esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
High sensitivity GPS extends the use of GNSS positioning to environments where signals are attenuated and reflected. HS GPS enables positioning even indoors, although only to a certain degree. GPS positioning inside objects is usually limited to top floors and the proximity of outer walls.  
Code-based autonomous positioning can often result in major errors of the estimated position. Therefore, observations under adverse conditions should be treated with caution. Owing to the limitations of the equipment available, the custom developed positioning is based on single frequency code pseudoranges and Doppler observations. The procedure relies on the modified L\({}_{1}\)-norm robust estimation to mitigate the influence of gross observation errors. The receiver clock offset and the position unknowns are determined separately. The clock offset is estimated using Doppler observations only, whereas the position is determined using both code and Doppler observables. An additional feature is the recalculation of the satellite ranges after each epoch. Geometric ranges are used insteadof pseudoranges in the subsequent epoch. The initial position, however, has to be determined by code-only positioning since only relative velocities between the receiver and the satellites can be derived from Doppler observations. Therefore, in order to consider Doppler observables for positioning, reference ranges are required. The accuracy of the positions of the subsequent epochs depends upon the accuracy of the initial position. For this reason, it is essential that the initial position should be determined in an environment where conditions are favourable. The best initial position would ideally be determined by DGPS or by the cDGPS.  
Additional models and weightings can be applied to the positioning procedure. The elevation angle weighting and the SNR weighting can, in some cases, result in a better solution, although they can also cause gross position errors. On the other hand, the ionosphere and the troposphere models should be used in any case and under any conditions. The custom positioning procedure using code and Doppler observations with applied atmosphere models offers the best results for autonomous positioning under adverse conditions. The accuracy achieved is close to the accuracy level obtained under normal conditions.  
Under adverse conditions DGPS cannot be used because the quality of the results deteriorates. On the other hand, conditional DGPS maybe applicable if enough strong signals are available. In should be noted that DGPS in general can only be performed if the data from a reference receiver is available. The use of conditional DGPS raises the quality of the calculated positions considerably.
## 3 Experimental Results and Discussion
### Evaluation of Fall Detection
#### 3.2.1 Fall Detection on Representative Scenes  
Figure 16 shows some representative scenes of datasets used in the evaluation experiment. The first three images in Figure 16 show a normal walking, side fall, and front fall scene from the left. The next three images are a squatting, bending, and sitting scene from the left. Experimental results evaluated with the metric (23) are summarized in Table 2 with respect to the sensitivity, specificity, accuracy, and the error rate of the representative scenes in Figure 16. Ground Truth in Table 2 indicates the manually recorded frames to test whether it detects falls accurately or not. The term 'No alarm' means that the fall indicator is not turned on. The sensitivity and specificity rate obtained from the experiment are similar or higher than those obtained in other studies for indoor fall detection. We cannot directly compare our results with others since our study focuses on an outdoor environment. However, these results (overall 99.1% success and 0.9% error) demonstrate that our algorithm is a promising approach for fall detection in an outdoor environment. Moreover, Figure 17 shows the tracking results for each case presented in Figure 16. Our algorithm can successively track all the cases from top to bottom.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Person** & **Case** & **Our System** & **Ground Truth** & **Sensitivity (\%)** & **Specificity (\%)** & **Accuracy (\%)** & **Error (\%)** \\ \hline A & Bending & No alarm & No alarm & & & & \\ \hline \multirow{2}{*}{B} & Side fall & 52–96 & 52–95 & & & & \\  & Front/back fall & 54–100 & 55–98 & & & & \\ \hline \multirow{4}{*}{C} & Side fall & 52–110 & 49–109 & & & & \\  & Sitting & No alarm & No alarm & & & \\  & Bending & No alarm & No alarm & & & \\ \hline \multirow{4}{*}{D} & Sitting & No alarm & No alarm & & & \\  & Side fall & 38–71 & 36–70 & & & \\ \cline{1-1}  & Walking & No alarm & No alarm & & & \\ \cline{1-1}  & Bending & No alarm & No alarm & & & \\ \cline{1-1}  & Squatting & No alarm & No alarm & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification result of our system for representative scenes.  
Figure 15: State transition model of the proposed fall detection algorithm.  
Figure 16: Representative scenes and fall cases used for the evaluation.  
Figure 17: Resulting images of human tracking for various confounding cases: green colored ellipses demonstrate successful tracking and the red numbers are the image frame number.
