esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 1 Introduction
In order to improve the estimation accuracy of nonlinear systems, various improved algorithms have been designed based on KF, such as the extended Kalman filter (EKF) algorithm [29] and the unscented Kalman filter (UKF) algorithm [30]. In [31], a UWB positioning algorithm for an underground coal mine was proposed, which improved the positioning accuracy of the system by introducing the EKF algorithm. Similarly, in [32], an integrated indoor positioning system was proposed by combining IMU and UWB, in which EKF and UKF were employed to improve the positioning accuracy and expand the positioning application scene. Although UKF can effectively reduce the linearization error, the technology is extremely dependent on the initial parameter setting. The initial value setting of the parameters is adjusted manually using a trial-and-error method according to the application. However, this manual method takes more time, and sometimes the filter may diverge [33]. The accuracy of the estimated parameters of the KF technique depends, to a large extent, on the correct knowledge of the covariance matrix Q of its model noise and the covariance matrix R of the measurement noise [34; 35]. The statistics of Q and R highly influence the Kalman gain K and, consequently, the error of the measurements, where the covariance matrix R of the measurement noise represents the confidence in the filtering system on the observed values [36]. Meanwhile, the DOP values reflect the sensitivity of the positioning system to the ranging error: the larger the DOP value, the lower the positioningaccuracy of the UWB system, and the less credible the positioning output [37]. Therefore, the adaptive combination of the DOP numerical analysis model with the covariance matrix R of the measurement noise of KF can be considered to improve the positioning accuracy of the existing algorithms in the narrow space. Based on the existing research, this study designed a positioning algorithm that combines the DOP values and KF to improve the positioning accuracy of the narrow space. Firstly, the distribution characteristics of the UWB positioning error in a narrow space are systematically explored using a DOP numerical analysis model. Secondly, based on the initial positioning results acquired by the weighted least square (WLS) positioning algorithm, the DOP numerical analysis model was adaptively combined with KF to solve the problem of positioning misalignment in a narrow space. Finally, the proposed algorithm was tested on two narrow scenes with different length-width ratios. Both static and dynamic positioning results were reported in the experiments. The rest of this paper is structured as follows: Section 2 introduces the relevant mathematical principles and the details of the proposed positioning algorithm. Section 3 introduces the experiments and results. The discussions and conclusions are given in Sections 4 and 5, respectively.
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
## 6 Results and Discussion
### Power Delay Profile and Time Dispersion Results and Analysis
The RMS delay spread is less than 4 ns at 3.5 GHz; only at the farthest measurement point is the value of the RMS 7.8 ns. At 28 GHz, the RMS delay spread is less than 2 ns. This indicates that the RMS delay spread is low for the 5G system when the high directive antennas are used at Tx and Rx. This implies that a high data rate can be sent through the 5G channel with less inter symbol interference (ISI). Figure 14b shows the RMS delay spread for the NLOS scenario. It can be observed that both bands have low RMS delay spread. Moreover, the delay spreads for both frequencies are closer to each other. This indicates that, with the high frequency, the RMS delay  
Figure 14: RMS delay spread in both bands 3.5 GHz and 28 GHz for (**a**) LOS (**b**) NLOS. Figure 13: Power delay profiles measured at 3.5 and 28 GHz for a Tx-Rx separation distance of 10 m (**a**) LOS (**b**) NLOS. spread does not have any potential change, which encourages the use of the current 3 GPP channel model for 5G system with simple modifications in some parameters.
