esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction
## 4 Comparative Analysis  
Table 2 shows the comparison of the proposed MIMO antenna with other designs. The proposed MIMO antenna's decoupling method effectively suppresses mutual coupling at two frequencies, which can be designed to the required frequencies. This design does not require additional components and does not increase the overall dimensions of the MIMO antenna. From Table 2, it is evident that dipole or monopole antennas cannot achieve the required designable suppression frequencies; only patch antennas can. However, those patch antennas are designed for only one frequency. Therefore, only the proposed MIMO antenna can satisfy the design requirements for multifrequency coupling suppression with a monopole antenna type.  
Figure 21: The measured and simulated CCL values of the MIMO antennas with and without slits.
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 2 Literature Review  
### Overview of Design Review and Need of UAV  
During the lifecycle of a facility, it passes through different stages like planning, designing, construction, operation, and maintenance. During the design stage, it is important to review the design and carry out different design analyses so that the final design fairly meets the client's requirements. In this stage, different issues in the design are evaluated upon requirements and corrected in the design review before going into construction which helps the construction team as they don't have to commit changes. Accurate and reliable techniques should be applied in the design review to emit errors and get excellence in the accuracy of the project. This demands new techniques in the construction industry to check the feasibility of the project at the design stage rather than changing the design at the construction time which leads to an increase in the project cost and time. The BIM technique was used by [PERSON] et al. [10] in the design review process for clash detection, design evaluation, and optimization of the building design based on the evaluation that helps error minimization at the design stage. In the same way, earthworks also have the problem of accuracy and need design review techniques. Large volumes of earth are moved during earthwork, which is a significant part of the project. Exact quantities of this volume should be calculated at the design stage for an accurate estimation of the project cost. For this, achieving an accurate terrain model is important for the site design because the terrain model is compared with the design site to calculate the final quantities. A high-resolution digital elevation model is necessary to be achieved that directly affects these estimates. Many techniques can be used to get DEM as a reference for earthwork but the unmanned aerial vehicle (UAV) can be used as a choice for accurate mapping of the terrain [9]. UAV provides detailed photogrammetry of the ground that can be used to get accurate earthwork quantities in the design review.
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 2 Artificial Intelligence Technologies for Smart Greenhouses
### Current AI Technologies for Agriculture
#### 2.2.2 Water and Fertilizer Use Efficiency and Crop Yields with AI Systems
Since no experimental data contradicted the positive findings made by [PERSON] et al. [75] and [PERSON] et al. [79], it appears that the AI technologies were cost-effective and may enable farmers to achieve higher returns on investment on fertilizer application and gross returns above the fertilizer cost. The use-efficiency-related benefits that emerged from applying fertilizers were comparable to pesticide-use efficiency. [PERSON] et al. [80] reported a 50% reduction in the use of pesticides based on an initial theoretical mixture of 1000 L/ha. The introduction of computational algorithms helped to reduce the demand for pesticides to 470 L/ha. The study confirmed that the pesticide application rates could be further reduced to 300 L/ha [80]. [PERSON] et al. hypothesized a 33% reduction in pesticide use [80], which does not match [PERSON]'s research, which noted it was possible to reduce pesticide usage by up to 78% using the state-of-the-art algorithm systems developed by Israel-based Greeneye Technology [81]. Similarly, [PERSON] et al. postulated that intelligent technologies enabled farmers to practice variable rate application (VAR), which provided real-time data on the areas most infested with pests and diseases [82]. The utility of the VAR was further reinforced by the xarvio Spray Timer and SprayWeather, which enabled farmers to establish the most appropriate window for spraying. Drawing from the empirical data provided by [82], VAR eliminated the need to apply excessive fertilizers in areas least affected by pesticides, and was an ideal alternative compared to the system proposed by [PERSON] et al. [80]. The use of fewer pesticides in the production of freshly cut salads had a beneficial effect on human health, considering pesticide residues in foods were risk factors for myriad non-communicable diseases. On the downside, the benefits that accrue from DSS, VAR for pesticides, xarvio Spray Timer and SprayWeather, AI-based fertilizer, irrigation, and other systems may be less relevant to remote areas with limited IT infrastructure and technical expertise [8]. However, these challenges may not persist in the long term, considering the high pace of IT infrastructure deployment in rural and urban areas, previously classified as unprofitable for such ventures [83]. From another perspective, the deployment of IT infrastructure would only provide a partial solution, given the aging of the population was also a major issue of concern.