## 3 Test Results, Analyses, and Discussion
### AFM Analysis
#### 3.3.2 Morphological Features of the Asphalt-Binder Surface  
The surface roughness indices reflected the degree of development of the beelike structure. Figure 12 shows the roughness indices that were calculated from the height of 30 \(\times\) 30 \(\upmu\)m AFM images. The roughness indices selected for analysis were the average roughness (\(R\)a) and root-mean-square roughness (\(R\)q).  
After multiple aging and rejuvenation cycles, the \(R\)a and \(R\)q values of the asphalt-binders with different rejuvenators had a similar response trend. After aging, the \(R\)a and \(R\)q values of the asphalt-binder surface decreased. By contrast, the roughness indices appeared to have been restored after rejuvenation, which is similar to the findings reported by [PERSON] et al. [47]. This could be because the molecular movement of the asphalt-binders decreased after aging, with the accumulation and nucleation of the polar components being hindered. This led to the reduction in the size and height of the beelike structures. The addition of the rejuvenator to supplement maltenes could promote the mobility of the asphalt-binder molecules. Therefore, the size and height of the beelike structures of rejuvenated asphalt-binder were greater than those of the aged asphalt-binder. This helps to explain the mechanism responsible for the formation of the beelike structures during the rejuvenation process.  
However, the three rejuvenators showed different rejuvenation effects. The surface of the asphalt-binders with the Type III rejuvenator were the roughest and had the greatest beelike structures in height. The asphalt-binders with the Type II rejuvenator had the least roughness, with the lowest height in the beelike structures. After three rejuvenation cycles, the roughness of the rejuvenated asphalt-binder with Type III was more than twice that of the asphalt-binder with Type II. This might be because the Type III rejuvenator replenished most of the light components and greatly improved the mobility of the maltene molecules. By contrast, Type II only slightly promoted the accumulation of the polar components.  
Figure 12: AFM roughness analysis: (**a**) rejuvenated asphalt-binders; (**b**) aged asphalt-binders.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 4 Simulation Test Results  
### The Contamination Distribution of Insulators in Wind Tunnel  
When the wind speed is 5 m/s, the diameter of the particles is 400 meshes, relatively humidity is 60% in non-energized condition, the contamination distribution of Type-A is shown in Figure 7a. When electrify the insulator with voltage of DC + 35 kV, the contamination distribution of Type-A is shown in Figure 7b.  
From the test results, conclusions can be made as follows:  
1. As is shown in Figure 7a, contamination distribution is obviously non-uniform on insulator surface. Contamination degree of leeward side of insulator is greater than that of windward side and fan-shaped pollution layer gets formed at leeward side. Thus, the contamination degree of the top surface is higher than that of the bottom surface. In the position towards air flow, a thicker pollution layer will also get formed at round bar. This is due to the air pressure in the position direct to the incoming air flow getting increased, causing more pollution particles to adhere on the insulator surface.
2. As is shown in Figure 7b, the effect of DC voltage on the contamination degree is obvious. Contamination degree in the dc energized condition is heavier than that in a non-energized condition. Besides this, contamination distribution along the insulator sting is obviously uneven, and the contamination degree of the high voltage area is much heavier than that of the middle area along the insulator sting, which is consistent with that observed at the operating HVDC transmission lines.  
Figure 7: The contamination distribution of different insulators. (**a**) Sample in non-energized condition; (**b**) Sample with voltage of DC + 35 kV.
## 5 Conclusions  
This study conducted triaxial compressive tests on Y-shaped fiber-reinforced subgrade fill with different fiber contents, fiber lengths, and confining pressures; and the strength characteristics of the fiber-reinforced soil were analyzed. The following conclusions were drawn:  
1. The strength of the fiber-reinforced soil first increased and then decreased as the fiber content and length increased. The fiber reinforcement effect was the best at a fiber content of 0.2% and fiber length of 12 mm in all test conditions. As the confining pressure increased, the strength of the fiber-reinforced soil increased.  
Figure 17: Comparison of the measured and predicted \(E_{i}\) of the fiber-reinforced soil.  
Figure 18: Comparison of the measured and predicted \((\sigma_{1}-\sigma_{3})_{\text{ult}}\) of the fiber-reinforced soil.  
2. The failure pattern of the fiber-reinforced soil specimens was due to bulging deformation, showing plastic failure characteristics.
3. Through regression analysis of our test data, an empirical model for predicting the failure strength of fiber-reinforced soil was established. This model considers the fiber content, fiber length, and confining pressure. It can rapidly estimate the failure strength of fiber-reinforced soil and provide a reference for engineering applications.
