esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Minimum Inhibitory Concentrations (MIC) and Minimum Fungicidal Concentrations (MFC)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Thyme**} & \multicolumn{2}{c}{**Lemon**} & \multicolumn{2}{c}{**Geranium**} & \multicolumn{2}{c}{**Basil**} & \multicolumn{2}{c}{**Clove**} & \multicolumn{2}{c}{**Cinnamon**} \\ \hline
**\% (r/r)** & **mg/mL** & \(n\)** & **mg/mL** & \(n\)** & **mg/mL** & \(n\)** & **mg/mL** & \(n\)** & **mg/mL** & \(n\)** & **\% (r/r)** & **mg/mL** & \(n\) \\ \hline \(<\)0.005 & \(<\)0.046 & 11 & \(<\)0.043 & 27 & \(<\)0.044 & 0 & \(<\)0.048 & 7 & \(<\)0.053 & 2 & \(<\)0.002 & \(<\)0.021 & 43 \\
0.005 & 0.046 & 0 & 0.043 & 0 & 0.044 & 0 & 0.048 & 0 & 0.053 & 0 & 0.002 & 0.021 & 1 \\
0.010 & 0.092 & 6 & 0.085 & 5 & 0.089 & 0 & 0.096 & 1 & 0.105 & 0 & 0.004 & 0.041 & 5 \\
0.020 Among all tested EOs, cinnamon oil exhibited the highest antifungal activity against isolates of both species with MIC and MFC values in the range of 0.002 (or less) to 0.125% (\(v/v\)), followed by oil obtained from _Thymus vulgaris_ (MIC and MFC values \(\leq\) 2.5% (\(v/v\)) against all strains tested). Within the tested range of concentration--up to 2.5% (\(v/v\)) the single strains resistant to the activity of other oils were identified (Tables 1 and 2). In the case of cinnamon oil most of the tested isolates of _C. albicans_ (\(n=114\); 62.3%) revealed MIC below the concentration 0.002% (\(v/v\)) and respectively for _C. glabrata_ that was 56.6% (\(n=43\)). The modal of MIC values for thyme oil was 0.08% (\(v/v\)), which was observed for 31 isolates (16.9%), however the growth of 30 isolates was inhibited at the concentration of this product lower than 0.005% (\(v/v\)). The most frequent MIC value of geranium and clove oil was 0.16% and in the case of lemon and basil oils 0.313% (_C. albicans_). In the case of _C. glabrata_ that was 0.005% for lemon oil, 0.16% for thyme, geranium, and clove and 0.313% for basil oil. Considering fungicidal activity, the MFC concentration was mostly equal to or twice the MIC value (Tables 3 and 4). Only in some cases MFC was much higher than MIC.
## 5 Experimental Setup and Results Discussion
### TCA Localization Test  
To reduce testing complexity and improve measurement accuracy, we placed the tag on the ceiling plane of the laboratory. The plane measures 732 \(\times\) 488 cm, and its coordinate system (X, Y) is defined as a 12 \(\times\) 8 grid, with each square measuring 61 \(\times\) 61 cm. Subsequently, three base stations were placed at the origin (0, 0), on the X-axis at (12, 0), and on the Y-axis at (0, 8), as shown in Figure 10. In the figure, the black dots represent the actual positions of the tags, while the blue, red, and green dots indicate the positions of the base stations. The corresponding blue, red, and green circles are drawn using the measured distance values from each base station as the radius. The overlapping area in the center represents the region where the theoretical tag position is most likely to appear based on the TOA method. The cross mark denotes the estimated position calculated using the center of gravity positioning method.  
The experimental results indicate that when the tag is closer to the origin, the intersection of the three circles can be approximately represented as a triangle. For instance, when  
Figure 8: One-to-one barrier-free distance measurement.  
