esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 4 Conclusions  
This chapter proposes a novel method based on the Parrot Optimizer for channel estimation in wireless communication systems (PO-CE). The PO-CE algorithm was evaluated in pilot-based OFDM systems under 5G NR TDL-300 frequency-selective channels and compared with LS and MMSE algorithms using BER and MSE as performance metrics. The experiments were conducted under different system configurations, including various modulation schemes (QPSK, 16 QAM, 64 QAM, and 256 QAM) and pilot densities (\(1/3\), \(1/6\), \(1/9\), and \(1/12\)).  
Simulation results indicate that the PO-CE algorithm's performance is close to that of the MMSE algorithm and that it significantly outperforms the LS algorithm. Given that the MMSE algorithm requires prior channel statistical information and has high complexity, making it difficult for practical applications, the PO-CE algorithm becomes a suitable candidate for addressing channel estimation problems in OFDM-based wireless systems.  
Figure 8: BER performance comparison of LS, PO, and MMSE under different pilot densities. \(X\)-axis: SNR [dB]. The curves are the results of simulations.  
Future research may focus on improving and evaluating the performance of the PO-CE algorithm under channel coding and MIMO configurations.  
Conceptualization, K.S.; Software, K.S.; Validation, K.S.; Writing--original draft, K.S.; Writing--review & editing, J.X.; Visualization, J.X.; Funding acquisition, J.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Natural Science Basic Research Program of Shaanxi (Program No.2023-JC-YB-575).  
Data Availability Statement: Data are contained within the article.  
Author [PERSON] was employed by the company Bull Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Rheological Properties of Lunar Mortars
## 5 Additional Considerations in the Context of Cement-Based Lunar Materials
In order to increase the overall properties of extraterrestrial cement composites, additive manufacturing using in situ resources (ISRU) is being developed to facilitate future off-planet habitation by enabling the development of infrastructure and other objects without the need for dedicated manufacturing facilities or distant transport of materials [40]. Laser sintering, direct printing, and powder binder jet printing have already been proposed as potential solutions in developing a lunar habitat [6]. Each of these methodologies has been tested utilizing various regolith simulants. With the use of laser sintering bricks, gears, nuts, and other components have been produced [41], exhibiting a wide range of compressive strengths (5-420 MPa) [42]. Powder binder jet printing was developed to manufacture structural elements of habitat and the feasibility of printing and curing the cementitious binder in vacuum conditions has already been validated [43]. All those methods require the consistency of manufactured cement mortar or concrete mix to be at a satisfactory level allowing for the use of any given technology. Water is a scarce lunar resource and the water demand for construction materials production competes with the demand for other crucial water applications. Therefore, there is a benefit in developing methods to recover water from these cementitious binders while producing a material with specific mechanical parameters and sufficient durability. Lunar regolith-based magnesium oxychloride (MOC) cement composites are promising candidates for water recovery [40]. These types of cement are based on the reaction between MgO and an MgCl\({}_{2}\) solution, enabling gel phases to be produced that lead to a high-compressive-strength binder that can incorporate a large amount of filler material, such as lunar regolith [44].
# Crop Water Content of Winter Wheat Revealed with Sentinel-1 and Sentinel-2 Imagery
## 2 Materials and Methods  
### Study Area  
The study area is located in Gaocheng District, Shijiazhuang City, Hebei Province (37\({}^{\circ}\)51' N-38\({}^{\circ}\)18' N, 114\({}^{\circ}\)38' E-114\({}^{\circ}\)58' E), China, as shown in Figure 1. Gaocheng District is located in southeastern Hebei Province, Taihang, Shandong Plain, and the climate is warm, temperate, semi-humid continental monsoon. Because the study site is located on a plain, topography and landforms have little impact on climate and the distribution of climatic factors is relatively loose. The region is characterized by dry and windy springs, hot and rainy summers, cool autumns, and cold and rainy winters. Wheat is the main food crop in Gaocheng District and is primarily grown in small-scale plots. Winter wheat cultivation begins in early October each year and harvest occurs at the end of May the following year [34].
