esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Low-Level Feature Distributions Based No Reference Image Quality Assessment
## 5 Conclusions  
In this paper, we propose a novel framework for NR IQA method, namely TLLFD. First, the normalized information is used as the image preprocessing, which is conducive to the statistical analysis of subsequent features. Then, two low-level feature distributions with unique regression function are extracted, and finally the quality regression analysis is carried out by using nonlinear regression method.  
The feature extraction is carried out in two stages. One stage is the weight CLBP histogram coefficients are taken as an image texture feature, and the other stage is the parameters of the Weibull distribution fitting gradient map are used as an image gradient feature.  
Verified by the experimental results, the TLLFD method can be compared with the state-of-the-art NR IQA method even when compared with the state-of-the-art FR IQA method. Compared with deep learning-based methods, the TLLFD method also achieves superior performance and strong generalization.  
The disadvantage of this method is that some experimental parameters are not adaptively chosen, and knowledge of classical features is needed for deep understanding.  
The follow-up work will further study the basic features of the image, and analyze it using the method proposed in this paper. The combination of classical features can have a certain comparative power with the deep learning method, so as to obtain more consistent results with the real MOS.  
Figure 5: SROCC (w) versus running time (log scale) of different methods.  
**Author Contributions:** Conceptualization, H.F., G.L. and X.Y.; Data curation, H.F., G.L., X.Y., L.W. and L.Y.; Formal analysis, G.L.; Funding acquisition, L.Y.; Investigation, X.Y. and L.W.; Methodology, H.F., G.L. and X.Y.; Project administration, L.W. and L.Y.; Resources, G.L. and L.Y.; Supervision, G.L.; Validation, H.F. and L.W.; Writing--original draft, H.F. and L.Y.; Writing--review & editing, G.L. and L.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China (Grant No. 62061040, 12162029, 61941111, 61906102), in part by the Key research and development programs of Ningxia (Grant No. 2019 BEG03056), and in part by the Natural Science Foundation of Ningxia (Grant No. 2021 AAC03039).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The MATLAB source code of TLLFD is available online at [[https://github.com/Yazhen1/TLLFD](https://github.com/Yazhen1/TLLFD)]([https://github.com/Yazhen1/TLLFD](https://github.com/Yazhen1/TLLFD)) (accessed on 11 May 2022).  
**Acknowledgments:** All individuals included in this section have consented to the acknowledgement.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Equivalent Inelastic SDOF System for Steel Frames with Hysteretic Dampers  
This section describes the methodology for converting steel frames with steel hysteretic dampers into an equivalent inelastic SDOF system.  
As shown in Figure 1a, the steel hysteretic dampers can be described using shear springs that depend on inter-story drifts. In order to describe an inelastic behavior, a hysteresis rule of a spring in the equivalent SDOF system can be defined by an approximated skeleton curve with respect to a base shear force versus roof drift curve obtained from NSPA, as shown in Figure 1b. When all of the structural components of a building behave in a similar manner, it is possible for an equivalent SDOF system with an assumed single skeleton curve to estimate the inelastic seismic performance of the entire system. However, if the hysteresis rules of each structural member, such as the dampers and frame members, are significantly different, then the assumed single skeleton curve leads to a loss of accuracy in estimating the seismic performance, because the assumed bilinear (or tri-linear) single skeleton curve may not be sufficient to consider the behavior of the dampers.  
In the shear frame with dampers (hereafter referred to as the MDOF system), the story shear force is computed by accumulating the lateral force, which is resisted by the column elements and the dampers above the story level. Therefore, the base shear force, which is the shear force on thefirst story, is computed by accumulating two lateral forces, namely, the lateral force resisted by the column elements of each story and the lateral force resisted by the dampers of each story. In other words, the lateral force resisted by the dampers can also be divided into several forces, as shown in Figure 1d. Therefore, as shown in Figure 1e, the equivalent SDOF system of the MDOF system can be described using multi-springs with skeleton curves approximated by the base shear force relative to the roof drift curve, as shown in Figure 1d. Based on the fore-mentioned approach, the basic concept of an equivalent inelastic SDOF system with multi-springs was presented in a previous study [28]. Parts of the theoretical development of the method used in this paper are also based on a previous study [28]. The equivalent inelastic SDOF system with multi-springs comprises an effective mass \({}_{s}\overline{M_{1}}\) and an effective height \({}_{s}\overline{H_{1}}\) that are described in Section 2.2.1; an inelastic spring equivalent to the bare frame that is described Section 2.2.2; and inelastic springs equivalent to the dampers that are described Section 2.2.3.