Figure 9: Calibration of localization error.  
positioning the coordinates (3, 6), the coordinates estimated using the triangle centroid algorithm are (2.73, 5.70), resulting in an actual error of approximately 24.30 cm. When positioning the coordinates (5, 3), as shown in Figure 10, the experimentally determined coordinates are (4.97, 2.99), corresponding to a positioning error of about 1.92 cm. Similarly, for the coordinates (7, 3), the calculated position is (6.78, 2.76), yielding an actual error of approximately 19.95 cm.  
However, when the tag is placed at positions with large X and Y coordinate values--far from the origin--such as (7, 5), (10, 7), (11, 5), (0, 8), and (12, 0), the intersection area of the circles becomes elongated and narrow, making it difficult to approximate a triangular shape. For example, when positioning the coordinates (7, 5), as shown in Figure 11, the triangle centroid algorithm yields a result of (6.43, 4.12), corresponding to an actual positioning error of approximately 63.68 cm. When positioning the coordinates (10, 7), the experimentally determined coordinates are (8.34, 4.39), with an error of about 188.45 cm. Similarly, for the coordinates (11, 5), the calculated position is (9.73, 2.98), resulting in an error of approximately 145.55 cm. These results demonstrate that when both X and Y coordinate values are large, the triangle centroid algorithm becomes ineffective, leading to significant positioning errors. Detailed experimental results for 10 coordinate points within the positioning space are summarized in Table 1.  
Figure 11: Test of tag local at (7, 5) using the TCA method.  
Figure 10: Test of tag local at (5, 3).
## 2 Design of the All-in-One Collimating Splitter
### Design of the Phase Profile
The optimized phase profile of the Dammann grating (\(\phi_{D}\)) is shown in Figure 2b. The inset shows an enlarged phase profile of one supercell of the Dammann grating, which has C4 and mirror symmetry as previously defined. By synthesizing the phase profile of the collimating lens (\(\phi_{L}\)) and the Dammann grating (\(\phi_{D}\)), the profile of the final collimating splitter (\(\phi\)) is derived, as shown in Figure 2c. Figure 2: The mechanism of the all-in-one collimating splitter based on metasurfaces. The normalized phase profile of (**a**) the collimating lens (\(\phi_{L}\)), (**b**) Dammann grating (\(\phi_{D}\)) and (**c**) the synthesizing metasurface (\(\phi\)), respectively. The inset of (**b**) is the enlarged view of the Dammann phase profile. The size of the designed metasurface is 80 \(\upmu\)m \(\times\) 80 \(\upmu\)m, covering the effective area of the incident Gaussian beam. The designed wavelength is 1.55 \(\upmu\)m.  
The final results are characterized by the total diffraction efficiency (\(\eta\)) and uniformity, which is defined as the standard deviation (\(SD\)):  
\[\eta=\sum_{i=1}^{N}I_{i},\ SD=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(I_{i}-\frac{ \eta}{N}\right)^{2}}, \tag{5}\]  
The final efficiency and uniformity of the Dammann grating shown in Figure 2b reach 96.27% and 0.011642%, respectively. The diffraction efficiency is calculated by integrating the intensity at the corresponding diffraction order within the directional cosine radius of 0.05 in the angular spectrum. Thus, the total value (\(\eta\)) can be slight deviated from unity.
## 5 Results
### LSMs by Individual Models  
In the first phase of analysis, landslide susceptibility maps (LSMs) were produced applying the single-model based assessment method. Therefore, four different LSMs of the Rudraprayag district were produced by random forest, artificial neural network, support vector machine and logistic regression models individually. All the models were functioned in 'R studio' using R programming along with SPSS v17. For all the models, the susceptibility was categorised into five classes based on [PERSON]'s natural breaks classification algorithm such as Very-high (VH), High (H), Moderate (M), Low (L) and Very-low (VL). For the classification of LSMs into VH, H, M, L, and VL, four techniques were tested, i.e., geometric interval, natural breaks, equal interval, and quantile. The calibration results provided by the [PERSON]'s natural break are more prominent in distributing landslides as it has no bias and minimum deviation in intra-class and maximum deviation in inter-class which was also found in the work of [PERSON] et al. [78].  
