esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Irrigation Information Retrievals from Space and Their Utility for Users
## 2. Irrigation Mapping with Ground Observations and National Statistics  
The first digital global map of Irrigated Areas (GMIA, [9]) was produced at a resolution of 0.5\({}^{\circ}\) (see Figure 1a as an example for India). The map was mainly based on a compilation of outlines of main irrigation areas and the FAO total area in a country between 1990 and 1995 and is based on an inventory of subnational irrigation statistics.  
This dataset was also the base for a derived dataset including crop calendars of irrigated crops [25]. Later, [PERSON] et al. [26; 27] developed the global Historical Irrigation dataset (HID) by collecting subnational irrigation statistics from various sources to estimate the temporal development of irrigated areas between 1900 and 2005 at a resolution of 5 arcmin.  
All of these maps provide the so-called \"Area Equipped for Irrigation (AEI)\", as defined by FAO, which was inventoried by national or local authorities (Figure 1 shows an example of the AEI of the different datasets plotted over India). However, despite being very important, AEI are based upon agricultural statistics which may be outdated or inaccurate [26]. For example, the abandonment of irrigation infrastructures in former soviet countries is not accounted for, while the expansion of irrigated areas based on groundwater is often not included in the AEI. Despite these limitations, this dataset remains a reference for many potential applications, including global hydrological modeling, the modeling of changes in crop productivity, or climate impact assessments.
## 4 Methodology and Procedure  
To meet the objectives abovementioned, the responses of three steel buildings are estimated by modeling them, as stated earlier, by complex 2D MDOF frames. Therefore, higher mode contributions are explicitly considered. Such models are subjected to the action of fifteen seismic records corresponding to the seismic hazard of the model location zone. The nonlinear analyses are performed with the Ruaumoko Software [43] where the Newmark Average Acceleration Method of Newmark with a time interval (\(\Delta\)t) of 0.005  s. is used. Large displacement effects are also considered in the dynamic analysis. The results of the \(M_{L}\) matrix are compared to those of \(M_{C}\). The Rayleigh damping model is used, where the \(K_{t}\) matrix and 3% of viscous damping (\(\zeta\) = 3%) are assumed in the construction of the damping matrix. This amount of damping is fixed in the first and second modes for the smaller model; such modes are 1 and 3, and 1 and 6 for the mid and high models, respectively.  
Damping represents a very controversial issue within the seismic analysis of buildings. There is some available information regarding the evaluation of the damping ratio. From earthquake-vibration tests of buildings deformed below yield point [44] it was found that the damping ratio ranged from 2.9% to 7.0% for the first mode while the range of variation goes from 1.0% to 5.9% for the second mode. In the same reference, taking into account that other sources of energy dissipation are considered in the \(C_{R}\) matrix, conservative values of 2-3% and 5-7% are suggested for stress levels of half of yield, and yield point, respectively. In another research [45] it was found that for small amplitudes of vibration the damping ratio for the fundamental mode of vertical vibrations approximately varies from 2% to 5% for R/C buildings, and from 0.5% to 2% for the steel buildings. Based on this information and on the fact that many seismic codes suggest using a damping ratio of 5% for all sources of energy dissipation, including that of inelastic behavior, a value of 3% seems to be reasonable and conservative for steel buildings. It is worth to mention that in many research projects, whose results have been published in major journals, a value of 3% is assumed.  
Beam and beam-column elements were used to represent the horizontal and vertical horizontal members of the models, respectively. One, two and three structural elements are used to represent the beams. Three DOFs are considered at each node. The panel zones are assumed to be rigid. Bilinear hysteretic behavior with 3% post-yielding stiffness is considered. The combined action of axial loads and flexural moments is taken into account by the mathematical expression suggested by [PERSON] and [PERSON] [46].