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 3 Results
### Skin Absorption of Veratraldehyde  
In the LC-MS/MS quantitative analysis, veratraldehyde was either not detected or detected in small amounts in blood samples collected from one control (G1) and two experimental groups (G2 and G3, 300 and 600 mg/kg percutaneous application, respectively) of Sprague-Dawley rats (Table 10). The pharmacokinetic parameters C\({}_{\text{max}}\) and T\({}_{\text{max}}\) were 0.2 (\(\pm\)0.40) ng/mL and 1.3 (\(\pm\)2.31) h for G1, 0.63 (\(\pm\)0.77) ng/mL and 24.67 (\(\pm\)47.00) h for G2 and 0.55 (\(\pm\)0.85 ng/mL) and 1.67 (\(\pm\)2.08) h for G3, respectively. The absorption pharmacokinetics of veratraldehyde into blood via the skin showed typical primary dose-dependent nonlinear kinetics.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Repellency (\%, Mean \(\pm\) SE)**} & \multicolumn{1}{c}{**CPT**} \\ \cline{2-7}  & **1 h** & **2 h** & **3 h** & **4 h** & **5 h** & **6 h** & **(\%, 95\% CI) \({}^{\text{c}}\)** \\ \hline WJ-1041 1 ** & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 98 \(\pm\) 1.2 & 96 \(\pm\) 0.7 & 85 \(\pm\) 4.4 & 5.7 (5.01–6.32) \\
10\% DEET 2  & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 99 \(\pm\) 1.5 & 98 \(\pm\) 2.1 & 91 \(\pm\) 3.1 & 78 \(\pm\) 4.3 & 5.3 (4.68–5.99) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Repellency and CPT of WJ-1041 containing 10% veratraldehyde as the active ingredient against _Aedes albopictus_ females using the indoor test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Repellency (\%, Mean \(\pm\) SE)**} & \multicolumn{1}{c}{**CPT**} \\ \cline{2-7}  & **1 h** & **2 h** & **3 h** & **4 h** & **5 h** & **6 h** & **(\%, 95\% CI) \({}^{\text{c}}\)** \\ \hline WJ-1041 1 ** & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 96 \(\pm\) 3.7 & 94 \(\pm\) 3.0 & 90 \(\pm\) 2.1 & 5.7 (5.01–6.32) \\
10\% DEET 2  & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 97 \(\pm\) 2.6 & 98 \(\pm\) 1.6 & 6.0 (0) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Repellency and CPT of WJ-1041 containing 10% veratraldehyde as an active ingredient against _Culex pipiens pallens_ females using the indoor test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multicolumn{5}{c}{**Repellency (\%, mean \(\pm\) SE)**} \\ \cline{2-5}  & **1 h** & **2 h** & **3 h** & **4 h** \\ \hline WJ-1041 1 ** & 97 \(\pm\) 2.1 & 85 \(\pm\) 5.6 & 82 \(\pm\) 5.5 & 66 \(\pm\) 5.2 \\
10\% DEET 2  & 95 \(\pm\) 2.7 & 82 \(\pm\) 5.9 & 62 \(\pm\) 6.3 & 41 \(\pm\) 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Repellency of WJ-1041 containing 10% veratraldehyde as an active ingredient against _Haemphysalis longicornis_ nymphs using a filter paper assay.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 3 Preliminaries and Background
### Secret Sharing  
Secret sharing is a key cryptographic technique for secure multi-party computation, particularly suitable for distributed computing environments [23]. It involves decomposing Bloom filter bits and computational masks into shares and distributing them across multiple computational nodes, ensuring that no single node holds complete information, thereby protecting data privacy in a distributed setting. To support different operations, this method adopts two types of secret sharing schemes:  
Boolean Secret Sharing: Primarily based on XOR operations, it is used to securely handle the bits of Bloom filters, enabling computational nodes to compute Boolean shares of the difference between two corresponding bits without revealing the original bit values, denoted as \(\left[d_{j}\right]_{B}=\left[b_{i_{j}}\right]_{B}\oplus\left[b_{j,i}\right]_{B}\).  