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 3 CF of the Aggregated Interference in LOS
### Uniform Cylindrical Arrays
The granularity of numerical integration basically depends on the main beamwidth: the azimuth \(K=N_{c}\) is a safe choice with good accuracy, while for the elevation angle, \(M=N_{v}/2\) to \(M=N_{v}/4\) is acceptable (\(M=N_{v}/2\) is a safe choice for large \(\lambda\), e.g., \(\lambda>0.1\)). Thus, the complexity reduces with respect to massive integration, and in fact, this method would be a good way to reduce the computation complexity. Alternatively, whenever one uses a beam gain approximation model, such as the flat-top model [31], the summation reduces straightforwardly to two terms, and this would be quite fast in terms of computation for network analyses._  
**Remark 3**. : _The array gain (2) of vertical ULA is critical for the analytical tractability of the CF derivation, and the use of \(N_{v}>1\) could be questionable for a height that is too small. The array gain (2) holds true when the array aperture is compact compared to the array height \(h\) to have a plane wave-front. In practice, for mmW communications at a frequency of approximately 30 GHz (or larger), the wavelength is approximately 1 cm (or smaller), and for \(N_{v}=10\) antennas, the array aperture for half-wavelength antenna spacing is 5 cm (or less). In the scenario in Figure 1 the height should be above the people heights, and for \(h>2\), \(m\) the approximation that the array aperture is compact (2 \(m\gg 5\) cm) holds true._
## 5 Case Simulation and Verification  
### Experimental Design  
In this session, stress simulation for VIG was carried out to compare the stress and deformation distribution under different supporting pillar placement parameters. Glass substrate, with a 500 mm length, 500 mm width and 5 mm height, was selected to fabricate VIG. The supporting pillars with 0.3 mm height and 0.6 mm diameter were equally spaced between the glass panes. The width of the edge sealing was 10 mm.  
The mechanical properties and parameters of the glass pane were designed based on the National Standard of Safety Glass for Construction (GB 15763.2-2005) [31], with a 2500 kg m\({}^{-3}\) density, a 72 GPa elastic modulus and a 0.24 Poisson ratio, consistent with Table 2. The supporting pillar was made of stainless steel with a density of 7800 kg m\({}^{-3}\), an elastic modulus of 200 GPa and a Poisson ratio of 0.3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline a/b & 1.0 & 1.2 & 1.4 & 1.6 & 1.8 & 2.0 \\ \hline \(\beta_{1}\) & 0.1386 & 0.1794 & 0.2094 & 0.2286 & 0.2406 & 0.2472 \\ \(\beta_{2}\) & \(-\)0.238 & \(-\)0.078 & 0.011 & 0.053 & 0.068 & 0.067 \\ \hline \end{tabular}
\end{table}
Table 1: Coefficients when rectangular flat-plate functioned by uniform load [16].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Structure Parameters** & **Abbreviations** & **Value** & **Unit** \\ \hline VIG size & \(L\times L\) & 500 \(\times\) 500 & mm \(\times\) mm \\ Glass substrate thickness & \(h\) & 5 & mm \\ Vacuum gap/pillar height & \(h_{2}\) & 0.3 & mm \\ Edge sealing width & \(m\) & 10 & mm \\ Atmosphere pressure & \(q_{0}\) & 0.1 & MPa \\ \hline \multirow{3}{*}{Constant coefficient} & \(\alpha_{1}\) & 0.8719 & \\  & \(\beta_{1}\) & 0.1386 & \\  & \(\beta_{2}\) & \(-\)0.238 & \\ \hline Glass density & & 2500 & kg m\({}^{-3}\) \\ Glass elastic modulus & & 72 & GPa \\ Glass Poisson ratio & \(\
u\) & 0.24 & \\ \hline \end{tabular}
\end{table}
Table 2: Structure parameters of VIG from reference [30].  
ANSYS workbench simulation was performed with different supporting pillar radii and spacing treatments. Supporting pillars were distributed in a square array, and three treatments were designed as follows: R3a63, with a 0.3 mm supporting pillar radius and 63 mm spacing; R3a50, with a 0.3 mm radius and 50 mm spacing; and R2a63, with a 0.2 mm radius and 63 mm spacing (Table 3).