## 4 Experiments and Results
### Evaluation Metrics
They provide insights into the overall accuracy and robustness of the algorithms regarding various aspects like their ability to localize objects correctly, estimate their size and orientation accurately, track their movement, and even predict certain attributes depending on the task.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 3 Methodology and Data Processing
### Quality Factors \(Q_{s}\) and \(Q_{p}\)  
The quality factor Q is a key parameter in estimating the seismic hazard because it influences the amplitude of ground motion. This parameter will help us to determine the attenuation of seismic waves for each layer. As suggested by [48; 49], in the case of a lack of precise constrains, the quality factor \(Q_{s}\) can be calculated based on the shear-wave velocity of sediments Vs using Equation (3):  
\[Q_{s}=\frac{V_{s}}{10} \tag{3}\]  
In the field of engineering seismology, the quality factor for P-waves (\(Q_{p}\)) is typically assumed to be twice \(Q_{s}\)[50].
## 2 Constitutive Model and Calibration
### Calibration of Parameters and Model Validation
#### 2.2.2 Determination of \(\lambda\), \(\sigma_{0}\), and \(\gamma_{o}\)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Layer No.**} & \multirow{2}{*}{**Soil Type**} & \(\mathbf{G_{0}}\) & \(\mathbf{E}\) & \(\sigma_{y}\) & \(\sigma_{0}\) & \(\mathbf{C}\) & \multirow{2}{*}{\(\mathbf{A}\)} & \multirow{2}{*}{\(\mathbf{\gamma}\)} \\  & & **(MPa)** & **(MPa)** & **(kPa)** & **(kPa)** & **(MPa)** & **(MPa)** & \\ \hline \(1\) (top) & Silty clay & 28.38 & 84.58 & 51.79 & 5.18 & 84.58 & 0.1 & 1814.63 \\ \hline \multirow{2}{*}{2} & Very soft silty clay & 24.73 & 73.7 & 47.46 & 5.7 & 73.7 & 0.12 & 1764.85 \\ \hline \multirow{2}{*}{3} & Very soft clay & 24.28 & 72.35 & 34.29 & 4.46 & 72.35 & 0.13 & 2425.41 \\ \hline \multirow{2}{*}{4} & Clay & 37.76 & 112.52 & 45.55 & 5.47 & 112.52 & 0.12 & 2807.38 \\ \hline \multirow{2}{*}{5} & Silty clay & 71.52 & 211.7 & 51.96 & 5.72 & 211.7 & 0.11 & 4578.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the constitutive model. Figure 7: Comparison of the kinematic hardening model and Mohr-Coulomb model with experimental test data: (**a**) \(q=60\) kPa, (**b**) \(q=70\) kPa.
## 2 Materials and Methods
### Data Collection  
All patients were requested to complete the questionnaires before implant insertion (baseline) and at 1, 2, 3, 4, and 5 years following the operation. A single evaluator performed all the clinical data collection at the Prosthodontics department of the Dental Hospital, TMDU.  
OHIP--Consisting of 19 question items and mirroring the seven conceptual subdomains present in the English version, the validated Japanese version of the Oral Health Impact Profile for edentulous (OHIP-EDENT-J) [24] was employed to assess the oral health-related QoL of edentulous patients. These conceptual subdomains are as follows: \"functional limitation\", \"pain\", \"psychological discomfort\", \"physical disability\", \"psychological disability\", \"social disability\", and \"handicap\". Participants were questioned on how frequently they had gone through a particular incident in the preceding month for each question item. Responses were recorded on a 0-4 scale (0--never, 1--hardly ever, 2--occasionally, 3--fairly often, and 4--very often). As a result, the OHIP-EDENT-J summary score, the total of all 19 question item frequencies, could vary from 0 to 76, with higher values denoting a greater degree of OHRQoL impairment. A lower score indicates a better QoL. In accordance with the four-dimension concept, we analyzed the total score rather than each domain individually [21].  
PDA--Patient's denture assessment (PDA) is a reliable and valid questionnaire for the self-assessment of complete dentures [6], gauged the multilateral effects of removable denture therapy on patient perceptions, consciousness, and sentiments towards their dentures. Unlike OHIP, this questionnaire places more emphasis on individual domains than on the overall result. Comprising 22 question items that fall under the six subscales (\"function\", \"lower denture\", \"upper denture\", \"expectation\", \"esthetics and speech\", and \"importance\"), each test item was measured using a 100 mm Visual Analog Scale (VAS), which was made up of a horizontal 100 mm line with endpoints that represented the worst-case scenario on the far left and the best-case scenario on the far right.  
