esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proactive Geospatial Information Services (PGIS)
### Service Patterns in PGIS  
There are three service patterns defined in PGIS from simple to complex; these are direct pattern, composition pattern, and coordinating pattern. Direct pattern means that the existing geospatial data can satisfy user's demands directly via data services. The data will be sent to the user without any processing. The involved services in direct pattern are data services. For composition pattern, user's demands cannot be directly satisfied by the existing geospatial data. Additional processing by service chain is required, which aggregates small granularity services. The involved services in composition pattern include data services and processing services. Coordinating pattern indicates that for a specific user demand, there is no existing geospatial data available and processing by service chain as well as data transportation are also required. The coordinating service pattern covers the whole workflow from new data acquiring through sensor services, to data processing by service chain, and to the final data dissemination through transport network services. The involved services in coordinating pattern are sensor services, data services, processing services, and transport network services. The three service patterns in PGIS are illustrated in Figure 4.  
Figure 3: An example of PGIS application response to a fire accident.
# Enhanced Phosphorus Removal from Wastewater Using RSPRC and a Novel Reactor
## 3 Results and Discussion
### Effect of RSPRC Dosage in Reactor on Phosphorus Removal  
Figure 9 presents the experimental data on the effect of RSPRC dosage in the reactor on phosphorus removal. Interestingly, it was found that the P removal rate was almost the same as that of the former agitation experiment. However, [PERSON] and [PERSON] hold the view that the acquired absolute values for adsorption capacities from agitation experiments cannot be extrapolated to practical applications. They explained that the direct contact between the grains and solution differs and the agitation of the blast furnace slag may cause the destruction of the material, which may increase the sorption sites and thus the sorption capacity could be overestimated [35]. A possible explanation for the difference might be that the reactor realizes multi-stage series adsorption, which improves the removal rate and makes up for the effect of insufficient contact. With the increase in initial P concentration, the concentration of effluent phosphorus decreases when the dosage of adsorbent increases. If the initial P concentration is larger, the trend tends to be more obvious [30; 36]. Further analysis indicates that when the P concentration in the wastewater is reduced to a certain extent, that the phosphorus removal performance of the adsorbent in the wastewater begins to decrease. On the condition that the P concentration drops to a very low value (0.36 mg/L), the effect of the adsorbent in this study is greatly inhibited, and it is impossible to effectively reduce the P concentration in the wastewater.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Condition** & **Initial Concentration** & **Effluent Concentration** & **Phosphorus Removal** \\  & **(mg/L)** & **(mg/L)** & **Rate (\%)** \\ \hline a & 0.50 & 0.34 & 32 \\ b & 0.50 & 0.28 & 44 \\ c & 0.50 & 0.18 & 64 \\ d & 0.50 & 0.08 & 84 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effect of experimental condition in reactor on phosphorus removal.
# A Brazilian Space Launch System for the Small Satellite Market
## 2 Methodology and Launch Vehicle Configuration
### Cost Analysis
#### 2.2.2 Vehicle Fabrication Cost  
Launch vehicle production cost is based on the costs of material, processing and manufacturing, assembly and verification, engineering support and quality assurance costs [16]. These costs are applied to the fabrication of the vehicle and its engines. Total fabrication cost is further influenced by the system engineering integration factor powered to the number of stages and the productivity correction factor as shown in Equation (22).  
\[C_{fab}=f_{0}^{N_{e}}\left(\sum_{1}^{N_{e}}F_{S}+\sum_{1}^{N_{e}}F_{E}\right) f_{8} \tag{22}\]  
where,  
\(F_{S}\)--stage fabrication cost;  
\(F_{E}\)--engine fabrication cost.  
