esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Simulation Settings
#### 4.2.2 The Localization Error Analysis of DRSL Algorithm  
As it can be seen in Figure 9, the topology of DUANs is changing overtime. Therefore, the dynamic reference selection-based self-localization algorithm is essential for the mobile network.  
Figure 8: The comparison of localization error between different reference selection scheme.  
Figure 7: The Localization result of different reference selection.  
The proposed DRSL algorithm is based on a dynamic reference selection scheme and a prediction-based localization method. The reference selection and the localization error described in the last section are set to be the initial value for DRSL algorithm.  
Figure 10 shows the trajectory of one mobile node in the network.  
Figure 11 illustrates the localization error of the mobile node in a certain time. The variance of observation error is 5.  
Figure 10: The trajectory of one mobile node in the network.  
Figure 9: The topology change of DUANs.  
Compare the localization error between the algorithm with dynamic reference selection scheme and the algorithm without dynamic reference selection scheme by doing 500 Monte Carlo experiments in Figure 12. Each experiment has a random and different initial topology.  
As can be seen in Figure 12, With different range measurement variance, the localization error of the dynamic reference selection scheme is less than that of the scheme without dynamic reference selection. Figure 12 demonstrates that the dynamic reference selection scheme can greatly improve the localization performance for DUANs with network topology changing problem.  
Compare the localization accuracy of the proposed DRSL algorithm with more different algorithms. The result is shown in Figure 13.  
Figure 11: The Localization error analysis.  
Figure 12: The localization error analysis of dynamic reference selection scheme.  
As can be seen in Figure 13, the localization error of all these algorithms in Figure 13 increase with the growth of the range measurement variance. Furthermore, the proposed DRSL algorithm has the highest localization precision among the three algorithms. This is because the proposed DRSL algorithm can both assist insufficient anchors in accomplishing the localization procedure with high precision and solve the topology changing problem in DUANs with mobile sensor nodes. The MASL algorithm is an anchor-free algorithm which can work with insufficient anchors; however, it cannot be adapted to the mobile sensor network environment and will be influenced by the low transmission rate in underwater environment dramatically which leads to the highest localization error consequently.
# On-Axis Optical Bench for Laser Ranging Instruments in Future Gravity Missions
###### Abstract
The OB design, based on a lens retroreflector proposed by [PERSON] [11], and the design based on a corner cube retroreflector proposed by [PERSON] et al. [10], are both examples of on-axis topologies. For optical benches of space-borne interferometers with baselines longer than \(10^{6}\,\mathrm{m}\), such as those proposed for gravitational wave detection missions, the on-axis OB topology is the scheme of choice. In this paper, we present the design of an on-axis OB for laser ranging in future gravity missions. Our design presents the following features:  
* Two quadrant photoreceivers in balanced detection configuration capture the length and angular signals of the RX-LO heterodyne interference. * Five two-lens imaging systems using a total of four lenses (each lens is shared by more than one system) serve as beam compressors for the LO and RX beams, and beam expander for the TX beam, enhancing the light collecting area and decreasing the TX beam's far-field divergence. * A fast steering mirror actuates on the tip and tilt of the LO beam using the differential wavefront sensing signals of the RX-LO beatnote, maximizing the heterodyne efficiency of the interferometer, thus yielding phase signals with optimal signal-to-noise ratio, regardless of the attitude of the local S/C. * The RX and LO imaging systems minimize the RX and LO beam walk at the photodiodes irrespective of the attitude of the local satellite, greatly reducing the coupling of attitude jitter to the inter-satellite range measurement, and keeping the detection system in a stable operating point. * The TX imaging system, in combination with the actuation of the fast steering mirror on the locally generated beam, ensures accurate TX beam pointing to the remote S/C, eliminating the need for a retroreflector. * The imaging system magnifications can be tuned to flexibly adjust the size of the beams to match the RX/TX aperture, as well as the active area of the photodiodes. The paper is structured as follows: In Section 2, we detail the design of the proposed OB. In Section 3, we present the computer model of the optical bench and the results of simulating S/C angular motion, including an account of thermoelastic effects and refractive index variations due to in-orbit temperature fluctuations. Finally, in Section 4 we summarize our findings.