Patient Satisfaction--Patients evaluated their overall level of satisfaction with their dentures by responding to the question \"How satisfied are you with your prosthesis?\", using a 100 mm VAS, ranging from 0 (completely dissatisfied) to 100 (completely satisfied)\"; therefore, greater satisfaction is indicated by a higher score.
[PERSON] defined an elicitor as a substance that stimulates the production and accumulation of biologically active components in plant cells [10]. Recent studies have expanded the definition of elicitor to include signaling molecules that induce defense responses, such as fungal polysaccharides, peptides, fermentation broth, and fungal secretions [11].  
The natural yield of secondary metabolites synthesized by _S. sanghuang_ is low and diverse. Therefore, it is important to explore methods for synthesizing more of the desired target metabolites. In recent years, the use of fungal elicitors to induce rapid and massive synthesis of target secondary metabolites in plant culture cells has become widespread. [PERSON] et al. reported the induction of biosynthesis of terpenoid indole alkaloids in cambial meristematic cells of Catharanthus roseus using _Aspergillus flavus_ fungal elicitor [12]. [PERSON] et al. selected three fungal strains (_Aspergillus niger_, _A. flavus_, and _Aspergillus oryzae_) as elicitors for mycelium or fermentation broth preparation to improve the yield of ginseng in adventitious root cultures of _Panax ginseng_. The results showed that ginseng production increased 3.52-fold with _A. niger_ elicitor prepared from mycelium compared to the untreated group [13]. [PERSON] et al. researched the impact of fungal elicitors on the biomass and asparagine production of _Centella asiatica_ multi-bud cultures in a dose- and time-dependent manner. The results demonstrate for the first time the potential application of _Trichoderma harzianum_ CF in upregulating the asiaticoside biogenic pathway in _C. asiatica_[14]. Therefore, the application of fungal elicitors to liquid fermentation cultures of medicinal fungi to increase secondary metabolite levels has a specific theoretical basis and practical significance.  
As science and technology progress, more and more methods are being utilized for research, including proteomics, metabolomics, transcriptomics, genomics, and others. In order to develop high-throughput sequencing technology and systematic biology, multi-omics skills have become essential in biological sciences [15; 16]. [PERSON] et al. reported that metabolite profiles and differentially expressed genes demonstrated significant synergistic effects on specific characteristic pathways based on the various growth stages of _Hypsizygus marmoreus_, and the flavor-related metabolites varied [17]. [PERSON] et al. analyzed the transcriptome and metabolome of two cultivars of broccoli cultivated after jasmonic acid treatment. These biomarkers of metabolites and transcripts were used in effective marker-assisted breeding strategies to screen for insect-resistant cultivars [18]. Multiomics has become an invaluable analytical tool. The application of omics technology in edible fungi is becoming more and more extensive [19; 20]. Therefore, this paper attempts to combine metabolomics and transcriptomics under conditions of elicitor treatment (ET) and without elicitor treatment (WET) to investigate the effects of fungal elicitors on the metabolites of _S. sanghuang_.
# Quality Assessment of Banana Ripening Stages by Combining Analytical Methods and Image Analysis
## 2 Materials and Methods
### Physicochemical Measurements during Banana Ripening  
An aw-meter, acquired from AquaLab Dew Point Water Activity Meter 4 TE, METERGroup, Inc., Pullman, WA, USA, was used to measure water activity of the banana-flesh samples [17].  
The moisture contents of the banana-flesh samples were measured using an electronic moisture analyser (Kern MLS 50-3, KERN & SOHN GmbH, Balingen, Germany). Each banana-flesh sample (0.2-0.4 g) was placed on a sample pan. An internal halogen dryer heated the samples, which resulted in moisture vaporization. During the drying process, the instrument continuously determined the sample mass and displayed the moisture loss. The percentages of the moisture contents were calculated based on the weight differences before and after drying. The maximum temperature was maintained at 120 \({}^{\circ}\)C for all measurements [17].  