RF model based LSM (Figure 5b) is categorised into 18.68%, 15.85%, 17.63%, 17.91%, and 29.93% area of the district under VH, H, M, L, and VL classes, respectively. Random forest was fabricated by the relative weight of the mean decrease in accuracy of the Gini index. The ANN model produced 21.59%, 15.08%, 18.93%, 18.29%, and 26.11% areas are susceptible under VH, H, M, L, VL categories, accordingly (Figure 5a). Similarly, the LR model-based result categorized 23.62% area of the district as very highly susceptible, 13.31% as highly susceptible and 15.15%, 18.52%, and 29.38% areas as moderate, low, and very-low category, respectively (Figure 5c). In the case of the SVM model-based LSM (Figure 5d), the very-low (25.93%) and moderate (16.81%) susceptibility class cover the largest areas of the district respectively followed by very-high (23.83%), high (14.38%) and low susceptibility (25.93%) classes. The comparative visualization of the individual model based LSMs are represented in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sl. No** & **LCFs** & **Average Merit (AM)** \\ \hline
1 & Altitude & 0.05082 \\
2 & Drainage density & 0.03908 \\
3 & Road density & 0.03446 \\
4 & Earthquake zone & 0.03378 \\
5 & Distance to lineaments & 0.03232 \\
6 & Slope gradient & 0.02895 \\
7 & LU/LC & 0.02478 \\
8 & Geology & 0.02399 \\
9 & Rainfall & 0.02313 \\
10 & Soil depth & 0.02191 \\
11 & Soil type & 0.01946 \\
12 & NDVI & 0.01659 \\
13 & Curvature & 0.01383 \\
14 & TWI & 0.00861 \\
15 & Slope aspect & 0.00338 \\
16 & SPI & 0.00326 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average merit of the LCFs in the modelling.  
Figure 5: Landsslide susceptibility mapping using single models: (**a**) ANN (**b**) RF, (**c**) LR and (**d**) SVM.
## Results and Discussion
### Monitoring of Oxygen Consumption of E. coli  
_E. coli_ was used as a biological model in the experiment to measure the oxygen consumption of bacterial cells. According to the literature, _E. coli_ has a shorter generation time, which is distributed approximately 20-90 min depending on temperature and cell density [34]. This makes it easier to detect changes in the extracellular oxygen concentration of _E. coli_. During the growth of _E. coli_, oxygen is consumed by aerobic metabolism. In the experiment, a layer of mineral oil was added to the upper layer of the LB medium to reduce the gas exchange between oxygen and the outside air. In order to observe the changes in the oxygen consumption rate at different concentrations, the initial cell densities of _E. coli_ JM109 were set to \(5\times 10^{6}\), \(2.5\times 10^{6}\), \(1.3\times 10^{6}\), \(6.3\times 10^{5}\), and 0 cfu/mL, respectively, and the concentration of PtTFPP was 3 \(\mathrm{\SIUnitSymbolMicro\mathrm{g}/mL}\) (2.56 \(\mathrm{\SIUnitSymbolMicro\mathrm{m}}\)M). The experiment was carried out in a 96-well plate at 37 \(\mathrm{\SIUnitSymbolCelsius}\), and its phosphorescence intensity was detected at an excitation wavelength of 405 nm by a microplate reader (Figure 8). Even when the concentration of _E. coli_ was as low as \(6.3\times 10^{5}\) cfu/mL, a significant change in phosphorescence intensity was observed after 80 min due to the aerobic metabolism of _E. coli_ in LB medium. As the initial _E. coli_ density increases, the completeness of oxygen consumption becomes faster and faster.  