# Yeast Starter Culture Identification to Produce of Red Wines with Enhanced Antioxidant Content
## 2 Materials and Methods
### TEAC Antioxidant Capacity Determination  
The Trolox equivalent antioxidant capacity (TEAC) assay is based on the scavenging ability of antioxidants to quench the radical cationic activity of 2,20-azinobis (3-ethylbenzothiezolone 6-sulphonate) (ABTS\({}^{+}\)). The assay was performed as previously described [31] with some modifications. To generate the ABTS\({}^{+}\) radical cation, ABTS was dissolved in water (7 mM) and incubated with 2.45 mM potassium persulfate (final concentration) in the dark at room temperature for 12-16 h before use. For the calibration curve, the ABTS\({}^{+}\) solution was diluted with water to an absorbance value of 0.70 (\(\pm\)0.02) at 734 nm and mixed with 20 \(\upmu\)L of Trolox standard solutions (from 0 to 25 \(\upmu\)M). The assay was performed with extracts from wine, and absorbance was determined at 734 nm. Values were expressed as \(\upmu\)mol Trolox equivalents (TE)/L.
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## 5 Discussion  
Normalized MODIS TOA reflectance takes the place of the original MODIS TOA reflectance in the proposed method. Taking the cross-calibration of PMS using MODIS reference sensor over the Golmud test site as an example, the uncertainty of this paper is analyzed as below.
## 2 Analytical Method
### Springback Prediction  
#### 2.4.1 Elastic Unloading  
In elastic springback, the unloading process, which causes an equal value of moment in the opposite direction, is assumed to be elastic. Therefore, as shown in Figure 2 the unloading path in the moment-curvature diagram is a straight line with the same slope of the elastic region (\(m=\partial M/\partial\left(\frac{1}{R}\right)\)) from the \(M=M^{*}\) to \(M=0\), where \(M^{*}\) is the bending moment at the instant of unloading. Moreover, the magnitude of the springback is considered as the curvature change from the unloaded centreline curvature as shown in Figure 2.  
Hence, the equation for the elastic unloading of applied \(M\) is expressed as:  
\[M_{U}^{E}=M^{*}=\int_{-t_{0}/2}^{t_{0}/2}E^{\prime}(\frac{y}{\Delta r})ydy=\frac {1}{12}E^{\prime}t_{0}^{3}\frac{1}{\Delta r} \tag{29}\]  
where 1/\(\Delta r\) is the curvature change due to the unloading (springback) and \(M_{u}{}^{e}\) is the elastic unloading moment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Material** & **Loading Direction** & **Tensile Yield Point, \(\sigma\)t** & **Compressive Yield Point, \(\sigma c\)** & \(c\) & \(\gamma_{t}\) & \(\gamma_{c}\) \\ \hline DP980 & RD & 675 & 710 & \(-\)0.196 & 1 & \(-\)1.052 \\  & TD & 706 & 738 & \(-\)0.172 & 1 & \(-\)1.045 \\  & RD & 335 & 260 & 0.942 & 1 & \(-\)0.776 \\ SUS304 & TD & 360 & 390 & \(-\)0.310 & 1 & \(-\)1.083 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters in uniaxial conditions.  
Figure 2: Curvature change of the moment-curvature diagram during elastic unloading.
## 3 Data Collection and Observation Quality Assessment
### Carrier-to-Noise Ratio  
Before the positioning accuracy analysis, the C/N\({}_{0}\), multipath errors and satellite availability of GPS and BDS satellites in both the static and kinematic tests are presented in Figure 4. The C/N\({}_{0}\) ratio is an important indicator to show the noise level of the original GNSS observations, and it can be used to identify the quality of GNSS measurements. A larger C/N\({}_{0}\) value means a higher-quality GNSS measurement. Figure 4 shows the variations in C/N\({}_{0}\) ratios of these visible GPS and BDS satellites during the static experiment and dynamic experiment, and the corresponding statistical results are listed in Table 3. Accordingly, the minimum, maximum, and average values of C/N\({}_{0}\) for these observed GPS satellites are 10.94 dB-Hz, 49.85 dB-Hz, and 37.33 dB-Hz, respectively, which are generally  
Figure 3: Reference trajectory for the dynamic experiment.  
Figure 2: Equipment (MI8, PANDA GNSS receiver, POS320, GNSS antenna, and MI8) (**a**) and the sky-plots of GPS and BDS (**b**) in the dynamic experiment.  
higher than those of BDS satellites in the static experiment. Similarly, in the dynamic experiment, the average C/N\({}_{0}\) of the GPS satellite is also higher than the average C/N\({}_{0}\) of these BDS satellites, with the corresponding values of 40.46 dB-Hz of GPS and 34.27 dB-Hz of BDS.