## 5 Feasibility Study Through System Simulations
### NN Structure for Each Pattern-Matching Method and Model Training Details
#### 5.8.3 NN Structure for Pattern-Matching Method 4-MultFingIn-ClassOut  
In pattern-matching method 4-MultFingIn-ClassOut, the mapping from the fingerprints to the position coordinates is solved as a classification problem. The NN structure consists of a 2D CNN with \(N_{SS}\times N_{SS}\times N_{F}\) inputs, with one for each fingerprint from each sensor in the 2D sensor array. The NN has \(N_{T}\) outputs, with the purpose of mapping the inputs to a classification among \(N_{T}\) tile-classes, expressed as a likelihood probability. The pattern-matching method is explained in Section 4.4.4 and summarized in Table 6 (Section 4.4).  
Pattern-matching method 4-MultFingIn-ClassOut implements the 2D CNN shown in Table 13. From Mechanism 1 (Section 5.7.1), we found that it was not necessary to use pooling layers. A softmax activation function is used in the last layer to implement multi-class classification. The model is trained using the RMSprop optimizer, with a categorical cross-entropy cost function. The RMSprop optimizer performs the optimization in the training of the model with an adaptive learning rate.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Layer** & **Size** & **Activation Function** & **Notes** \\ \hline Dense & \(N_{F}\) & Hyperbolic tangent & Input: vector of \(N_{F}\) fingerprints from a sub-tile. \\ Dense & 32 & Hyperbolic tangent & - \\ Dense & 32 & Hyperbolic tangent & - \\ Dense & 32 & Rectified linear unit & - \\ Dense & 2 & Linear & Output: position coordinates. \\ \hline \hline \end{tabular}
\end{table}
Table 11: FFNN for pattern-matching methods 1-SingFingIn-PosOut and 2-SingFingIn-AggPosOut.  
\begin{table}
\begin{tabular}{l c l l} \hline \hline
**Layer** & **Size** & **Activation Function** & **Notes** \\ \hline Dense & \(N_{ST}\times N_{F}\) & Hyperbolic tangent & Input: tensor of the fingerprints from \(N_{ST}\) \\  & & & sub-tiles (1D arrangement) by the size of \\  & & & the set of fingerprints, \(N_{F}\). \\ Dense & 2048 & Hyperbolic tangent & - \\ Dense & 2048 & Hyperbolic tangent & - \\ Dense & 2048 & Hyperbolic tangent & - \\ Dense & 1024 & Hyperbolic tangent & - \\ Dense & 512 & Rectified linear unit & - \\ Dense & 2 & Linear & Output: position coordinates. \\ \hline \hline \end{tabular}
\end{table}
Table 12: FFNN for pattern-matching method 3-MultFingIn-PosOut.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 2 Study Area and Data
### Dataset
#### 2.2.2 FY-4A DSD Data  
FY-4A DSD is a dust detection dataset based on the FY-4A AGRI data, which has a spatial resolution of 4 km and a temporal resolution of 1 h and is available on the National Satellite Meteorological Center's Data Services Network ([[http://satellite.nsmc.org.cn/PortalSite/Default.aspx](http://satellite.nsmc.org.cn/PortalSite/Default.aspx)]([http://satellite.nsmc.org.cn/PortalSite/Default.aspx](http://satellite.nsmc.org.cn/PortalSite/Default.aspx)) (accessed on 8 March 2022)). DSD is based on the spectral threshold method and the probability density function (PDF) to distinguish dust and soot from clouds, clear air, and water bodies on land. It uses the individual variability of dust in each spectral display from clouds, the surface, and clear atmosphere [51]. The DST and IDDI products from the DSD dataset were used in this study. DST is determined by the number of the passed dust detection indicators and the PDF function of their thresholds. The higher the score, the greater the possibility of dust. The recommended dust fraction is over 16 for dust pixels, 14-16 for possible dust pixels, and below 14 for non-dust pixels [51]. IDDI is based on the idea that dust causes attenuation of the infrared signal emitted from the surface, resulting in a reduction in the brightness temperature detected by the satellite [29]. IDDI depicts the difference between the sensor observed BT (brightness temperature) and BT from the clear-sky image, which allows for a semi-quantitative description of dust  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Waveband** & **Spectral Properties** & **Central Wavelength** & **Spatial Resolution/km** \\ \hline NOMChannel01 & & 0.47 & 1 \\ NOMChannel02 & & 0.65 & 0.5 \\ \hline NOMChannel03 & & 0.825 & 1 \\ NOMChannel04 & & 1.375 & 2 \\ NOMChannel05 & & 1.61 & 2 \\ NOMChannel06 & & 2.225 & 2–4 \\ \hline NOMChannel07 & & 3.75 & 2–4 \\ NOMChannel08 & & 3.75 & 4 \\ NOMChannel09 & & 6.25 & 4 \\ NOMChannel10 & & 7.1 & 4 \\ NOMChannel11 & & 8.5 & 4 \\ NOMChannel12 & & 10.8 & 4 \\ NOMChannel13 & & 12 & 4 \\ NOMChannel14 & & 13.5 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main technical parameters of FY-4A AGRI.  