## 3 Experiment and Result Analysis
### Performance Analysis of RZTD-Constrained Medium-Long Baseline RTK
Figures 11 and 12 illustrate the performance improvements in terms of TTFF, fixed rate, vertical STD, and RMS when comparing the GPT2-constrained model with the traditional RTK model. Table 3 shows the average statistical results of 15 baselines for the four indicators. The improvement is calculated as follows:  
\[Improvement\ rate=\alpha\frac{v_{\mathrm{c}}-v_{t}}{v_{t}}\times 100\%, \tag{9}\]  
where \(v_{t}\) and \(v_{\mathrm{c}}\) are the values of these  
In terms of TTFF, comparing the results on DOY016, the performance of BDS decreased on the other three days, whereas the GPS and GPS/BDS-combined scenarios did not show apparent performance reduction. The performance of the fixed rate indicator showed a slight difference for the four days in different seasons. Compared to the results on DOY016, the performance of STD and RMS indicators on the other three days showed different levels of reduction for the GPS/BDS-combined scenario. Specifically, the performance reduction on DOY190 was most obvious. On DOY016, the average improvement of STD and RMS indicators for the 15 baselines was 41.3% and 33.1% for the GPS/BDS-combined scenario, while the corresponding values were 24.0% and 12.1% on DOY190. The atmospheric circumstance varies greatly in the summer, the precision of the empirical GPT2 model becomes uncertain, and the performance of the RZTD constrained model may also be reduced. Compared to the GPS/BDS-combined situation, only slight performance reduction was observed in the BDS-only scenario for these two indicators on DOY190. The satellite geometry structure of the BDS-only scenario was weak; with the external constraint, the performance could be greatly improved even in relatively tricky tropospheric conditions. Meanwhile, compared to STD, the performance of the RMS indicator showed a larger reduction; for example, on DOY190, the improvement of the STD indicator for Baselines 11-13 was positive for the GPS/BDS-combined situation, whereas the improvement of the RMS indicator became negative for these three baselines. It should be noted that the length of these three baselines was more than 100 km. The a priori RZTD corrections have larger uncertainty when the baseline is longer and the external troposphere constraint may result in position bias. In this case, the variance of a priori RZTD information also matters, which will be discussed in the next section. In general, the GPT2-constrained model can improve the performance of medium-long RTK in different tropospheric conditions. For BDS, the average performance improvement of TTFF, fixed rate, STD, and RMS for the four days were 30.9%, 7.5%, 52.4%, and 40.0%, respectively. The corresponding values for the GPS/BDS-combined scenario were 33.0%, 0.7%, 34.0%, and 19.8%.
## 2 Materials and Methods
### Dielectric Measurement  
Based on Equation (2), the real part of the complex dielectric constant \(\epsilon^{\prime}r\) and loss are shown in Figure 8a,b. The dielectric constant \(\epsilon^{\prime}r\) obtained from this study showed a similar pattern for both SE and PDMS samples. The dielectric constant \(\epsilon^{\prime}r\) for SE was higher than that of the PDMS samples with a similar range, between 12 and 48. The maximum difference in the dielectric constant \(\epsilon^{\prime}r\) for SE 5.5/4.5 was higher than that in PDMS 10.5/0.5  
Figure 6: Tensile strength for various mixtures and thicknesses.  
Figure 7: Elastic modulus for various mixtures and thicknesses.  
by a factor of \(\pm\)1.2. Figure 8a shows a significant drop in the dielectric constant, which corresponds to the maximum frequency for the dielectric loss shown in Figure 8b. This transition represents the frequency at which the dipole polarization is completely out of phase with the applied oscillating electric field. The relative permittivity decreases at high frequency as \(\gamma\), \(\beta\), and \(\alpha\) dispersion occurs. In the gigahertz region, \(\gamma\) dispersion occurs due to water molecule polarization, and in the kilohertz region, \(\beta\) dispersion occurs due to the polarization of cellular membranes, whereas at low frequency, \(\alpha\) dispersion is associated with the ionic diffusion of cellular membranes. Since both PDMS 10/1 and SE 5/5 samples had a high Young's modulus and tensile strength, we illustrate the dielectric constant \(\ell^{\prime}r\) and conductivity of standard PDMS 10/1 and SE 5/5 samples for a frequency between 0.2 and 19.2 GHz in Figure 9.  
Figure 8: (**a**) The real part of the complex dielectric constant \(\ell^{\prime}r\) and (**b**) the dielectric loss \(\varepsilon r^{\prime}\) for various mixtures of PDMS and SE.
## 2 Materials and Methods
### Hydrodynamic Conditions  
Due to the lack of suitable observations, the currents in the region have been characterized using the outputs of a state-of-the-art numerical model. Model velocity fields in the study area were retrieved from the Western Mediterranean Operational forecasting system (WMOP), developed and operated by the Balearic Islands Coastal Observing and Forecasting System (SOCIB; [[http://www.socib.eu/](http://www.socib.eu/)]([http://www.socib.eu/](http://www.socib.eu/)); accessed on 31 October 2021). WMOP is a configuration of the Regional Ocean Modeling System (ROMS) [43] adapted to the western Mediterranean. Its domain covers from the Strait of Gibraltar to the Sardinia Channel, with a spatial resolution of 1.8-2.2 km and 32 stretched sigma vertical levels. More details about the boundary conditions, the atmospheric and river run-off forcing, and the assimilation of sea level, water temperature, and salinity data through a local multi-modelensemble optimal interpolation scheme can be found in [PERSON] et al. [44], [PERSON] et al. [45], and [PERSON] and Mourre [46].  