In Equation (22), unit engine fabrication cost of solid and storable (liquid) propellant rockets (\(F_{ES}\)), based on their inert mass (\(m_{E}\)), in kilogram, is calculated respectively by  
\[F_{ES} =2.3m_{E}^{0.399}f_{4} \tag{23}\] \[F_{EL} =1.9m_{E}^{0.535}f_{4} \tag{24}\]Also, in Equation (22), stage fabrication cost based on solid (\(F_{SS}\)) and storable (liquid) propellant (\(F_{SL}\)), from their inert mass (\(m_{S}\)) without engine, in kilogram, is calculated respectively by  
\[F_{SS}=0.05F_{ES} \tag{25}\]  
\[F_{SL}=1.439m_{5}^{0.593}f_{4} \tag{26}\]  
The fabrication cost of a solid propulsion stage, Equation (25), was also proposed by our group. It follows the same rationale of the solid stage development cost.
## 1 Introduction
For one farm field, the crop yield, NDVI, and LAI are intuitive reflections of soil fertility, since good soil fertility often corresponds to high crop yield, the NDVI, and LAI. Remote sensing crop yield estimation is also a vital issue in precision agriculture. Traditional crop yield estimation technologies use agro-meteorological models, or they analyze the relationship between remote sensing spectral bands or vegetation indexes (such as NDVI, EVI, GVI, SAVI, and LAI) and the field-measured yields to forecast the crop yield in large agricultural regions [46; 47; 48; 49; 50; 51; 52; 53]. However, these methods are restricted by specific crop cultivars, crop growth stages, and geographical regions [54; 55]. Crop models, such as the CERES-Wheat model [56], the WOFOST model [55; 57; 58], and the SWAP (soil-water-atmosphere-plant) model [59], are good alternatives to solve the above problems since they can simulate key physical, physiological, and soil processes, and they fully consider issues such as abnormal weather conditions and natural disasters [60]. Thus, in recent years, the integration of remote sensing technology and crop models has been the focus of crop yield estimation research [54; 57; 61; 62]. For example, [PERSON] et al. [54] developed a simple yet effective method with fast algorithms to assimilate the time-series HJ-1 A/B data into the WOFOST model, and the results demonstrated that the proposed method could improve spring maize yield simulations. meanwhile, the DEM can partly explain soil erosion and diffusion [38]. These remote sensing products are helpful in discerning representative sample locations with good feature space coverage [63; 64]. Thus, we could use these products to optimize sample locations to achieve good feature space coverage when the samples are also geographically well dispersed. In this paper, we proposed a sampling design method considering both good spatial coverage and feature space coverage to improve farm field-level soil mapping accuracy with limited sample sizes. First, the proposed method determined the optimal super-grid space by analyzing the influence of different sized super-grid spaces on soil mapping accuracy, and then, this was compared to regular grid sampling (good spatial coverage) and k-means sampling (good feature space coverage) using visual and quantitative analysis of the soil mapping. The ordinary kriging (OK) method was employed for farm field-level soil organic matter (SOM) mapping.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 3 Proposed Method
### Watermark Extraction  
#### 3.2.1 Detection of the Embedded Watermark  
We designed the proposed collusion-resilient watermarking based on informed-detection watermarking that requires a reference mesh. For the watermark extraction, we first obtained a spectral coefficient vector \(\mathbf{c}^{*}=(c_{1}^{*},c_{2}^{*},...,c_{n}^{*})^{T}\) and \(\mathbf{c}\) from the suspicious mesh \(\mathbb{M}^{*}\), and reference mesh \(\mathbb{M}\) using the method described in the embedding section. Then, we calculated the residuals vector \(\mathbf{r}^{*}\) by subtracting \(\mathbf{c}^{*}\) from \(\mathbf{c}\).  