Figure 7: Reversibility and response time tests of the micelles **M1 (A)**, **M2 (B)**, and **M3 (C)** with PtTFPP under the excitation at 405 nm through the purging of nitrogen and oxygen.  
Mineral oil seals are commonly used to measure oxygen consumption and cell acidification during cell growth. The experiment also studied the effect of oil seals on the rate of oxygen consumption. The experiment was conducted in two parallel experiments under comparable conditions, one with an oil-free test and one with an oil-sealing test (Figure S3). The results showed that the time of depletion of oxygen in the oil-sealing group is shorter than that in the oil-free group, indicating that oil sealing can effectively isolate the oxygen exchange between the medium and the outside air, which is essential for completing the experiment in a shorter amount of time.
# A Novel Signal Design and Performance Analysis in NavCom Based on LEO Constellation
## 2 Related Surveys  
The aspect that has the primary impact on the S-band signal design is the mutual compatibility issue, which comes from existing (or planned) systems in-band and in adjacent bands.  
From a systematic perspective, there are a large number of signal sources distributed across the dimensions of space, air, ground, and sea. GEO/MEO/LEO constellations in space, LTE 4G/5G base stations, navigation base-stations on the ground, and Wi-Max and Wi-Fi transmitters on vessels, all emit radio waves all the time. Additionally, a large number of receivers are distributed across space-air-ground-sea networks; these detect, acquire, and track radio waves to provide PNTRC services for various users, such as aircraft, vehicles, and vessels, etc. The service scenario diagram is shown in Figure 1, and the main systems are listed in Table 1[32; 33; 34; 35].  
Figure 1: PNTRC service scenario diagram covering space–air–ground–sea integrated networks for the future. Integrated sensing and communication (ISAR) sources such as satellites, air balloons, unmanned aerial vehicles, ground stations, and mobile communication base stations are included, as well as Wi-Fi and Wi-Max transmitters, etc. These are distributed across different dimensions, use different mechanisms, and provide PNTRC services to various users such as aircrafts, vehicles, vessels, and individual mobiles.  
NavCom signals on LEOs, as a subset of the PNTRC source, also face many difficulties, including:  
* Spectrum resources are extremely limited, meaning that it is impossible to give exclusive frequency bands to each system;
* Chaotically propagating signals cause a sharp decline in system performance due to mutual interference, meaning that they cannot be reused. For the above-mentioned bottlenecks, it is necessary to bear in mind the following issues in signal design:
* Spectrum reuse or spectrum efficiency--that is, the work performed per hertz;
* Planning and design between signals of the same frequency, especially the verification interference/anti-interference and mutual compatibility of planned signals in the same band;
* In addition to the traditional time-frequency features, mining novel dimensional features is also extremely important for the extraction and distinguishing of ubiquitous signal designs for PNTRC.
# A Machine Learning Method to Estimate Reference Evapotranspiration Using Soil Moisture Sensors
## 3 Results and Discussion
### Comparison of the Regression Models
Both datasets at 6-h intervals achieved better results for all the regression methods studied. This indicates that splitting up the data had a positive effect in the accuracy. The number of samples was more than 3 times larger in dataset (ii), and five times larger in the case of dataset (iii). The difference between the results of sets (ii) and (iii) was negligible for the regression trees, SVR, and ANN. That is, these methods were more robust to missing data due to sensor failures. However, for the optimal method, RFC + K*, the RMSE was reduced to half by discarding sensor 3. This fact shows that, since this method is an instance-based regression, the existence of erroneous or incomplete data had a bigger effect on its accuracy. For this reason, it worked much better in dataset (iii). The second-best method was the ANN, which achieved the lowest RMSE for dataset (ii), with a value of 0.3037 mm/day, improving the accuracy of RFC + K* in this set. This was equivalent to an MRE above 11.3% and an \(R\) of 0.924, which is also a very accurate result. However, the improvement in the ANN upon removing sensor 3 was insignificant, reducing RMSE by only 0.0065. The results of the remaining methods were always worse than those of RFC + K* and ANN. This proves the complexity of the problem, indicating that the relationship between ETo and the input parameters cannot be captured with a simple model. Furthermore, the other techniques applied in Weka, such as linear regression, k-nearest neighbors, Bayes networks, and logistic regression, produced even worse results. Figure 4a shows that temperature is a very important parameters, but there are other factors not related to the temperature.