Arithmetic Secret Sharing: Based on modular addition, it is used for numerical computations. It securely distributes arithmetic shares of random masks \(\left[r\right]_{A}\) and constants \(\left[1\right]_{A}\) required for Boolean-to-arithmetic conversion protocols and supports secure additive aggregation of arithmetic difference shares obtained from Boolean secret sharing, i.e., computing \(\sum\left[d_{j}\right]_{A}\pmod{p}\), to derive partial shares of the final Hamming distance [24].  
Through the synergistic application of these two secret sharing techniques, this method establishes a foundation for securely and efficiently computing the Hamming distance between records without revealing the specific contents of Bloom filters.
## 2 Geometric Model, Mesh Division, and Boundary Conditions
### Calculation Method and Boundary Conditions  
CFD is used to simulate the flow and flow distribution within the 6 \(\times\) 5 parallel multi-branch assembly. According to calculation requirements, the turbulence model selected is the _k-epsilon-realizable_ model with an Enhanced Wall Treatment function [13]. Steady-state calculations are performed using the SIMPLE algorithm for the flow field. The discretization scheme for the governing equations adopts a second-order upwind format, and convergence is considered achieved when the residuals of variables in the continuity and momentum equations are below \(10^{-5}\)[14]. Turbulence models include the k-\(\varepsilon\) model, k-\(\omega\) model, Reynolds stress model (RSM), large eddy simulation (LES), and so on. The k-\(\epsilon\) model has a good effect on the fully expanded turbulence (high Reynolds number). The k-\(\omega\) model has high accuracy in the near-wall region and can analyze low Reynolds number flow without a wall function, which is suitable for boundary layer simulation. The Reynolds stress model (RSM) directly solves the Reynolds stress component and is suitable for complex flows such as strong eddy currents and rotating machinery. Large eddy simulation (LES) is suitable for boundary layer flow and is usually used in aerodynamics. This manuscript is a study of fluid flow in cold plate pipelines which mainly monitors the pressure and flow in the pipeline, and whether the flow in the pipeline is evenly distributed. The model has high accuracy in the near-wall region. It can analyze low Reynolds number flow without a wall function and is suitable for boundary layer simulation. The comparison of boundary layer research is not the focus of this paper. Therefore, the k-\(\varepsilon\) model is used in the manuscript.  
Turbulent kinetic energy (k) transport Equation (9), Turbulent dissipation rate (\(\varepsilon\)) transport Equation (10):  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho ku_{j})}{\partial x_{ j}}=\frac{\partial}{\partial x_{j}}+[(\mu+\frac{\mu t}{\sigma k})\frac{ \partial k}{\partial x_{j}}]+P_{k}-\rho\varepsilon+S_{k} \tag{9}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\  
gradient multiplied by the kinematic viscosity), unit: m\({}^{2}\)/s\({}^{3}\); t is time; \(x_{j}\) is the space coordinate (\(j\) = 1, 2, 3); \(j\) is the time-averaged velocity component (j = 1, 2, 3); \(\mu\) is Molecular Dynamic Viscosity; \(\mu\) is the turbulent viscosity (Eddy Viscosity), which is calculated by \(k\) and \(\varepsilon\), where \(C_{\mu}\) is an empirical constant. \(P\) is the production term of \(k\), which is the main source of turbulent energy and is caused by the time-averaged velocity gradient. \(S\varepsilon\) is the User-Defined Source Terms.  
Ethylene glycol is used as the working fluid, which is treated as an incompressible single-phase flow with constant physical properties. The boundary conditions are set as shown in Table 1.
## 3 Simulation Results
### Comparison with Line Voltage Stability Index  
As mentioned previously, different voltage stability indices may give different results, especially for large-scale power grids [27]. A comparison study of the results obtained by the proposed method and voltage collapse proximity indicator (VCPI) method [26] for line outage contingency ranking is demonstrated here. Note that theoretically the value of VCPI ranges from 0 to 1, and it approaches 1 when the operating point is close to the voltage-collapse point [26].  