We obtain two-dimensional vector \(\mathbf{s}^{*}=(\mathbf{s}_{1}^{*},\mathbf{s}_{2}^{*},...,\mathbf{s}_{N}^{*})\) from \(\mathbf{r}^{*}\), where  
\[\mathbf{s}_{j}^{*}=(r_{i_{0}+Nj+1}^{*},...,r_{i_{0}+N(j+1)}^{*}). \tag{6}\]  
Then, we determined whether the suspicious model was watermarked or not as follows:  
\[\left\{\begin{array}{ll}\text{Watermarked}:&\text{ if }\sum_{j}corr(\mathbf{s}_ {j}^{*},\mathbf{s}_{1})/N>\frac{\tau_{w}}{n},\\ \text{No watermark}:&\text{ otherwise.}\end{array}\right. \tag{7}\]  
where \(1\leq j\leq N\), and \(\mathbf{s}_{1}\) is the symbol vector used in the embedding step, and \(n\) is the number of attackers that can be covered by the code set \(\mathbf{N}\). Here, \(\tau_{w}\) is a threshold to minimize a false-positive rate (\(<\)\(10^{-5}\)) that was determined by experiment without any collusion attack. Because the embedded signal could be attenuated by \(\frac{1}{n}\) (in the worst case), \(\tau_{w}\) must be adjusted by the maximum number of attackers to avoid false-negative error. The function term \(corr(\mathbf{r},\mathbf{s})\) denotes the normalized correlation calculated as in the following equation:  
\[corr(\mathbf{r},\mathbf{s})=\frac{(\mathbf{r}-\mathbf{\bar{r}})\cdot( \mathbf{s}-\mathbf{\bar{s}})}{\|\mathbf{r}-\mathbf{\bar{r}}\|\cdot\|\mathbf{ s}-\mathbf{\bar{s}}\|}, \tag{8}\]  
where the bar above a symbol denotes the mean value. When the sum of correlation values is smaller than \(\frac{\tau_{w}}{n}\), we reject the suspicious model as a watermarked model whether the extracted code is attacked or not.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 4 VLC/RF Hybrid Network Applications
### Social-Environmental Applications  
VLC/RF networks have been presented as a reliable alternative to obtain the location of mobile terminals in deep indoor scenarios (tunnels, mines, and subways) where GPS has no connection. The RF network can be used to obtain the external location of the site, while the VLC network can provide the internal location of the user. Several proposals for positioning applications using VLC/RF networks have been put forward [172; 173; 174], and a minimum estimation error of 5.8 cm has been demonstrated.  
Several authors have investigated the process of power transfer from a VLC network to the nearest users [118; 119]. This power transfer allows the energy transmitted by the LED lights to be stored for later use in other transmissions. This application has demonstrated high energy efficiency for hybrid VLC/RF networks, and could have a large impact on overall energy consumption [175; 176].  
The growth and development of cities is an aspect that has been taken into account in technological research throughout history. The implementation of hybrid networks, especially VLC/RF networks, represents a key parameter to achieve efficient management of each area of the city, thereby improving the life of its inhabitants. Hybrid VLC/RF technology can allow energy optimization, reduce household consumption, organize the movement of vehicles, and increase the flow of mobile traffic, among many other aspects necessary to achieve the development of sustainable cities [83; 131; 132].  
The main advantages of hybrid VLC/RF networks over conventional RF networks are their ability to increase transmission bandwidth and alleviate congestion in RF networks. In addition, these hybrid networks can be deployed and operated satisfactorily in locations where a pure RF network is insufficient. Table 5 comparatively summarizes the performance of VLC/RF and conventional RF networks for the applications proposed in this section. The \(\star\) and \(\bigstar\) marks respectively indicate good and excellent performance considering the criteria of previous works and the corresponding performance metrics.
###### Abstract  
Additive manufacturing (AM) has a greater potential to construct lighter parts, having complex geometries with no additional cost, by embedding cellular lattice structures within an object. The geometry of lattice structure can be engineered to achieve improved strength and extra level of performance with the advantage of consuming less material and energy. This paper provides a systematic experimental evaluation of a series of cellular lattice structures, embedded within a cylindrical specimen and constructed according to terms and requirements of ASTMD1621-16, which is standard for the compressive properties of rigid cellular plastics. The modeling of test specimens is based on function representation (FRep) and constructed by fused deposition modeling (FDM) technology. Two different test series, each having eleven test specimens of different parameters, are printed along with their replicates of 70% and 100% infill density. Test specimens are subjected to uniaxial compressive load to produce 13% deformation to the height of the specimen. Comparison of results reveals that specimens, having cellular lattice structure and printed with 70% infill density, exhibit greater strength and improvement in strength to mass ratio, as compared to the solid printed specimen without structure.  