## 4 Solution Methodology
### Adaptive Gradient Algorithm  
The core idea of the adaptive gradient (AG) algorithm is to adjust the search step size according to the scale of the BAP problem. For a large BAP, a larger step size can be taken at the beginning of the search phase to improve the search speed, while the search step size is gradually reduced in the subsequent process to achieve better search quality.  
Figure 3 shows the flowchart of the AG algorithm and the pseudocode is presented in Algorithm 2, where \(S\) represents the step size and the notation \(\lceil x\rceil\) denotes the smallest integer greater than or equal to \(x\). The other notations are the same as those described in the FG algorithm. The AG algorithm differs from the FG algorithm in that it has an additional step (Step 2) to adjust the search step size \(S\). Initially, the remaining buffer capacity \(R\) is equal to \(N\), so the value of \(S\) is \(\lceil N/(K-1)\rceil\). In Step 4, a total of \(S\) buffer units are allocated from \(R\) to the buffer with the largest gradient component. Since the value of \(R\) decreases in each iteration, the step size \(S\) will decrease in the next iteration.  
and  
\[R_{i}=R_{i-1}-S_{i}. \tag{5}\]  
Combining (4) and (5), we obtain  
\[R_{i}\leq\frac{K-2}{K-1}R_{i-1}. \tag{6}\]  
Note that \(R_{0}=N\). Then,  
\[R_{i}\leq\left(\frac{K-2}{K-1}\right)^{i}N. \tag{7}\]  
It follows from (7) that the computational complexity of the AG algorithm is \(O(lnN)\). Compared to the FG algorithm, which has a computational complexity of \(O(N)\), the AG algorithm is much more efficient.  
Figure 3: Flowchart for the AG algorithm.
# Positioning Performance Limits of GNSS Meta-Signals and HO-BOC Signals
## 3 GNSS Receiver Signal Processing
### GNSS ML Baseband Signal Processing and Delay/Phase CRB
\(\mathbf{\Lambda}\) and \(\mathbf{V}\) are defined as (for \(N_{1}\leq n,n^{\prime}\leq N_{2}\)):  
\[(\mathbf{V})_{n,n^{\prime}}=\left|\begin{array}{l}n^{\prime}\
eq n:(-1)^{|n -n^{\prime}|}\;\frac{2}{(n-n^{\prime})^{2}}\quad;\;\;(\mathbf{\Lambda})_{n,n^{ \prime}}=\left| \begin{array}{l}n^{\prime}\
eq n:\frac{(-1)^{|n-n^{\prime}|} }{(n-n^{\prime})}\\ n^{\prime}=n:0\end{array} \right.\;. \tag{13}\]
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 5 Conclusions  
In the paper, a speech enhancement algorithm based on recursive learning with a hybrid dilated convolution model was proposed. A hybrid dilated convolution module (HDM) is proposed to solve the problem of insufficient utilization of local information in one-dimensional dilated convolution. Through HDM, the receptive field can be enhanced, the context information can be fully utilized, and the speech enhancement performance of the model can be improved. A recursive recurrent network training model is proposed, which solves the problems of a conventional network with many training parameters and a complex network structure. We improved the speech enhancement quality while reducing the training parameters. The experimental results showed that RRSENet performs better than the other two baseline time-domain models. Future research includes exploring the RRSENet model for other speech processing tasks such as de-reverberation and speaker separation.  