4. The stress-strain curve of the fiber-reinforced soil exhibited strain-hardening characteristics and could be fitted by a hyperbolic curve. Thus, the Duncan-Chang model can be used to mathematically describe the stress-strain relationship of the fiber-reinforced soil. A method for calculating \(E_{i}\) and \(\left(\sigma_{1}-\sigma_{3}\right)_{ult}\), the Duncan-Change model parameters, was proposed, which provides a reference for the accurate expression of the mechanical properties of heavy-haul railway subgrade.
5. It is important to note that this work concerned a case study, which is intended to develop a strength empirical model for predicting the strength of the fiber-reinforced fill with a modest number of samples and tests. Accordingly, more tests are necessary to be conducted to further verify the applicability of the proposed models.  
Methodology, investigation, resources, original draft preparation, review and editing, Y.Z.; supervision, X.L. and W.G.; validation, P.L., G.L., and L.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the State Key Laboratory of Frozen Soil Engineering, grant number SKLFSE201907, National Key Research and Development Program of China, grant number 2018 YFC1505300, National Major Scientific Instruments Development Project of China, grant number, 41627801, National Natural Science Foundation of China, grant number 51678145. The APC was funded by 2018 YFC1505300. This work was possible with the equipment support of Northwest Institute of Eco-Environment and Resources. The fibers has been provided by Shanghai Bonin Engineering Fiber Materials Co. LTD, Shanghai, China. The authors are grateful to [PERSON] for offering fibers' details. The authors declare no conflict of interest.
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 6 Pharmacological Properties of Seaweed to Overcome Chronic Disease
### Cancer  
The mechanism of cancer cell formation and proliferation is shown in Figure 4a. Numerous studies on the effects of seaweed extracts against breast cancer have been reported. _Sargassum hemiphyllum_ fucoidan inhibited the viability of MCF-7 and MDA-MB-231 cell lines by modulating the miR-29c/ADAM12 and miR-17-5p/PTEN axes [72]. _Laurencia papillosa_[117] and _Sargassum_ sp. [118] showed significant inhibition of viability of the MCF-7 cell line. A high level of inhibition of the MDA-MB-231 cell line was also observed following treatment with _Bifurcaria bifurcata_[119] and _Ulva fuscata_ by downregulating the EGFR/PI3K/Akt pathway [120]. _Eucheuma cottonii_ suppressed the growth of tumor cells in rats inoculated with breast-cancer tumors in in vivo experiment [118]. Other in vivo studies reported that Wistar rats administered with 50 mg/kg body weight orally, daily for 10 weeks, suppressed breast carcinogenesis [121]. Studies of the effects of seaweed-derived compounds on cancer which were conducted in vitro and in vivo are summarized in Table 1.  
Figure 4: Mechanisms of chronic diseases, including cancer (**a**), neurodegenerative disease (**b**), alcoholic liver disease (**c**), diabetes (**d**), obesity (**e**), cardiovascular disease (**f**), osteoarthritis (**g**) and osteoporosis (**h**).  
Carrageenanen extract from _Gigartina pistillata_ inhibits cell growth in colorectal cancer stem cells with an IC\({}_{50}\) value of 1 \(\mu\)g/mL [125]. Moreover, _Hizikia fusiforme_ inhibited EJ tumor growth both in vivo and in vitro through G1-phase cell cycle arrest by downregulating cyclins and cyclin-dependent kinases (CDKs), as well as by inhibiting the expression of MMP-9 by downregulating NF-kB, AP-1, and Sp-1 [122]. In addition, seaweed extract also exhibited potent anticancer activity against hepatocellular carcinoma (HCC) cell lines [123], the U87 MG cell line [129], and sarcoma 180 ascites tumor cells both in vitro and in vivo [126]. Furthermore, the inhibition of HT-29 cell proliferation by fucoidan may be due to the downregulation of IGF-IR signaling by the main IRS-1/PI3K/AKT pathway.