The measurements of total soluble solids (TSS) were conducted with a hand-held refractometer (Kern Optics Analogue Brix refractometer, ORA 80 BB, KERN & SOHN GmbH, Balingen, Germany). The samples of the banana flesh were homogenized in a blender and the produced pureed samples were placed on the prism of a refractometer. The TSS were obtained as \({}^{\circ}\)Brix, as provided by a direct reading of the refractometer [17].  
The color values L* (lightnessh* (hue angle in degrees), a* (redness/greenness) and b* (yellowness/blueness) were determined by using a tristimulus chromatometer (model CR-400, Minolta, Tokyo, Japan), which had been calibrated according to [PERSON] et al., 2023. Measurements were taken at the surfaces of the banana flesh slices [17].  
The texture measurements of the banana-flesh samples were conducted using a texture analyzer, the TA-XTplusC by Stable Micro Systems (Godalming, U.K.), as described by [PERSON] et al., 2023. Adhesiveness, firmness, cohesiveness, springiness, and chewiness were the evaluated texture characteristics [18].  
The method defined by the AOAC was performed for the determination of tittatable acidity (%) [19], which was expressed as g of malic acid/100 g of the banana.  
An iodine test was used to determine the presence of starch in the banana flesh during ripening. The surfaces of the banana flesh slices, with 1.5 cm thickness, were immersed in a starch-iodine (1% potassium iodide and 0.25% iodine) staining solution for 5 s [20]. A blue-black color resulted if starch amylose was present. The starch distribution in the slices was then estimated by image analysis.  
Each measurement was carried out for eight replicates.
## 4 Strength Regression Analysis
From Table 9 and Figure 6, it can be observed that the differences between the theoretical and measured values of the compressive strength, flexural strength and flexural-compressive ratio were, respectively, 0.1-1.7, 0.02-0.53 and 0.01-0.08; which shows that the fitting equation from the linear supposition is reasonable. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Results Analysis**} & \multicolumn{10}{c}{**Specimens**} \\ \cline{3-14} \multicolumn{2}{c}{} & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) & \(\mathbf{L_{4}}\) & \(\mathbf{L_{5}}\) & \(\mathbf{L_{6}}\) & \(\mathbf{L_{7}}\) & \(\mathbf{L_{8}}\) & \(\mathbf{L_{9}}\) & \(\mathbf{L_{10}}\) & \(\mathbf{L_{11}}\) & \(\mathbf{L_{12}}\) & \(\mathbf{L_{13}}\) & \(\mathbf{L_{14}}\) & \(\mathbf{L_{15}}\) & \(\mathbf{L_{16}}\) \\ \hline \multirow{3}{*}{\(f_{\text{ct}}\)} & Measured & 10.1 & 6.10 & 3.30 & 1.70 & 1.80 & 7.60 & 5.70 & 4.10 & 3.20 & 4.40 & 4.70 & 1.80 & 3.00 & 8.80 & 3.30 & 4.90 \\  & Theoretical & 8.81 & 5.92 & 3.34 & 0.71 & 3.40 & 6.22 & 4.25 & 4.25 & 3.48 & 3.35 & 5.67 & 3.21 & 3.93 & 7.10 & 4.06 & 5.64 \\  & Deviation & 1.2 & 0.18 & 0.04 & 0.99 & 1.60 & 1.38 & 1.55 & 0.10 & 0.28 & 1.05 & 0.97 & 1.41 & 0.93 & 1.70 & 0.76 & 0.74 \\ \hline \multirow{3}{*}{\(f_{\text{t}}\)} & Measured & 2.40 & 1.50 & 1.30 & 0.50 & 0.70 & 1.80 & 1.40 & 1.20 & 1.10 & 1.20 & 1.10 & 0.60 & 0.90 & 2.20 & 0.80 & 0.90 \\  & Theoretical & 2.11 & 1.52 & 1.00 & 0.48 & 1.13 & 1.29 & 1.06 & 1.06 & 0.96 & 1.57 & 1.4
# Computational Investigations and Control of Shock Interference +
## 3 Model Validation
### Computational Domain and Mesh  
The IDRL backpressured isolator was 24 inches long, 2 inches wide, and 1 inch high. The structured mesh dimensions for this isolator were 1000 x 80 x 104. The streamwise spacing was held fixed at 0.02 x isolator height (h) for the front half of the isolator to resolve the region where the shock-train was expected to reside, and was stretched in the second half of the isolator to a value of 0.05 h at the isolator exit. The maximum grid spacing in the vertical (y) and spanwise (z) directions was 0.02 h (adjacent to the planes of symmetry). The grid was split into a total of 1488 grid blocks with 95% load balance on 720 processors. Grid sensitivity studies were performed with three structured grids (Grid 1: 1.3 million, Grid 2: 10 million, and Grid 3: 85 million). It was found in a previous work by [PERSON] et al. [22] that grid resolution with Grid 2: 10 million structured produced the same results as Grid 3: 85 million structured mesh, within measurement error. Therefore, it was determined that Grid 2, with 10 million grid points, is appropriate for this demonstration. The isolator schematic with the inflow, outflow, and symmetry planes is shown in Figure 6. In this work, the facility nozzle was not simulated, but the isentropic nozzle exit flow conditions were utilized as the boundary condition.