additive manufacturing; function representation; cellular lattice structure; compressive testing; strength to mass ratio
## 2 Experimental Setup
### THz System  
The optical signal at the input of the THz system is distributed to the two PCAs by a fiber optic PM 50/50 beam splitter. The PCAs are ErAs:In(Al)GaAs photoconductors, optimized for 1550 nm excitation, similar to those presented in Reference [27]. The source consists of a superlattice of 15 nm InGaAs, 1.5 nm p-InAlAs, 0.8 monolayers ErAs, and 1.5 nm p-InAlAs with 90 periods. It is connected to an H-dipole with a central dipole length of 35 \(\upmu\)m and a gap of 20 \(\upmu\)m. The receiver is a superlattice with 90 periods of 15 nm InGaAs, 1.6 monolayers delta-p-doped ErAs, and 2 nm p-InAlAs with an H-dipole of length 100 \(\upmu\)m and a gap of 10 \(\upmu\)m. The optical signal for the detector can be delayed by a translation stage (M-ILS150 CC Motorized Linear Stage, Newport). The THz system was operated under ambient air.  
The photocurrent generated at the receiving PCA is converted to a voltage with a transimpedance-amplifier (TIA) and detected by a lock-in amplifier (MFLI, Zurich Instruments). Lock-in detection is facilitated by modulating the bias voltage of the transmitting PCA with a sine wave. The sine wave is generated by a signal generator (HP8116A). A high-voltage amplifier (Tabor Electronics 9200) is used to adapt the output voltage range to the required bias voltage of the PCA (\(\sim\)120 V). The lock-in amplifier is synchronized with the signal generator.
## 4 Model Evaluation
### On-Track Comparison of FFNN with the CALIOP and PHYS Model
Under non-dust conditions, more than half of data are filtered over land and about half masked over the ocean. The remaining dusty percentage based on CALIOP is 33% over land and 8.2%  
Figure 5: Comparison of CALIOP product, FFNN prediction, and PHYS prediction. First, two stacked bar graphs show the comparison between CALIOP and FFNN. Numbers in CALIOP bar show the percentage of dust and non-dust over the total number of CALIOP data, while the numbers in FFNN bars represent the conditional accuracy of the FFNN regarding CALIOP defined dusty or clear. Three stacked bars on the right depict the comparison between CALIOP, FFNN, and PHYS under the PHYS ADP cloud mask. Panel (**a**) shows the comparison over land, while panel (**b**) shows the comparison over the ocean. over ocean. Over land, PHYS significantly under-predicts dust (39.3% from PHYS vs. 90.7% FFNN) with a similar false alarm rate as FFNN under non-dusty conditions. Over the ocean, the PHYS method shows a much higher false positive rate when compared with FFNN under non-dust conditions. As we mentioned before, CALIOP can be \"too sensitive\" to dust when very small dust components exist in the atmosphere or thin cirrus clouds present at low altitudes. However, only one condition leads to CALIOP incorrectly identifying dusty pixels to clouds, which is when the dust layer is very optically thick, which occurs mostly near the dust source region over land and very little in the transported dust layer over the ocean. Thus, the PHYS identified dusty pixels (more than half) under CALIOP non-dust conditions can be caused by mis-classifying other types of aerosols as dust. Over the ocean, PHYS has a slightly lower true positive rate when compared to FFNN. Excluding clouds, PHYS shows the true positive rate of 29% and 24% and the false positive rate of 40% and 53% for over land and ocean, respectively. The low true positive rate could be due to the different definitions applied between active sensor and passive sensor in terms of defining dust. For CALIOP, we consider an atmospheric column dusty if there is any dust observed in that vertical profile. FFNN follows the same definition since it is trained based on CALIOP data. This definition captures much more very low dust loading compared with what PHYS can do. The high false positive rate for PHYS, especially over the ocean, indicates that there is misclassification of other types of aerosols into dust by PHYS. Note again that our study excludes any cloudy scenes including thin cirrus identified by CALIOP. Under this circumstance, CALIOP has a reliable ability to separate dust from other aerosols under clear sky. Overall, FFNN can reproduce the CALIOP's dust classification with a better accuracy than PHYS when following the CALIOP dust definition which includes conditions with very low dust loading.