Daily velocity currents data from August 2016 to March 2020, covering the region of the Menorca Channel, were retrieved from the SOCIB data repository. Then, the model near-bottom velocities were interpolated to the initial and final positions of each beam trawl sample, while the mean, the 5 th, and the 95 th percentiles of the velocity components were computed to characterize the near-bottom velocity at each of the beam-trawl positions. Mean values of velocity components and module between the initial and final beam-trawl positions were calculated.
## 3 Implementation and Results
In terms of the CPU time comparison, see Table 10.  
In the diagram below (Figure 15), we illustrate the dependence of the memory used by our algorithm of all tested instances. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Description** & \(Time_{CII}\) & \(Time_{H}\) \\ \hline TSPLIB/_pla33810_ & 25.7 m & 21.0 m (DPIO [12]) \\ TSPLIB/_pla85900_ & 4.1 h & 1.4 h (DPIO [12]) \\ Art Gallery/_mona-lisa100K_ & 2.3 h & 1.4 h and 1.1 h (ACO-RPMM [9] and Partial ACO [6]) \\ Art Gallery/_vangogh120K_ & 4.6 h & 1.9 h and 1.5 h (ACO-RPMM [9] and Partial ACO [6]) \\ Art Gallery/_venus140K_ & 4.8 h & 2.6 h and 2.1 h (ACO-RPMM [9] and Partial ACO [6]) \\ Art Gallery/_pareja160K_ & 7.7 h & 3.5 h (ACO-RPMM [9]) \\ Art Gallery/_coubert180K_ & 10.1 h & 4.5 h (ACO-RPMM [9]) \\ Art Gallery/_earring200K_ & 15.1 h & 6.0 h and 5.1 h (ACO-RPMM [9] and Partial ACO [6]) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative CPU time for the problem instances for which the other heuristics were faster. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Description** & \(Error_{CII}\) & \(Error_{H}\) \\ \hline TSPLIB/_rat783_ & 7.4\% & 19.1\% and 19.5\% (DFACO [10] and ACO-3 Opt [10]) \\ \hline ART/_Mona-lisa100K_ & 3.4\% & 5.5\% (Partial ACO [6]) \\ \hline ART/_Vangogh120K_ & 3.5\% & 5.8\% (Partial ACO [6]) \\ \hline ART/_Venus140K_ & 3.4\% & 5.8\% (Partial ACO [6]) \\ \hline ART/_Earring200K_ & 3.9\% & 7.2\% (Partial ACO [6]) \\ \hline VLSI/_dca1376_ & 7.6\% & 19.6\% (PRNN [11]) \\ \hline VLSI/_djb2036_ & 10.0\% & 23.4\% (PRNN [11]) \\ \hline VLSI/_xqc2175_ & 9.1\% & 21.4\% (PRNN [11]) \\ \hline VLSI/_xqc3891_ & 9.7\% & 21.7\% (PRNN [11]) \\ \hline VLSI/_bgb4355_ & 8.4\% & 22.8\% (PRNN [11]) \\ \hline VLSI/_xsc6880_ & 10.1\% & 21.9\% (PRNN [11]) \\ \hline VLSI/_bnd7168_ & 9.2\% & 21.7\% (PRNN [11]) \\ \hline VLSI/_ida8197_ & 7.2\% & 23.2\% (PRNN [11]) \\ \hline VLSI/_dga9698_ & 9.6\% & 21.1\% (PRNN [11]) \\ \hline VLSI/_xmc10150_ & 9.6\% & 20.3\% (PRNN [11]) \\ \hline VLSI/_xxb13584_ & 9.5\% & 23.6\% (PRNN [11]) \\ \hline VLSI/_ffn19289_ & 9.3\% & 22.5\% (PRNN [11]) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparative relative errors for some problem instances.
###### Abstract  
Airborne fungal spores have always played an important role in the spread of fungal crop diseases, causing great concern. The traditional microscopic spore classification method mainly relies on naked eye observations and classification by professional and technical personnel in a laboratory. Due to the large number of spores captured, this method is labor-intensive, time-consuming, and inefficient, and sometimes leads to huge errors. Thus, an alternative method is required. In this study, a method was proposed to identify airborne disease spores from greenhouse crops using digital image processing. First, in an indoor simulation, images of airborne disease spores from three greenhouse crops were collected using portable volumetric spore traps. Then, a series of image preprocessing methods were used to identify the spores, including mean filtering, Gaussian filtering, OTSU (maximum between-class variance) method binarization, morphological operations, and mask operations. After image preprocessing, 90 features of the spores were extracted, including color, shape, and texture features. Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results showed that the average accuracy rates for the 3 classes of disease spores using the SVM model, LR model, KNN model, and RF model were 94.36%, 90.13%, 89.37%, and 89.23%, respectively. The harmonic average of the accuracy and the recall rate value (F value) were higher for the SVM model and its overall average value reached 91.68%, which was 2.03, 3.59, and 3.96 percentage points higher than the LR model, KNN model, and RF model, respectively. Therefore, this method can effectively identify 3 classes of diseases spores and this study can provide a reference for the identification of greenhouse disease spores.  