## 3 Methodology
### Performance Metrics  
Three metrics have been computed to assess the performance of the classifiers. Since the 18 tested datasets are highly unbalanced, the accuracy has not been computed because it would have been a biased metric and thus not represented a fair indicator of the classifiers' goodness. For ease of use, the following notations are used: true positives (TP) are rain minutes correctly classified, false positives (FP) are non-rain minutes incorrectly classified as rain, true negatives (TN) are non-rain minutes correctly classified, false negatives (FN) are rain minutes incorrectly classified as non-rain. The three metrics are listed in the following:  
1. Specificity is the ratio between the TN and all the negatives, indicating how good a classifier is in predicting non-rainy observations. \[Specificity=\frac{TN}{TN+FP}\] (2)Recall is the measure of our model correctly identifying TP. Thus, for all the samples that actually are rainy, recall tells us how many of them the classifier has correctly identified as rainy samples. \[Recall=\frac{TP}{TP+FN}\] (3)
3. F1-score represents the Harmonic mean of the precision and recall. The precision is the ratio between the TP and all the positives. For our problem statement, that would be the measure of samples that the classifier has correctly identified as rainy samples out of all the samples that have been predicted as rainy by the classifier. \[F1\text{-}Score=\frac{2*Precision*Recall}{Precision+Recall}\] (4)
## 3 Results and Discussion
### High-Resolution Neutron Powder Diffraction
#### 3.1.2 Ncnco03  
Table 2 contains the results of the structure refinement obtained for the NCNCO03 sample by TOF NPD. Refinement profiles are presented in Figures S5-S8. The oxygen content in the NCNCO03 sample after synthesis is hypo-stoichiometric, which is expected based on an increased content of copper in the composition. After heating in air, the same results as for the NCNCO01 sample can be seen, namely, the appearance of the oxygen interstitials at the expense of oxygen atoms from the apical site.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NCNCO01** & **As Synthesized** & **After Treatment in Air** & **Before Treatment in Vacuum** & **After Treatment in Vacuum** \\ \hline \(a\), Å & 5.31796 (7) & 5.31948 (8) & 5.32090 (7) & 5.32054 (8) \\ \(b\), Å & 5.38898 (8) & 5.38596 (8) & 5.38846 (8) & 5.39752 (8) \\ \(c\), Å & 12.3197 (1) & 12.3235 (2) & 12.3265 (2) & 12.3234 (2) \\ \(V\), Å\({}^{3}\) & 353.062 (5) & 353.074 (6) & 353.420 (5) & 353.900 (6) \\ y (Nd/Ca) & \(-\)0.0082 (6) & \(-\)0.0090 (6) & \(-\)0.0091 (6) & \(-\)0.0075 (6) \\ z (Nd/Ca) & 0.3614 (1) & 0.3626 (2) & 0.3607 (1) & 0.3608 (2) \\ \(1\)U (Nd/Ca), Å\({}^{2}\) & 0.009 (1) & 0.013 (2) & 0.011 (1) & 0.014 (1) \\ U (Ni/Cu), Å\({}^{2}\) & 0.011 (1) & 0.006 (2) & 0.0036 (9) & 0.005 (1) \\ z (O1) & \(-\)0.0122 (2) & \(-\)0.0137 (2) & \(-\)0.0122 (2) & \(-\)0.0120 (2) \\ U (O1), Å\({}^{2}\) & 0.011 (1) & 0.013 (2) & 0.011 (1) & 0.014 (1) \\ y (O2) & 0.0353 (5) & 0.0391 (5) & 0.0365 (5) & 0.0420 (4) \\ z (O2) & 0.1766 (3) & 0.1742 (3) & 0.1743 (3) & 0.1765 (3) \\ U (O2), Å\({}^{2}\) & 0.021 (2) & 0.017 (2) & 0.026 (1) & 0.020 (1) \\ \({}^{2}\)F (O2) & 1.00 (1) & 0.94 (1) & 0.99 (1) & 1 \\ z (O3) & - & 0.297 (4) & - & - \\ F (O3) & 0 & 0.056 (8) & 0 & 0 \\ wR, \%/GoF & 3.71/1.67 & 3.93/1.86 & 3.36/1.73 & 3.34/1.66 \\
4+8 & 4.00 (2) & 3.98 (3) & 3.98 (2) & 4.00 (2) \\ \hline \end{tabular} \({}^{1}\)U—atomic displacement factor; \({}^{2}\)F—occupancy of a site.  
\end{table}
Table 1: Results of the Rietveld refinement of the NCNCO01 structure obtained at 50 \({}^{\circ}\)C by TOF NPD.  
in a vacuum, the oxygen content in the NCNCO03 sample remained the same, and other structural parameters were close to those obtained before the temperature treatment. The structure of the as-synthesized NCNCO03 sample and the NCNCO03 sample after the high-temperature treatment in static air is shown in Figure S9a,b, respectively.