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 3 Effects of Nanofluid Physical Properties and Heated Surface on Boiling Heat Transfer
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Nanoparticles** & **Base Liquid** & **Concentration** & **Heated Surface** & **CHF/HTC** & **Reasons** \\ \hline \multirow{2}{*}{GNs} & \multirow{2}{*}{EG/DW} & 0.005, 0.01, 0.02, 0.05, & \multirow{2}{*}{heater wire} & CHF enhancement & The surface wettability caused by the deposition of \\  & & 0.1 wt.\% & & HTC enhancement & nanoparticles is enhanced. \\ \hline \multirow{2}{*}{GONs} & \multirow{2}{*}{Water} & 0.001, 0.002, 0.0005, & \multirow{2}{*}{Copper spheres} & CHF non-monotonic & Non-monotonic changes consistent with changes in \\  & & 0.01 wt.\% & & change & surface wettability caused by GON \\ \hline \multirow{6}{*}{CNT} & \multirow{6}{*}{Water} & 0.1, 0.2, 0.3 wt.\% & \multirow{2}{*}{stainless steel rod} & CHF enhancement & Nanoparticle deposition causes changes in surface \\  & & & HTC enhancement & roughness and vaporization core points. & [71] \\ \cline{3-5}  & & & & CHF enhancement is due to the complete wetting & \\  & & & & HTC enhancement & behavior exhibited by the nanoparticles. & \\  & & & & HTC deterioration is due to the increase in & [76] \\  & & & & HTC deterioration is due to the increase in & \\  & & & & & wettability and thickness of the porous & \\  & & & & layer structure. & \\ \cline{3-5}  & & & & & & layer structure. & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline \multirow{6}{*}{SiO\({}_{2}\)} & \multirow{6}{*}{Water} & 0.05-1 vol.\% & \multirow{2}{*}{cylindrical heater} & CHF enhancement & Nanoparticle deposition causes an increase in \\  & & & & & & surface roughness. \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline \multirow{6}{*}{SiO\({}_{2}\)} & \multirow{6}{*}{Water} & 0.01, 0.01, 0.05, 0.1 wt.\% & \multirow{2}{*}{vertical cylinder} & \multirow{2}{*}{HTC deterioration} & The change in surface properties caused by the \\  & & & & & deposition of nanoparticles on the surface has a \\  & & & & & major influence on the quenching process. & \\ \hline \multirow{2}{*}{EG/DW} & \multirow{2}{*}{0.1, 0.25, 0.5, 0.75 vol.\%} & \multirow{2}{*}{hot-wire} & HTC enhancement with & Boling heat transfer changes caused by \\  & & & & concentration of 0.25\% & nanoparticle coatings. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Effects of Night Ventilation on Indoor Air Quality in Educational Buildings--A Field Study
###### Abstract  
Night ventilation methods have been used in educational buildings to guarantee indoor air quality at the beginning of occupied periods. A typical method has been to pre-start ventilation 2 h before the space usage. Another selection has been to ventilate a building continuously during the night with a minimum airflow rate that can dilute material emissions. In this study, the pre-started, continuous, and intermittent ventilation methods were compared by assessing indoor air quality in field measurements. The daytime ventilation was operating normally. The test periods lasted for 2 weeks. Indoor air quality was assessed by measuring the total volatile organic compounds and microbial concentrations using the quantitative polymerase chain reaction method. Additionally, the thermal conditions, carbon dioxide, and pressure differences over the building envelope were measured. The results show that the night ventilation strategy had negligible effects on microbial concentrations. In most cases, the indoor air microbial concentrations were only a few percent of those found outdoors. The averaged concentration of total volatile organic compounds was at the same level with all the night ventilation methods at the beginning of the occupied periods in the mornings. The concentrations reached a minimum level after 2-h ventilation. The concentrations of total volatile organic compounds were higher during the day than at night. This reveals that space usage had the largest effect on the total volatile organic compounds. Generally, the results show that continuous night ventilation does not significantly affect the biological and chemical contaminants. Consequently, a 2-h flushing period is long enough to freshen indoor air before occupancy.  
intermittent ventilation; night purging; indoor air quality; TVOC; microbes +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Evaluation of AA and TP Prediction Models and Future Direction  
The combination of FOD preprocessing and CARS-based band selection with PLSR modeling provided promising improvements for predicting AA and TP content in fresh tea leaves. For AAs, the method increased the R\({}^{2}\) by approximately 7% compared to raw spectra or first-order derivatives, while for TPs, the improvement was more modest. Although these enhancements indicate the potential of FOD and CARS in capturing key spectral features, the overall prediction accuracy remains preliminary. Limitations include the relatively small sample size and the focus on leaf-level measurements only.  