greenhouse; disease; airborne spore; classification methods +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Impact on Seed Germination
#### 3.1.2 Relative Growth (%)
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Treatment**} & \multirow{3}{*}{
\begin{tabular}{c} **Con.** \\ **(mg/mL)** \\ \end{tabular} } & \multicolumn{3}{c}{**\% Relative Growth of Wheat Shoot and**} & \multirow{3}{*}{**Relative GR \% \({}^{*}\)**} & \multirow{3}{*}{**GI \({}^{**}\)**} \\ \cline{3-3} \cline{5-6}  & & & **Root** & & \\ \hline \multirow{4}{*}{SPs} & 0.002 & 37.01 \({}^{\text{a}}\)\(\pm\) 32.29 & 70.03 \({}^{\text{a}}\)\(\pm\) 31.80 & 103.6 & 72.62 \\  & 0.004 & 33.05 \({}^{\text{a}}\)\(\pm\) 29.82 & 65.36 \({}^{\text{a}}\)\(\pm\) 29.60 & 107.1 & 74.23 \\  & 0.03 & 16.96 \({}^{\text{b}}\)\(\pm\) 7.95 & 63.65 \({}^{\text{a}}\)\(\pm\) 22.04 & 100 & 62.43 \\  & 0.06 & 13.88 \({}^{\text{b}}\)\(\pm\) 8.24 & 41.86 \({}^{\text{bc}}\)\(\pm\) 19.82 & 100 & 41.01 \\ \hline \multirow{4}{*}{Cu NPs} & 0.002 & 20.71 \({}^{\text{b}}\)\(\pm\) 16.38 & 61.78 \({}^{\text{a}}\)\(\pm\) 24.24 & 96.4 & 59.67 \\  & 0.004 & 19.61 \({}^{\text{b}}\)\(\pm\) 18.84 & 55.56 \({}^{\text{ab}}\)\(\pm\) 34.72 & 96.4 & 53.55 \\ \cline{1-1}  & 0.03 & 17.18 \({}^{\text{b}}\)\(\pm\) 9.27 & 53.38 \({}^{\text{abc}}\)\(\pm\) 11.72 & 100 & 53.17 \\ \cline{1-1}  & 0.06 & 13.22 \({}^{\text{b}}\)\(\pm\) 9.91 & 36.72 \({}^{\text{c}}\)\(\pm\) 18.51 & 100 & 36.77 \\ \hline \hline \end{tabular}  
* \(GR=Relative\)\(germination\)\(rate=\left[\begin{array}{c}\text{Sends generated in tested sample}\\ \text{Sends generated in control}\\ \text{Sends generated in control}\\ \text{100}\\ \end{array}\right]\)
* 100; \({}^{**}\)\(Gl=Germination\)\(index=\left[\begin{array}{c}\text{Sends generated in tested sample}\\ \text{Relative generation interquartile root length}\\ \text{100}\\ \end{array}\right]\)  
\end{table} 
Table 3: Effect of SPs and Cu NPs on percentage relative growth of shoot and root lengths, germination rate and germination index of wheat seed.
## 2 Materials and Methods
### Implementation
#### 2.3.1 Searching for Regular Expressions Matches  
The application back-end begins its search by creating a regular expression object.  
Regular expressions are patterns used to search for character combinations in text strings. Regular expressions can contain various special characters and modifiers that define the pattern to search for [17].  
The magnitude of the search range directly impacts the algorithm's processing time; smaller ranges are preferred for optimal efficiency.
# Macro-Meso Mechanical Behavior of Loose Sand under Multi-Directional Cyclic Simple Shear Tests
## 4 Conclusions  
In this study, loose sand samples under different complex shear paths and directions of consolidation shear stress were simulated in bi-directional simple shear DEM models, liquefaction characteristics and corresponding meso-mechanisms were analyzed, and the following conclusions were drawn:  
1. Bi-directional cyclic shear stress accelerated the drop in vertical stress, especially in the first and last cycles, in which the FIG8 shear path led to a faster liquefaction than the OVAL and SL shear paths. In addition, as the \(\theta\) increased, samples were easier to liquefy.