## 2 Materials and Methods  
### Different Configurations of the HD System  
In the first stages of configuring the SBS for efficiency, the system is checked with an HD system, as shown in Figure 1. The design was set so that the effectiveness of the freshwater output of the solar multiple condensation evaporation cycle (SMCEC) can be improved [16,17]. In the first stages, the system is altered by accommodating the following segments:  
1. The air solar collector (ASC);
2. The water solar collector (WSC);
3. The evaporation tower;
4. The humidifier;
5. The condensation tower.  
Each component had its unique purpose in the system. The system based on the first configuration is shown in Figure 1. The seawater is preheated in the system, which in turn reduces the overall heat usage in the system, one of the reasons that it is improved. Therefore, the system can be used when the thermal energy can be limited, and thepreeheating heat is recovered in other stages of the system. The water heating process follows several processes so that the final product can be collected with the utmost efficiency. The existing and incoming hot and cold air balance the working of the system so that the water heating and the collection process are successful.  
The first configuration is characterized by the use of collectors of water and air for the respective heating of water and air simultaneously so that the performance of the system in terms of thermal efficiency can be increased. Moreover, the system operates under reduced pressure to lower the evaporation temperature and thus limit the fouling and corrosion phenomena, which increase with the temperature. In all designs, the fresh water is gathered from the base of the buildup tower, while the saline solution at the base of both the humidifier and dissipation tower will be either reused and joined with the input solution at the section point or dismissed if there should be an occurrence of increment of saltiness rates.  
The flexibility of the first configuration of the HD solar desalination system allows us to derive two other settings (operating scenarios) depending on weather conditions and the season of the year:  
1. For days with low sunshine intensity and to avoid condensation of water vapor at the solar air collectors, the second configuration of the solar desalination system, presented in Figure 2, is recommended. This scenario includes a field of the collectors of solar water with a humidifying module and the distillation module. In this scenario, all the energy needed for evaporation is provided by solar water collectors.
2. For days with high solar radiation intensity, the first, second, and third configurations presented in Figures 1-3, respectively, can be used. In this case, the amount of thermal energy required for evaporation is supplied by the field of solar water collectors (second configuration) or by the field of solar air collectors (third configuration) or by both (first configuration).  
Figure 1: First configuration.
# Environmental Behavior and Remediation Methods of Roxarsone
## 2 Ecological Hazards of ROX
### Enrichment of Arsenic Supplied by ROX  
Introducing ROX to the environment in livestock manure has been found to affect the soil microbial community structure and diversity and to lead to arsenic becoming enriched in plants. When livestock are fed ROX and manure containing ROX is applied to farmland, the arsenic contamination sequence will be ROX in feed\(\rightarrow\)animal\(\rightarrow\)animal feces\(\rightarrow\)soil\(\rightarrow\)crops. ROX or \(p\)-arsanilic acid can markedly decrease the heights rice plants grow to, the effective number of tillers, the mass of straw produced, and the yield of rice grains and cause the rice grains to contain arsenic at contents that can pose risks to human health [27]. Roots were found to be more sensitive than above-ground plant tissues to stress from ROX because the ROX content of the plant roots were higher than the ROX content of the above-ground plant tissues [28]. The median effective concentration for inhibition of wheat root elongation by ROX is about 20 mg/L [29]. Different forms of arsenic can accumulate to different degrees in plants and different plant organs. ROX metabolites (DMA, As(III), and As(V)) have been found to accumulate in plants (turnips and water spinach), but ROX and MMA were not found to be absorbed by the plants. Only As(III) was detected in garland chrysanthemum shoots, but both As(III) and As(V) were detected in the roots [30; 31; 32; 33]. Applying phosphorus or nitrogen fertilizer to farmland can increase arsenic absorption by crops, but adding Fenton reagents to soil can decrease arsenic absorption and enrichment in rice [34]. Soil containing abundant free iron oxides and/or with a high pH causes straight-head disorder in rice to be more likely when ROX is present [35].