Methodology, Z.S. and Y.M.; software, Y.M.; validation, F.T.; investigation, X.F.; writing--original draft preparation, Y.M.; writing--review and editing, Z.S.; visualization, F.T.; supervision, X.F.; project administration, X.F.; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.  
This work is partly supported by the Key Research and Development Program of Shaanxi (Program Nos. 2021 ZDLGY15-01, 2021 ZDLGY09-04, 2021 GY-004 and 2020 GY-050), the International Science and Technology Cooperation Research Project of Shenzhen (GJHZ20200731095204013), the National Natural Science Foundation of China (Grant No. 61772419).  
Not applicable.  
The data presented in this study are openly available in [41; 42].  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Real-Time Performance Observations
Thanks to the tests we have carried out, the average warning time for the Marmara Region is roughly 11 s. Of course, this varies depending on the distance from the event. Considering the calculation of the P wave, the process, and the network delay, there is a blind zone of approximately 60 km. To improve this, the number of stations should be increased, low latency hardware should be used, and the transmission cables in the stations should be designed as fiber optic. Geoscientists emphasize that the next major earthquake will occur in Istanbul, where the western part of the NAF passes. According to the results obtained in this study, a warning time of approximately 8 s can be predicted for a possible Istanbul earthquake. In addition, when a possible Istanbul earthquake occurs, a blind zone of approximately 40-45 km occurs in Bursa.
## 5 Conclusions  
In this study, we explored the conditions required for the availability of a linear empirical model of SIF to estimate GPP in the canopy of cork oak during the growing season to provide support for applying satellite SIF on regional GPP estimation in different plant function types. The study found that SIF and GPP have good linear relationships, which change with the magnitude of PAR. When PAR is low, the correlations are positive and when PAR is high, GPP decreases with an increase in PAR, resulting in a negative correlation with SIF. Therefore, the magnitude of synchronized PAR should be taken into consideration. The confidence margin of the empirical linear relationship of SIF and GPP can be determined based on its response to PAR from ground-based observations for certain function types when using remote sensing SIF to estimate GPP, especially for the satellite SIF signal obtained around noon. In analyzing the productivity of deciduous broadleaf forests in the future, it is more meaningful to accumulate long-term daily observations and analyze the relationship between GPP and PAR according to growth phrase and climate, and then further combine water indicators to analyze their relationship with plant growth. Since this study only discussed the relationship between SIF and GPP in the growing season of cork oak, further studies will be conducted in other growing months, especially the early and late stages of growth to inquire into the mechanism links between SIF and GPP for broad-leaved forests.  
The following are available online at [[https://www.mdpi.com/article/10.3390/rs13122363/s1](https://www.mdpi.com/article/10.3390/rs13122363/s1)]([https://www.mdpi.com/article/10.3390/rs13122363/s1](https://www.mdpi.com/article/10.3390/rs13122363/s1)), Figure S1: The relationship between GPP and PAR on the daily scale, with the colour bar on the right representing DOY.  
Conceptualization, J.Z. and F.W.; Methodology, X.C. and Y.Z.; Software, X.C. and M.H.; Validation, H.H.; Writing--original draft preparation, X.C.; Writing--review and editing, J.Z., F.W. and H.H.; Visualization, X.C.; Supervision: J.Z. and F.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds of the Central Public Welfare Scientific Research Institutes (CAFYBB2020 QD002-2) and National Nonprofit In-stitute Research Grant of the Chinese Academy of Forestry (CAFYBB2018 ZA001).  
We thank [PERSON] from the Northwest A & F University for advising. We thank [PERSON] and [PERSON] from Nanshan Forest Farm, Jiyuan City for contributing to field measurements. We thank [PERSON], [PERSON], [PERSON] and [PERSON] for helping to run the spectrometer systems and eddy covariance systems. We also appreciate editors and three anonymous reviewers for their constructive suggestions and comments that improved the manuscript.  
The authors declare no conflict of interest.