference level) at a distance of 1 m from a Wi-Fi access point operating at 2.45 GHz, which would correspond to an Effective Isotropic Radiated Power (EIRP) from the access point of about 0.6 W.  
In a study by [PERSON] et al. [36] indoor WiFi-5G was measured in an office building in Ghent, Belgium using personal exposimeters (PEMs). Results showed maximum and mean power density values equal to 8.9 mW/m\({}^{2}\) and 165.8 \(\upmu\)W/m\({}^{2}\) (1.8 V/m and 0.25 V/m, in terms of electric field strength). Three studies [13, 14, 16] focused on the assessment and comparison of the RF-EMF exposure of a mobile phone user that is either connected to an femtocell or a conventional macrocell base station while in an office environment. Results of the three studies showed that, in average macrocell coverage and mobile phone use-time conditions, the use of the femtocell improved reception quality indoor [13] while the total exposure was reduced by using a femtocell, mostly due to the significant decrease in the output power of the mobile phone [14, 16]. Finally, a study by [PERSON] and colleagues [47] focused on the RF-EMF exposure due to a femtocell in a typical office by simulation, without considering the exposure due to external sources such as base station for telecommunications of indoor sources different from the femtocell, such as mobile phones. In this study the finite-difference time-domain (FDTD) method was used to calculate electric fields emitted from a long-term evolution (LTE) femtocell, with and without the presence of 20 people and furniture. Simulated electric fields at most of the locations on the horizontal plane with a height of 1.0 m above the floor are found in the range of \(-\)10 to \(-\)30 dB V/m, which means a good signal will be picked up in the office. The maximum power density emitted from the LTE inside the office and maximum localized SAR induced in a standing person were found to be far below the IEEE safety standard for public exposure in uncontrolled environments [48].
###### Abstract  
In this study, silver nanoparticles (AgNPs) were green-synthesized extracellular by the action of bioactive compounds in cultural filtrates of green microalga _Scenedesmus obliquus_ (KY621475). The influences of six different nitrogen sources (i.e., NaNO\({}_{3}\), CO(NH\({}_{4}\))\({}_{2}\), (NH\({}_{4}\))\({}_{2}\)CO\({}_{3}\), KNO\({}_{3}\), NH\({}_{4}\)NO\({}_{3}\), and (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)) on extracellular biosynthesis of AgNPs were observed by UV-Visible spectroscopy (380-425 nm) and confirmed using high-resolution transmission electron microscopy (HRTEM). The highest biomass production was observed in the case of urea and ammonium carbonate treatments, which, surprisingly, showed negative activity for AgNPs biosynthesis. Considering their coupling and compatible presence in cultural filtrates, reductases (especially nitrate reductase) as reduction agents are assumed to play a key role in the extracellular biosynthesis of AgNPs. The cultural filtrates of the potassium and sodium nitrate treatments produce AgNPs of relatively small size (5-10 and 4-10 nm, respectively), smaller than those produced by filtrate of ammonium nitrate treatment. The antimicrobial activity of produced AgNPs was a function mainly of particle size, which was influenced by the nitrogen source of the microalgal culture. The AgNPs produced from the KNO\({}_{3}\) and NaNO\({}_{3}\) cultural filtrates performed the best as antimicrobial agents.  
_Scenedesmus obliquus_; nitrogen sources; silver nanoparticles; biosynthesis; antimicrobial agent; nitrate reductase