In the simulation, all the loads including active and reactive powers in the test system are increased simultaneously based on their initial load levels. Table 9 illustrates the comparison results of the most critical lines under heavy load condition for Taipower system. In Table 9, one can see that different methods indeed give different ranking of critical lines. However, the results of the most critical lines obtained by the two compared methods are almost the same.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test System** & **Out of Service** & \(\mathbf{\lambda^{\text{max}}}\) & **VSM (\%)** \\ \hline \multirow{4}{*}{IEEE 30-bus} & G8 & 1.1638 & 16.38 \\  & G13 & 1.2144 & 21.44 \\  & Line 1-2 & 1.2575 & 25.75 \\  & Line 28-27 & 1.5173 & 51.73 \\ \hline \multirow{4}{*}{IEEE 118-bus} & G10 & 1.2387 & 23.87 \\  & G24 & 1.3142 & 31.42 \\ \cline{1-1}  & Line 18-19 & 1.5216 & 52.16 \\ \cline{1-1}  & Line 63-64 & 1.4674 & 46.74 \\ \hline \multirow{4}{*}{Taipower} & G11 & 1.1136 & 11.36 \\ \cline{1-1}  & G42 & 1.1597 & 15.97 \\ \cline{1-1}  & Line 30-100 & 1.3742 & 37.42 \\ \cline{1-1}  & Line 220-223 & 1.2961 & 29.61 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Selected Results of single contingency cases for test systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{4}{c}{**VSM (\%)**} \\ \cline{2-5}  & **ANN [15]** & **RT [16]** & **Proposed** & **Actual** \\ \hline
1 & 22.61 & 22.43 & 23.16 & 23.16 \\
2 & 18.33 & 18.26 & 18.69 & 18.69 \\
3 & 9.28 & 9.25 & 9.56 & 9.56 \\ \hline \hline \end{tabular}  
* ANN, artificial neural network; RT, regression tree.  
\end{table}
Table 8: Performance evaluation between the compared methods under the selected cases for Taipower system 1.
## 2 Materials and Methods  
### Raw Materials and Inoculum  
The coffee husk, pulp and mucilage were collected from Gomma-2 estate coffee farm (7\({}^{\circ}\)55'16.32\"N, 36\({}^{\circ}\)37\({}^{\prime}\)06.62\"E) Jimma Zone, Oromia regional state, about 400 km south-west of Addis Ababa, Ethiopia. Gomma-2 coffee farm is one of the six Limmu coffee farms. The total area of the farm is 1495 ha, of which 1118 ha are covered with coffee trees. The farm is at moderate altitude (1500-1600 m a.s.l.) and receives 1380 mm of annual rainfall. The annual temperatures of some coffee growing areas in Ethiopia are presented in Table 1[32]. It indicates the importance to investigate the bio-methane formation of coffee by-products at temperatures similar to the climatic conditions of the coffee growing areas.  
The pulp was collected immediately after pulping of the cherries. The sample was piled and divided into four quarters. The opposite quarters were discarded and the remaining quarters taken for drying (according to DIN (Deutsches Institut fur Normung) EN 14780). The nucilage was collected from the fermentation pits. The same procedures were followed for sampling. Both samples were collected every other day for a duration of a month. The samples were spread on a plastic sheet and sun dried. The husk was collected without a need of further drying. All samples underwent a size reduction to pass a 1 mm sieve and were shipped to the University of Hohenheim, Germany. The inoculum was obtained from the State Institute of Agricultural Engineering and Bioenergy, University of Hohenheim. It was cultivated in 300 L containers at 37 \({}^{\circ}\)C, and revitalized (2-5 Vol%) every 2 months with digestate from several biogas plants, under the institute's standard procedure for biochemical methane potential (BMP) assays [33]. Prior to the BMP assay, the inoculum was digested for two weeks in order to allow adaptation of the anaerobic bacteria for the specific working temperatures and degassing. The inoculum had a total solids content of 5.0% and a volatile solids content of 61.6% of total solids.
## 4 Discussion  
The magnitudes of multipath errors and noise shown in Tables 11-14, 17 and 18, are not absolute. However, we know the relative error and noise levels of the CORS stations. With the relative error/noise levels of the rover/unknown station's nearby CORS stations, we can select higher-quality CORS station(s) for baseline/network-based data processing.  
With the statistical data on the types of monument/receiver/antenna of the Hong Kong SatRef CORS stations in Tables 15-15, we have made the following observations.  