2. Compared to uni-directional cyclic simple shear tests, complex shear paths and an increased \(\theta\) weakened the contact force chain, and led to unstable skeleton particles and floating particles. As a result, effective vertical stress decreased faster, and samples were easier to liquefy.
3. The mechanical coordination number decreased rapidly at the beginning and the end of shearing, and was relatively stable in the middle. The MCN was about 4.2 when samples were liquefied. Compared to uni-directional cyclic simple shear tests, the mechanical coordination number decreased faster in bi-directional cyclic simple shear tests. When \(\theta\) was increased, the number of contacts between each particle decreased, the number of floating particles increased, and the mechanical coordination number dropped faster.
4. The anisotropic tensor in samples gradually increased during loading and decreased during unloading. In addition, the magnitude of the anisotropy tensor gradually increased during shearing. Under bi-directional shear paths, the sample's skeleton structure was subjected to a greater disturbance during the initial shearing stage, caused damage to the particle skeleton, and resulted in a faster liquefaction. With increased \(\theta\), the amplitude and peak value of the anisotropy tensor increased.  
Conceptualization and writing--original draft preparation, X.D. and Y.L.; methodology and supervision, Y.L.; software, J.L.; data curation, Z.W.; validation, Z.W.; writing--review and editing R.H. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under grant No. 51708040, and the Zhejiang Provincial Natural Science Foundation of China under grant No. LY23E080004.  
Not applicable.  
The authors declare no conflict of interest.
# Exploring the Potential of Yellow Mealworm (_Tenebrio molitor_) Oil as a Nutraceuticalal Ingredient
## 2 Materials and Methods
### Antioxidant Capacity  
Antivoxidant capacity was determined by measuring DPPH radical scavenging activity and ABTS radical scavenging activity. The DPPH radical scavenging activity determination of the whole oil was performed according to the method proposed by [PERSON] et al. (2007) (2017) with some modifications. The oil sample (100 \(\mu\)L) was incubated with a DPPH ethyl acetate solution (Sigma-Aldrich (St. Louis, MO, USA) (3 mL, 0.05 mM) for 60 min in darkness at room temperature. The decrease in absorbance of the resulting solution was measured at 517 nm, with a Jasco UV-Vis Spectrophotometer (Easton, MD, USA/Madrid, Spain).  
The ABTS radical scavenging activity was measured according to modified methods (2017; 2018) to evaluate the antioxidant activity of the polar part of the oil. Briefly, 2.5 g of oil sample was taken in a centrifuge tube, 2.5 mL of methanol (Panreac Quimica S.L.U.,Barcelona, Spain) was added, and vigorously mixed with vortex and centrifuged at 3600\(\times\)\(g\) for 5 min (Orto Alresa Digicen 20-R; Madrid, Spain). The supernatant was collected and the polar extract was obtained. The extraction with methanol (2.5 mL) (Panreac Quimica S.L.U., Barcelona, Spain) was repeated three times.  
ABTS radical cation (ABTS(\({}^{\star\star}\)) Sigma-Aldrich (St. Louis, MO, USA) was previously prepared by reacting 5 mL of stock solution (7 mM) with 5 mL of 2.45 mM potassium persulfate solution and stored in darkness for 16 h. Then, ABTS(\({}^{\star\star}\) solution was diluted with methanol (A734 = 0.700 \(\pm\) 0.02). An aliquot of 100 \(\mu\)L of oil polar extract was mixed with 3 mL of ABTS(\({}^{\star\star}\) solution. The mixture was vortexed for 10 s, and the absorbance (\(\lambda\) = 734) measurement was performed after 15 min in darkness at room temperature with a Jasco UV-Vis Spectrophotometer (Easton, MD, USA/Madrid, Spain).  
In both methods, a Trolox standard curve in the range 0.1-0.4 mM was prepared, and the results were expressed as Trolox equivalent antioxidant capacity per kg (mM TEAC/kg) and as % Inhibition using the following equation:  
\[\%\text{Inhibition}\;=\;\frac{(\text{A}_{\text{b}}-\text{A}_{\text{t}})}{ \text{A}_{\text{b}}}\,\times\,100 \tag{7}\]  
where A\({}_{\text{b}}\) was the blank sample's absorbance and A\({}_{\text{t}}\) the analyzed sample's absorbance.