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 4 Experiments
### Comparison on Clothing and Carrying Status Variations  
In this subsection, we evaluated the proposed methods (combined with two post-processing techniques, denoted as Gabor+Global GERF [20] and Gabor+SD-GERF) against clothing and carrying status variations on OU-TD-B, OU-LP-Bag \(\beta\) and CASIA-B (in case of side-view). OU-TD-B and  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**OU-TD-B**} & \multicolumn{2}{c}{**OU-LP-Bag \(\beta\)**} \\ \hline
**Methods** & **z-EER** & **Rank-1** & **z-EER** & **Rank-1** \\ \hline GEI [11] & 16.12 & 52.8 & 19.59 & 24.6 \\ GEnI [12] & 12.81 & 59.0 & 18.82 & 29.5 \\ Masked GEI [13] & 28.15 & 28.0 & 61.95 & 0.1 \\ Global GERF [20] & **11.68** & **60.6** & **16.22** & 33.5 \\ SD-GERF (proposed) & **7.41** & **71.5** & **12.99** & **36.1** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equal error rate (EER) with z-normalization (denoted as z-EER) [%] and rank-1 identification rate (denoted as Rank-1) [%] for intensity transformation-based methods. Digits with bold and italic bold fonts represent the best and the second-best results, separately. This convention is consistent throughout the whole paper.  
Figure 6: Detection error tradeoff (DET) curves with z-normalization (**left**) and cumulative match characteristic (CMC) curves (**right**) for intensity transformation-based methods in two datasets.  
OU-LP-Bag \(\beta\) are more difficult compared with CASIA-B since they have more variations, thus they are detailed analyzed both in identification and verification scenarios.
## 4 Experiments
### Experimental Setup  
Using the procedure above, we extracted triplets from each of the three documents. The statistics related to this procedure are shown in Table 4. For example, from the original United Nations, United States, and Philadelphia documents we extracted a number of 183, 425, and 496 unique subjects and objects. These words are connected by 30, 820, and 893 actions to form the triplets. When we further processed the generated triplets, the number of unique concepts typically declined due to abstraction. An exception to this trend was when we matched words into the SUMO ontology. This is because a single word can be mapped into multiple SUMO concepts. For example, in the triplet _organization reduce cost_, the subject _organization_ is mapped into two SUMO concepts: _Organization_ and _Managing_ with different degrees of confidence. At the same time, the object _cost_ is also mapped into two SUMO concepts: _Process_ and _Attribute_, also with different degrees of confidence. As a result of this step in our process, we added two concepts and three relationships.  
Also, data in Table 4 shows the process of abstraction of textual information in the three documents. For example, in the original text, words in the United Smart Cities text from United Nation were only 16.6% of the total number of words in the three documents and only 2.7% of these words were found in the other two documents. As we processed these documents, the percentage of concept overlap over documents increased to 37% after mapping words into the SUMO ontology, 49.5% after abstracting the SUMO concepts to level 5, and 55.2% after mapping the SUMO concepts into the SUMO subject files. The number of relationships/triplets also decreased but at a lower rate since relationships are not mapped in the SUMO ontology but rather into the WordNet lexical classes. This reduction in semantic graph elements will enable us to assess the differences and similarities between the three documents.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Stage** & \begin{tabular}{l} **United Nations** \\ **Concepts** \\ \end{tabular} & \begin{tabular}{l} **United States** \\ **Triplets** \\ \end{tabular} & \begin{tabular}{l} **Philadelphia** \\ **Concepts** \\ \end{tabular} & \begin{tabular}{l} **Triplets** \\ \end{tabular} & \
\begin{tabular}{l} **Concepts** \\ \end{tabular} &
\begin{tabular}{l} **Triplets** \\ \end{tabular} \\ \hline Original & 183 & 301 & 425 & 820 & 496 & 893 \\ Inflected & 161 & 285 & 339 & 802 & 406 & 862 \\ Replace Action & 161 & 269 & 339 & 767 & 406 & 819 \\ SUMO & 167 & 967 & 297 & 2177 & 327 & 2501 \\ SUMO Level 5 & 88 & 997 & 140 & 1820 & 150 & 2066 \\ SUMO Files & 16 & 63 & 18 & 137 & 21 & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistics for the triplets extracted from the three documents. For each of the processed document, we calculated (1) the unique count of words/concepts and (2) the unique count of relationships between concepts.