## 2 The Modular Multilevel Converter (MMC)  
The general structure of the MMC under study in this paper is shown in Figure 1. This MMC consists of \(n\)-many SMs in series in each arm, in which the SM voltage should be equal to V\({}_{\text{dc}}\)/N. Inductance and resistance elements are present in both arms and outbranches of the considered MMC, as depicted in Figure 1. Considering the current directions, the relationship between the upper and lower arm currents and the MMC output current can be stated as (1):  
\[i_{k}=i_{uk}+i_{lk}. \tag{1}\]  
On the other side, the MMC circulating currents can be written as follows (2):  
\[i_{cirk}=\frac{(i_{uk}-i_{lk})}{2}-\frac{i_{dc}}{3}. \tag{2}\]It can be deduced from (2) that the circulating current is dependent on the upper and lower currents as well as the dc link current, which all are depicted in Figure 1. As previously stated, the control laws of the MMC in the a-b-c reference frame are defined as \(v_{uk}\) and \(v_{lk}\). The accurate dynamic relations between the control laws and other MMC variables can be determined by using [PERSON]'s Voltage Laws (KVLs) in Figure 1. As a result, the following dynamic equations are given:  
\[\frac{-v_{dc}}{2}+v_{uk}+L_{t}\frac{di_{uk}}{dt}+R_{t}i_{uk}+L\frac{di_{k}}{dt }+Ri_{k}+v_{k}=0, \tag{3}\]  
\[\frac{v_{dc}}{2}-v_{lk}+L_{t}\frac{di_{lk}}{dt}+R_{t}i_{lk}+L\frac{di_{k}}{dt}+ Ri_{k}+v_{k}=0. \tag{4}\]  
The dynamic Equations (3) and (4) are utilized for achieving the ultimate control laws.
###### Abstract  
This study aims to optimize the Weather Research and Forecasting (WRF) model regarding the choice of the best planetary boundary layer (PBL) physical scheme and to evaluate the model's performance for wind energy assessment and mapping over the Iranian territory. In this initiative, five PBL and surface layer parameterization schemes were tested, and their performance was evaluated via comparison with observational wind data. The study used two-way nesting domains with spatial resolutions of 15 km and 5 km to represent atmospheric circulation patterns affecting the study area. Additionally, a seventeen-year simulation (2004-2020) was conducted, producing wind datasets for the entire Iranian territory. The accuracy of the WRF model was assessed by comparing its results with observations from multiple sites and with the high-resolution Global Wind Atlas. Statistical parameters and wind power density were calculated from the simulated data and compared with observations to evaluate wind energy potential at specific sites. The model's performance was sensitive to the horizontal resolution of the terrain data, with weaker simulations for wind speeds below 3 m/s and above 10 m/s. The results confirm that the WRF model provides reliable wind speed data for realistic wind energy assessment studies in Iran. The model-generated wind resource map identifies areas with high wind (wind speed \(>\) 5.6 m/s) potential that are currently without wind farms or Aeolic parks for exploitation of the wind energy potential. The Sistan Basin in eastern Iran was identified as the area with the highest wind power density, while areas west of the Zagros Mountains and in southwest Iran showed high aeolian potential during summer. A novelty of this research is the application of the WRF model in an area characterized by high topographical complexities and specific geographical features. The results provide practical solutions and valuable insights for industry stakeholders, facilitating informed decision making, reducing uncertainties, and promoting the effective utilization of wind energy resources in the region.  
WRF model; physical parameterization; planetary boundary layer; wind resource assessment; Iran +
Footnote †: journal: Applied Sciences
## 3 Application and Comparison of Various Classification Methods
### Online Sequential ELM  
To process the standard ELM approach, the parameters of ELM and the entirety of the training data are required for the training step. Therefore, the standard ELM assumes that all the training data are ready prior to the training process. However, in cases of real-time system operation or maintenance tasks, the training data are constantly increased and accumulated by the passage of time or the occurrence of new events.  
For this reason, [PERSON] et al. [47] developed the online sequential ELM (OS-ELM), which can train the sequentially accumulated data one-by-one. This learning approach is an appropriate technique for practical applications in which the number of training data or the property of timeliness is not fixed (i.e., training data have a validity period [48]). For instance, in the short-term prediction of stock prices, the training data that are older and less effective are given lower weighting than the recent data. Table 3 presents the pseudo code of OS-ELM [47].