Figure 1: Study area land cover type and PM stations.  
intensity [51]. It can take full advantage of high-frequency observations from geostationary satellites to dynamically monitor dust movement and intensity evolution.
# Some Thoughts about Infrared Radiation Response Characteristics during Loading of Sandstone Samples
## 4 Infrared Radiation Characteristics during Damage Evolution of Sandstone  
### Samples with Increasing Temperature  
Figure 3 shows the infrared radiation characteristics of the sandstone sample A\({}_{3}\) at different damage stages during compression. From crack closure to the elastic deformation stage, the color of the infrared thermal image changes gradually from dark (low temperature) to bright (high temperature), showing a uniform and gradual temperature increase. During the stable crack growth stage, more brilliant temperature stripes begin to appear in the local areas of the infrared thermal image. During the unstable crack growth stage, the infrared radiation is enhanced with oblique high-temperature stripes and low-temperature regions. These differences indicate a significant increase in the dispersion of the temperature distribution. Afterwards, the rock sample fails with macroscopic shear fractures. During the stable and unstable crack growth stages, the abnormal temperature stripes in the thermal image of the specimen fit the final fracture surfaces.  
Figure 4 shows the stress-time and AIRT-time curves of the rock samples. During the elastic deformation, the AIRT shows a linear increase with rising stress. The average temperature increase in the rock samples is 0.19 \({}^{\circ}\)C (see Table 1). In the stage of stable crack growth, the AIRT of rock continues to rise. Afterwards, during the unstable crack growth, the AIRT increase in rock is the smallest (the average increase in sandstone samples is 0.05 \({}^{\circ}\)C). Most samples can even show nearly constant values (with slight fluctuations) in this stage before final failure. These short moments can be called the'silent period' (see Figure 4). During the final failure, fractures are formed throughout the sample, including the surfaces. The micro-cracks on the surfaces will squeeze, move, and slip under compressive stresses. The fracture surfaces in contact move relative to each other, and the friction between the surfaces converts kinetic energy into thermal energy. The thermal energy induced by friction can cause the AIRT of the sample to increase abruptly (see Figure 4b).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature Trend**} & \multirow{2}{*}{**Sample**} & \multicolumn{5}{c}{**AIRT Variations/\({}^{\circ}\)C**} \\ \cline{3-6}  & & **CC** & **ED** & **SCG** & **UCG** & **At Failure** \\ \hline \multirow{8}{*}{**Increase**} & A\({}_{2}\) & 0.09 & 0.15 & 0.08 & 0.03 & 0.07 \\  & A\({}_{3}\) & 0.02 & 0.05 & 0.05 & 0.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 2 Materials and Methods
### Total Phenolic Content (TPC)  
Total phenolic content (TPC) was determined according to the Folin-Ciocalteu methodology [33] with our own modifications. Whole pepper seeds, juniper or allspices berries were milled according to the protocol described in PN-ISO 2825 and then subjected for polyphenic compounds extraction performed with 80% ethanol solution. Two independent ethanol extracts were prepared for each variant of the experiment. To determine the TPC, 0.5 mL of extract was added to a flask containing 30 mL of distilled water and mixed with 2.5 mL of Folin-Ciocalteau reagent. After 5 min, 2 mL of 1.7 M sodium carbonate was added. The mixture was well stirred, filled with distilled water up to 50 mL of volume and left for 1 h in darkness. After incubation the absorbance against a blank sample (without extract) at 750 nm was measured using a He\(\lambda\)ios \(\gamma\) ThermoSpectronic spectrophotometer (Thermo Electron Corporation, Waltham, MA, USA). TPC was expressed in terms of gallic acid equivalents  
Figure 1: Setup for microwave plasma treatment of selected spices.  
Figure 2: Photo of plasma treatment of juniper berries.  
concentration [mg GAE/g d.m.] on the basis of a standard curve. The measurements were done in two repetition for each extract.