Future work should consider expanding the sample set to include a broader range of tea varieties and growing conditions. Additionally, exploring finer increments in FOD order and extending the analysis to canopy-level measurements could further validate and enhance the robustness and generalizability of the proposed method.
# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Multimodal Segmentation:  
The shape or appearance model called Shape Constraint (SC-GAN) uses a Fully Convolutional Residual Network (FC-ResNet) fused with a shape representation model (SRM) for segmentation tasks on multimodal images in H&N cancer diagnosis. A pre-trained 3D convolutional auto-encoder is utilized for SRM as a regularizer in the training stage [110]. Multimodal segmentation should have acceptable results in both source and target domains. However, the domain shifts between multiple modalities make the learning task of divergent image features through a single model challenging. Three-dimensional unified GAN executes the auxiliary translation task by extracting the modality-invariant features and upgrading low-level information representations [111]. Hippocampal subfields segmentation based on SVM combined 3D CNN and GAN. Three-dimensional GAN-SVMacts as a generator and 3D CNN-SVM discriminator [112]. One2 One CycleGAN is used in survival estimation extracting features from MRI multimodal images. A single ResNet-based generator creates the T1 image from the T2 samples and the T2 image from the T1 samples reducing overfitting and providing augmentation to create virtual samples [113]. MRIs are used to locate the abrasion of disease or to understand the fMRI-based effective connectivity (EC) within a set of brain regions. The task of locating the abrasion caused due to Multiple Sclerosis (MS) in brain images is a real challenge as there is much inconstancy in the intensity, size, shape, and location of these abrasions. In [114], GAN uses a single generator with multiple modalities and multiple discriminators, one for each modality, to identify the NxN patch as real or fake.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 3 Basic Algorithms  
### Aca  
The Ant Colony Optimization (ACO) is a biomimetic metaheuristic for optimization inspired by ant colonies' foraging behavior in their ecosystems. The technique has its roots in the work of [PERSON] et al. in the 1990s. The basic idea behind the application of the ACO is the cooperative way ants create routes through the deposition of pheromones; when foraging, ants choose paths randomly and deposit pheromones indicating more desirable routes upon returning to the nest. Consequently, ants that follow are likely to go through  
Figure 1: Grid environment.  
paths with higher pheromone levels, hence creating a self-organizing optimization process with positive feedback.
## 2 Materials and Methods
### Spectroscopy  
Raman spectra were acquired using a handheld Raman (h-RAM) spectrometer, IDRaman mini 2.0 (Ocean Optics, Orlanda, FL, USA). The spectrometer was powered either from a laptop computer or using regular AA batteries. Its weight amounted to 380 g. The h-RAM was equipped with a point-and-shoot adapter (Ocean Optics, Orlanda, FL, USA).Using raster orbital scanning (ROS), the laser beam was moved following a circular pattern with a diameter of 3 mm. The laser beam had a width of approximately 25 \(\upmu\)m. Spectra were acquired from 400 to 2300 cm\({}^{-1}\) and the spectral resolution was 13 cm\({}^{-1}\). The laser excitation wavelength was 785 \(\pm\) 0.5 nm with a power of 100 mW. As a second instrument, a QE Pro Spectrometer Raman spectrometer (Ocean Optics, Orlando, FL, USA) was used operating at 638 nm and 35 mW with a 638 LAB FCA laser (Ocean Optics, Orlando, FL, USA). An RIP-RPB-638-FC probe was coupled to the spectrometer. The spectra were recorded with a resolution of 7-10 cm\({}^{-1}\) and a Raman range of 150 to 3150 cm\({}^{-1}\). The software OceanView 1.5.2 (Ocean Optics, Orlando, FL, USA) was used for spectral acquisition. No sample preparation was required when using the two fieldable spectrometers, since both the point-and-shoot adapter and the RIP-RPB-638-FC probe allowed measurements on the fruit by positioning the probe tip close to the fruit surface. The exclusion of ambient light using a blackened cardboard cylinder helped improve the signal intensity.  
For comparison, Raman spectra were also recorded on an NXR FT-Raman module (Thermo Scientific, Waltham, MA, USA). The module was connected to a Nicolet 6700 FT-IR spectrometer (Thermo Scientific, Waltham, MA, USA). The spectra were processed using Omnic software version 8.0 (Thermo Scientific, Waltham, MA, USA). For each measurement, 64 spectra were accumulated in the range of 100 to 3800 cm\({}^{-1}\). The resolution was set to 6 cm\({}^{-1}\). The wavelength of the laser was 1064 nm with a power of 900 mW. For this benchtop spectrometer, sample preparation required the excision of a part of the hot pepper (10 \(\times\) 10 mm) and positioning it in the spectrometer's sample cell.