# Synthesis and Features of Luminescent Bromo- and Iodohectorite Nanolay Materials
###### Abstract  
The smectites represent a versatile class of clay minerals with broad usage in industrial applications, e.g., cosmetics, drug delivery, bioimaging, etc. Synthetic hectorite Na\({}_{0.7}\)(Mg\({}_{5.5}\)Li\({}_{0.3}\))[Si\({}_{5}\)O\({}_{20}\)](OH)\({}_{4}\) is a distinct material from this class due to its low-cost production method that allows to design its structure to match better the applications. In the current work, we have synthesized for the first time ever nanoclay materials based on the hectorite structure but with the hydroxyl groups (OH\({}^{-}\)) replaced by Br\({}^{-}\) or I\({}^{-}\), yielding bromohectorite (Br-Hec) and iodohectorite (I-Hec). It was aimed that these materials would be used as phosphors. Thus, OH\({}^{-}\) replacement was done to avoid luminescence quenching by multiphonon de-excitation. The crystal structure is similar to nanocrystalline fluorohectorite, having the d\({}_{001}\) spacing of 14.30 A and 3 nm crystallite size along the 001 direction. The synthetic materials studied here show strong potential to act as host lattices for optically active species, possessing mesoporous structure with high specific surface area (385 and 363 m\({}^{2}\) g\({}^{-1}\) for Br-Hec and I-Hec, respectively) and good thermal stability up to 800 \({}^{\circ}\)C. Both materials also present strong blue-green emission under UV radiation and short persistent luminescence (ca. 5 s). The luminescence features are attributed to Ti\({}^{3+}\)/Ti\({}^{\rm IV}\) impurities acting as the emitting center in these materials.  
Keywords:bromohectorite; iodohectorite; clay materials; host lattice; titanium luminescence +
Footnote †: journal: Applied Physics Letters
## 2 Related Work
### Public Key Infrastructure (PKI)-Based  
The rationale behind PKI-based is primarily to preload many anonymous certifications and the relevant pair of keys (private/public keys) to each enrolled vehicle in advance. Many researchers [9; 10; 11; 12; 13; 14; 15; 16; 17] have proposed the PKI-based scheme for satisfying privacy and security in vehicular networks. However, the three main limitations of this approach are (i) huge certification management burden for TA, since massive numbers of anonymous certifications and the relevant pair of keys are required; (ii) storage management burden due to the small storage capacity of the vehicle; and (iii) low efficiency of the system, since the certificate is also verified in the verification method.
## 3 Filtering Method
### Filtering Algorithm  
As shown in Figure 2, two rows of sensors are arranged at intervals along the y direction below a yoke to detect magnetic fields in the x and z directions, respectively. The distance between the two rows of magnetic sensors is \(\sqrt{a^{2}+z_{0}^{2}}\). The two magnetic sensors Sx[i] and Sz[i] (i = 1, 2,..., N) are a pair, and there are N pairs of magnetic sensors. The filtering steps are as follows.  
1. Each sensor output is sampled, and the number of sampling points is denoted by M. The sampling results of Sx[i] and Sz[i] are array Sx[i,j] and Sz[i,j] (j = 1, 2,..., M).
2. The array Rx[i,k] and Rz[i,k] is calculated: \[\text{R}_{\text{x}}[\text{i},\text{k}]=\left\{\begin{array}{c}\text{S}_{ \text{x}}[\text{i},1]\ 1\leq\text{k}\leq\text{B}\\ \text{S}_{\text{x}}[\text{i},\text{j}]\ \text{B}<\text{k}\leq\text{M}+\text{B}\\ \text{S}_{\text{x}}[\text{i},\text{M}]\ \text{M}+\text{B}<\text{k}\leq\text{M}+ 2\text{B}\end{array}\right.\] \[\text{R}_{\text{z}}[\text{i},\text{k}]=\left\{\begin{array}{c}\text{S}_{ \text{z}}[\text{i},1]\ 1\leq\text{k}\leq\text{B}\\ \text{S}_{\text{z}}[\text{i},\text{j}]\ \text{B}<\text{k}\leq\text{M}+ \text{B}\\ \text{S}_{\text{z}}[\text{i},\text{M}]\ \text{M}+\text{B}<\text{k}\leq\text{M}+ 2\text{B}\end{array}\right.\] \[\text{q}\] is a loop variable with an initial value of B + 1.
3. MAX[i,q] = MAX[Rx[i,p] - Rz[i,p]] (p = q - B, q - B + 1,..., q + B).
4. The minimum in MAX[...q] (i = 1, 2,...,N) is found. The number of the pair that has the minimum is denoted by i0. The sampling point of the pair sensors are taken as the reference signal. Rx[i,q] - Rx[i0,q], Rz[i,q] - Rz[i0,q] (i = 1, 2,..., N): The differences are taken as the filtering results of each sensor at this sampling point.
5. \(\text{q}=\text{q}+1\). If q \(\leq\text{M}+\text{B}\), the process returns to step 3, otherwise the filtering ends.