# A Modified Bearing Capacity Model for Inclined Shallow Anchor Cable with Experimental Verification
## 4 Discussion
### Compare with Other's Models  
While classical models focus primarily on the anchor length and burial depth, they tend to overlook the influence of anchor inclination. When compared to previous classical methods, the proposed model exhibited stronger predictive capabilities, especially under conditions involving inclined loads. The bearing capacity formulations provided in this paper are compared with experiment results in the literature, including [PERSON] [34], [PERSON] [35], and [PERSON] [36]. The calculations used in this study were based on typical parameters extracted from these foundational works. The bearing capacity results derived from the formula were then compared with the experiment values from the literature.  
Figure 10: Influence of strength parameters of ground on the bearing capacity curve: (**a**) internal friction angle; (**b**) cohesion.  
Figure 9: Influence of geometrical parameters of the failure surface on the bearing capacity curve.  
In Table 5, the \"Experiment values of literature\" represent the bearing capacity values obtained from experiments of previous studies, while the \"Projected value of the model\" presents the results calculated using the proposed model from this paper. This distinction allows for a clear comparison between the experimental results and the values predicted by the new model.  
The comparison results indicate that the maximum deviation between the calculated values obtained using the proposed model and the values reported in the literature is approximately 17%. This level of discrepancy is within an acceptable range for engineering applications, suggesting that the proposed model provides a reliable estimation of bearing capacity. Such differences can be attributed to variations in testing conditions, material properties, and assumptions made in previous studies. Despite these factors, the overall consistency between the calculated results and the reference data reinforces the validity of the proposed model. This demonstrates the model's robustness and suitability for practical use in predicting the behavior of anchors under inclined loading conditions.
## 3 Results
### Spatiotemporal Consistency of Winter Wheat Planted and Harvested Areas from 2008-2018
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**prod\_NASS vs. plt\_CDL**} & \multicolumn{3}{c}{**prod\_NASS vs. plt\_NASS**} & \multicolumn{3}{c}{**prod\_NASS vs. hav\_NASS**} \\ \cline{2-11} \multicolumn{1}{c}{**Year**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**R\({}^{2}\)**} & **Bias** & **RMSE** & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**R\({}^{2}\)**} & **Bias** & **RMSE** & \multirow{2}{*}{**Slope**} & **R\({}^{2}\)** & **Bias** & **RMSE** \\  & & & **(10\({}^{3}\) ton)** & & **(10\({}^{3}\) ton)** & & & & & & & \\ \hline
2008 & 257.56 & 0.80 & 5.02 & 74.06 & 251.73 & 0.81 & 5.65 & 73.27 & 293.28 & 0.89 & 1.17 & 79.10 \\
2009 & 214.09 & 0.66 & 4.65 & 71.02 & 215.73 & 0.69 & 4.46 & 71.25 & 273.40 & 0.86 & \(-\)2.45 & 80.06 \\
2010 & 257.45 & 0.80 & 1.12 & 80.36 & 265.18 & 0.82 & 0.26 & 81.46 & 306.04 & 0.89 & \(-\)4.26 & 87.52 \\
2011 & 215.92 & 0.60 & 3.51 & 70.60 & 225.97 & 0.63 & 2.33 & 71.93 & 290.31 & 0.79 & \(-\)5.22 & 81.19 \\
2012 & 243.86 & 0.75 & 2.98 & 79.01 & 256.14 & 0.79 & 1.48 & 80.81 & 293.26 & 0.87 & \(-\)3.03 & 86.51 \\
2013 & 221.88 & 0.61 & 4.56 & 74.68 & 227.6 & 0.63 & 3.85 & 75.47 & 296.46 & 0.82 & \(-\)4.64 & 85.83 \\
2014 & 181.54 & 0.56 & 5.81 & 60.39 & 179.37 & 0.57 & 6.06 & 60.10 & 244.45 & 0.77 & \(-\)1.54 & 69.66 \\
2015 & 197.58 & 0.72 & 3.57 & 69.65 & 213.49 & 0.76 & 1.49 & 72.19 & 256.62 & 0.85 & \(-\)4.14 & 79.55 \\
2016 & 278.65 & 0.75 & 2.53 & 69.96 & 305.26 & 0.80 & \(-\)0.75 & 100.07 & 359.77 & 0.89 & \(-\)7.45 & 109.08 \\
2017 & 244.46 & 0.67 & 1.87 & 83.97 & 275.46 & 0.75 & \(-\)1.94 & 88.52 & 343.50 & 0.88 & \(-\)10.30 & 99.43 \\