###### Abstract  
Apparent resistivity imaging is a typical rapid imaging method in the ground-airborne frequency-domain electromagnetic method. At present, the apparent resistivity is typically calculated by the measured magnetic field, however, this imaging method exhibits limited capability in recognizing the center of three-dimensional anomalies. Therefore, this paper proposed the calculation of apparent resistivity using magnetic field gradients. To solve the problem of random artificial anomalies that existed during the calculated process, this paper presents a hybrid least square support vector machine (LSSVM) and Northern Goshawk optimization (NGO) to establish the mapping relationship between the magnetic field gradient and apparent resistivity variation. This approach enables accurate prediction of apparent resistivity variations and effectively resolves the challenge of correcting background resistivity. Furthermore, three typical theoretical models and field examples are used to predict the apparent resistivity variation, the imaging results demonstrate that the proposed NGO-LSSVM algorithm is a feasible and efficient tool for predicting the apparent resistivity variation with high accuracy. This study provides a novel and efficient imaging method, which facilitates the application of ground-airborne frequency-domain electromagnetics for high-resolution detection requirements, such as mineral exploration.  
m pacs: 1
## 3 Results and Discussion
### Identification of Potential Bacteria  
Figure 5 shows the 16S rRNA PCR product separated by 1% (\(w/v\)) agarose gel. The size of the amplified 16S rRNA was approximately 1500 bp.  
According to [PERSON] et al. [89], sizable areas of soils have been metal-contaminated due to the extensive use of chemical fertilizers containing essential min  
Figure 5: Electrophoresis gel image of PCR products. Marker: 1 kb marker. A 3 \(\mu\)L volume of the sample was loaded in each well. The target sequences were approximately 1500 bp. The percentage of gel was 1%. The forward primer was 5\({}^{\prime}\) GAG TTT GAT CCT GC TCA G 3\({}^{\prime}\) and the reverse primer was 5\({}^{\prime}\) GTT ACC TTG TTA CGA CT 3\({}^{\prime}\) (BioSune Biotechnology Co., Ltd., China). Note\({}_{1}\): Lane 1: empty; Lane 2: UPMC1166; Lane 3: UPMC1168; Lane 4: UPMC1254 (red box due to low density as compared to the other lanes); Lane 5: UPMC1389; Lane 6: UPMC1376; Lane 7: UPMC1393; Lane 8: Empty; Lane 9: UPMC703; Lane 10: UPMC704. Note\({}_{2}\): Gel electrophoresis was conducted to separate amplified DNA fragments of different lengths. The length of DNA fragments affects the speed of DNA migration through the gel pores. The stability of biological molecules is achieved with the help of the buffer that functions as a conductor as well as controls the pH. The DNA ladder containing known fragment sizes was employed to assess the length of the amplified DNA sample [52]. All the isolates (UPMC1166, UPMC1168, UPMC1254, UPMC 1389, UPMC1376, UPMC1393, UPMC703 and UPMC704) were sent to Apical Scientific for bacteria identification and are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Isolates** & **ID** & **Similarity (\%)** \\ \hline UPMC 1166 & _Bacillus methylotrophic_ & 99.0\% \\ UPMC 1168 & _Bacillus simensis_ & 99.0\% \\ UPMC 1254 & _Bacillus subtilis_ & 99.9\% \\ UPMC 1389 & _Bacillus albus_ & 99.0\% \\ UPMC 1376 & _Lactobacillus plantarum_ & 100\% \\ UPMC 1393 & _Bacillus marisfalxi_ & 98.8\% \\ UPMC 703 & _Burkholderia anthina_ & 99.4\% \\ UPMC 704 & _Burkholderia metallica_ & 98.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Identification of potential plant growth-promoting rhizobacteria (PGPR).  
erals. Areas close to industrial activities typically have poor soil quality, such as low pH, adverse carbon-to-nitrogen ratios, low organic carbon content, and low essential macronutrients [87; 88; 90]. The use of PGPRs complemented with the chemical fertilizers could enhance the soil nutrients and control plant diseases in a more sustainable and eco-friendly way [87].