1. According to the statistical data shown in Tables 15-18 about the type of monument, stations on building roofs have lower carrier-phase noise than hilltop stations. However, building-top stations have greater pseudorange and carrier-phase multipath errors (in terms of magnitude; all frequencies) than hilltop stations, except BDS. Usually, there are more reflective surfaces/materials on the roofs of buildings, so they are more likely to have a multipath effect.
2. Based on the statistical data shown in Tables 19-12 about the type of receiver, Trimble NetR9 has lower carrier-phase noise than Leica GR50. However, Leica GR50 has a lower pseudorange and fewer carrier-phase multipath errors than Trimble NetR9. The Leica GR50 receiver may have a better multipath mitigation design in tracking loops. However, a long integration time in multipath-mitigating tracking increases the noise level [15].
3. From the statistical data shown in Tables 13-16 about the type of antenna, Leica AR25 (LEIAR25.R4 LEIT) has slightly greater carrier-phase noise than Trimble TRM59800/TRM57971. However, Leica AR25 has a lower pseudorange and fewer carrier-phase multipath errors than Trimble TRM59800/TRM57971.00. Leica AR25 antenna may have better multipath mitigation capability.  
Note that no statistics and percentage differences are given because GNSS systems have different satellite orbits and signals/frequencies; only overall performance/observations are given.  
Since there are many combinations of the types of monuments/receivers/antennas at the selected IGS stations in the USA/Canada, there is not much we can draw from the results. We can only see that AOAD/M_T NONE and NOV750.R4 NOVS antennas have more significant carrier-phase multipath errors, as shown at the DUBO, FLIN, PRDS, and SASK stations in Tables 17 and 18.  
Multipath and measurement noise are site environment dependent. Therefore, individual site environment inspection/analysis are outside the scope of this work. Station administrators should try to minimize the multipath effect and measurement noise if the values of the assessment metrics are large.
# Sea Level Estimation Based on GNSS Dual-Frequency Carrier Phase Linear Combinations and SNR
## 5 Conclusions
Ongoing and related work will focus on further improving the precision of the L4 method. For example, the sea surface roughness will be considered an influencing factor, thereby reducing bias, and improvements will be made to the temporal resolution of sea surface height retrieval. It is anticipated that this will be achieved by refining the estimation method by taking the temporal changes in sea surface height into consideration. In addition, continued work will focus on making use of multiple satellite constellations at the same time to improve the spatial resolution of the height measurements. **Acknowledgments:** We are grateful to [PERSON], China University of Mining and Technology, for help with our study of the multipath forward model. The research is jointly financially supported by NSFC (National Natural Science Foundation of China) projects under grant Nos. 41704017, 41604003, 41574013 and 41731069. SC02 GPS data were downloaded from [[ftp://data-out.unavco.org](ftp://data-out.unavco.org)]([ftp://data-out.unavco.org](ftp://data-out.unavco.org)). We thank [PERSON], from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac), for editing the English text of a draft of this manuscript. **Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived this paper. [PERSON] analyzed the data. [PERSON], [PERSON] and [PERSON] wrote the paper. All authors reviewed the manuscript. **Conflicts of Interest:** The authors declare no conflict of interest. The financially supporting bodies played no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
###### Abstract  
The performance of underwater acoustic (UWA) communication is heavily dependent on channel estimation, which is predominantly researched by simulating UWA channels modelled in complex and dynamic underwater environments. In UWA channels modelling, the measurement-based approach provides an accurate method. However, acquirement of environment data and simulation processes are scenario-specific and thus not cost-effective. To overcome such restraints, this article proposes a comprehensive simulation platform that combines UWA channel modelling with orthogonal frequency division multiplexing (OFDM) channel estimation, allowing users to model UWA channels for different ocean environments and simulate channel estimation with configurable input parameters. Based on the simulation platform, three independent simulations are conducted to determine the impacts of receiving depth, sea bottom boundary, and sea surface boundary on channel estimation. The simulations show that UWA channel estimation is greatly affected by underwater environments. The effect can be mainly attributed to changing acoustic rays tracing which result in fluctuating time delay and amplitude. With 10 m receiving depth and flat sea bottom, the channel estimation achieves optimal performance. Further study indicates that the sea surface has stochastic effects on channel estimation. As the significant wave height (SWH) increases, the average performance of channel estimation shows improvements.  
U 30 December 2018; Accepted: 23 January 2019; Published: 28 January 2019