2018 & 218.49 & 0.59 & 2.08 & 79.48 & 249.05 & 0.65 & \(-\)1.73 & 83.93 & 319.78 & 0.8 & \(-\)10.53 & 95.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of the relationships between county-level winter wheat grain production and cropping areas in the CONUS from 2008–2018 from the NASS and CDL datasets. Figure 3: Comparisons between winter wheat planted areas and harvested area over CONUS from 2008–2018 at the county scale from the CDL and NASS datasets (plt_CDL, plt_NASS, hav_NASS). (**a**) Planted area, (**b,c**) planted area vs. harvested area, (**d–f**) the spatial discrepancy (relative difference, %) in 2010 for planted area (**d**), and between planted area and harvested area (**e,f**). Year 2011 is a typical drought year over the winter wheat belt, and 2016 is a wet year. Figure 4: The distributions of interannual trends of county-level winter wheat planted area over CONUS between 2008 and 2018 derived from the CDL and NASS datasets. (**a**) Trends from the CDL dataset, (**b**) trends from the NASS dataset, (**c**) \(p\)-value for the CDL dataset, S: significant, \(p\)-value \(<\) 0.05, NS: not significant, \(p\)-value \(>\) 0.05, (**d**) \(p\)-value for the NASS data, (**e**) the relationships between the trends of planted areas calculated from CDL and NASS, (**f**) the histograms of the trends of planted areas calculated from CDL and NASS.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Festa** & **Branco** & **Domings** \\ \hline  & & Penty1 acetate (34) (C) \\  & & butyl 2-methylbutanoate (34) (\(P\)) \\  & & Ethyl 2-methyl2-butanoate (44) (\(F\)) \\  & & 6-Methyl-5-hepten-2-ol (AL) (84) (C) \\ Propy1 propionate (19) \({}^{a}\) (\(F\), \(J\)) & 2-Methylpropyl-2-methylbutanoate (35) (\(F\), \(J\)) & Decanal (A) (B) (\(J\), \(C\)) \\  & & 2-A-Heptdelai isomer (A) (82) (\(F\), \(J\)) & 2-Nonanol (AL) (91) (C) \\  & & Formic acid (87) (\(F\), \(J\)) & Dhydro-2-methyl-3(2H)-thiophene (93) (C) \\  & & 2-Nonanol isomer (94) (\(F\), \(J\)) & Ethyl 3-methylbitropionate (99) (C) \\  & & Linadool (95) (\(F\)) & 2-(2-EEnvoxyoxy)ethanol (105) (C) \\ Benzyl alcohol (130) (C) & & Butanoic acid (104) (C) & Allyl methyl sulphide (108) (C) \\  & & Estragole (113) (\(F\)) & Citronello1 (123) (C) \\  & & Geranylacetone (129) (C) \\  & & Benzodiazep (134) (\(J\)) \\  & & Isopertiyl-phenyl acetate (136) (\(J\), \(C\)) \\  & & Nenfoldiol (138) (C) \\ \hline \hline \end{tabular} (C), cider; (F), fruit; (J), juice. \({}^{a}\) Numbers in brackets match with numbers assigned for VOCs listed in Table 1.  
\end{table} 
Table 2: Volatile organic compounds (VOCs) identified only in specific apple varieties (Festa, Branco, and Domingos). identified in apple-juice samples from Madeira as a putative biomarker for the discrimination of the geographical origin of apple juices [13]. Furthermore, the conversion mechanism of benzyl alcohol to toluene in fruit juices was also recently reported [33]. On the other hand, there are VOCs that allow for distinguishing apple varieties. In this sense, (Z,E)-\(\alpha\)-farnesene was only identified in 2 varieties (Festa and Branco) about 100 times less than another isomer ((E,E)-\(\alpha\)-farnesene) [34]. In this context, in a previous study, (Z,E)-\(\alpha\)-farnesene was able to differentiate banana plant cultivars since this VOC was detected in the Pacific plantain cultivar, but not identified in Cavendish cultivar, whereas (E,E)-\(\alpha\)-farnesene was detected in both banana cultivars [35]. Hence, (Z,E)-\(\alpha\)-farnesene might be used as a putative marker to discriminate apple-fruit varieties for food authenticity purposes. The same applies in the case of linalool, which, in the banana study mentioned above, was only detected in Pacific plantain and, in the current study, was only identified in Branco fruit samples; this VOC showed insect- and disease-control properties [35], with benefits for the quality of apple fruits. Both linalool and estragole could differentiate basil varieties [36]. In addition, 2-nonenal, with waxy and fatty aroma descriptors, was previously used to distinguish among 10 different fresh juibube varieties by HS-SPME/GC-MS coupled with E-nose [37]. Benzothiazole was also identified as a putative marker for distinguishing apple varieties from Madeira in a previous study on apple juices recently carried out by our research group [13].