## 7 Conclusions  
Reliability evaluation is the critical approach to guaranteeing and maintaining the accuracy of the transposition system in the power servo tool holder of CNC. Based on system responses, the first four statistical moments are derived to depict the system characteristics. Considering Kronecker operators and Edgeworth expansion, the reliability evaluation and reliability-based sensitivity by moments are proposed to present the system accuracy and performance. Also, results calculated with the proposed method for the transposition system are in agreement with the results from the Monte Carlo simulation with 10\({}^{7}\) samples. When compared with different methods, the proposed method can represent higher statistical characteristics, in order to prevent underestimations of system reliability. Considering the plus-minus sign, reliability-based sensitivity represents the effects of the parameters' dispersions on system reliability. The sensitivity indices can be utilized to optimize the system structure and to improve system accuracy. Also, the positions of dual gear 2 and dual gear 3 are more important than those of the other gears in the transposition system of the power servo tool holder. Based on the first four statistical moments, reliability and reliability-based sensitivity can be evaluated. A complex system with a high variation coefficient can decrease the calculation accuracy, and this needs to be investigated further. The influence of the axis distance needs to be investigated in depth for reliability-based optimization. Also, the time-varying reliability and the parameters' correlations can be analyzed based on the reliability evaluation method in the future.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Before Optimization (mm)** & **After Optimization (mm)** \\ \hline \(X_{1}\) & 62.27 & 62.245 \\ \(Y_{1}\) & 198 & 198.024 \\ \(X_{2}\) & 113.18 & 113.137 \\ \(Y_{2}\) & 216 & 216.010 \\ \(X_{3}\) & 163.24 & 163.266 \\ \(Y_{3}\) & 189 & 188.969 \\ \(X_{4}\) & 116 & 115.979 \\ \(Y_{4}\) & 106 & 106.004 \\ \(R\) & 0.983795 & 0.999998 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of parameters after optimization.  
**Author Contributions:** Methodology, D.Z.; Software, G.G.; Formal analysis, Y.G.; Resources, Q.M.; Supervision, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by the Shanghai Scientific Research project (No. 22511103604) and the Fundamental Research Fund for the Central Universities (2232023D-17). Also, this project was supported by the Foundation of Key Laboratory of Vibration and Control of Aero-Propulsion System, Ministry of Education (VCAME202104), which received support from Northeastern University.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
###### Abstract  
A novel indoor positioning method for a pigsty based on ultra-wideband (UWB) technology is proposed via reasonably arranging the base stations and positioning tags, which can provide strong support for the accurate positioning of specific pig pens in the pig house. The distribution of Dilution of Precision (DOP) values is analyzed in different deployment schemes which include the amount, the height, the deployment shape, and the distance of the base stations through positioning simulation. Moreover, the positioning effect of dynamic following positioning, the root-mean-square error (RMSE), and the variance of static positioning are measured. It is noteworthy that the Kalman filtering algorithm is adopted to smooth the dynamic following positioning experimental data for reducing the positioning error. The simulated results show that the DOP values decrease with the increase in height and distance, and these results and laws are consistent with the experimental results. However, the DOP values are best reached when the height is from 1.75 m to 2.75 m, and the distance is over 3.00 m. Furthermore, the DOP values of quadrilateral deployment are smaller than the ones of triangular deployment. In addition, the variance of static positioning and dynamic following positioning is decreased by utilizing the Kalman filtering algorithm. Therefore, the excellent positioning system and its solutions in a pigsty can be provided by our proposed indoor positioning method.  
UWB; positioning; base stations; DOP; Kalman filtering +
Footnote †: journal:  
0000-0001-999-2270-2201