## 2 The Principle of the Invariant Imbedding (IIM) T-Matrix Method
### Discretization and Vectorization of the Helmholtz Volume Integral Equation
\begin{array}{rl}\overline{\mathbf{G}}_{0}(\mathbf{r},\mathbf{r}^{\prime})&= ik\overline{\mathbf{Y}}_{mn}(\theta,\varphi)\overline{\overline{\mathbf{H}}}_{n}( kr)\overline{\overline{\mathbf{J}}}_{n}^{T}(kr^{\prime})\Big{(}\overline{ \overline{\mathbf{Y}}}_{mn}(\theta^{\prime},\varphi^{\prime})\Big{)}^{T}\\ &=\overline{\overline{\mathbf{Y}}}_{mn}(\theta,\varphi)\overline{\overline{ \mathbf{S}}}_{n}(r,r^{\prime})\Big{(}\overline{\overline{\mathbf{Y}}}_{mn}( \theta^{\prime},\varphi^{\prime})\Big{)}^{T}\end{array}  \tag{17}\]  
\[\overline{\overline{\mathbf{s}}}_{n}(r,r^{\prime})=ik\overline{\overline{ \mathbf{J}}}_{n}(kr)\overline{\overline{\mathbf{H}}}_{n}^{T}(kr^{\prime}) \tag{18}\]  
On the condition that \(r=r^{\prime}\), the expansion coefficients of \(\overline{\mathbf{G}}_{0}(\mathbf{r},\mathbf{r}^{\prime})\) are taken as the average value of those for \(r>r^{\prime}\) and \(r<r^{\prime}\). In this case, the matrix form of the dyad Green's function is similar to that of Equation (15), but the matrix \(\overline{\overline{\mathbf{s}}}_{n}(r,r^{\prime})\) should be rewritten as:  
\[\overline{\overline{\mathbf{s}}}_{n}(r,r^{\prime})=ik\Big{(}\overline{\overline{ \mathbf{J}}}_{n}(kr)\overline{\overline{\mathbf{H}}}_{n}(kr^{\prime})+\overline {\overline{\mathbf{H}}}_{n}(kr)\overline{\overline{\mathbf{J}}}_{n}(kr^{ \prime})\Big{)}/2 \tag{19}\]  
b. The discretization of the volume integral equation. Substitute the expansion equations of the field components and the dyadic Green's function into Equation (6), and use the orthogonality of the vector spherical harmonic function for simplification, then, the following integral formulas can be obtained for each \((m,n)\), written as:  
\[ \begin{array}{l}\mathbf{E}_{m^{\prime}n^{\prime}}(r,\theta,\varphi)=\overline{ \overline{\mathbf{Y}}}_{m^{\prime}n^{\prime}}(\theta,\varphi)\overline{ \overline{\mathbf{J}}}_{n^{\prime}}(kr)\\ +\int\limits_{V}dr^{\prime}r^{\prime}d\Omega^{\prime}\sum\limits_{n=1}^{\infty} \sum\limits_{m=-n}^{n}\overline{\overline{\mathbf{Y}}}_{mn}(\theta,\varphi) \overline{\overline{\mathbf{S}}}_{n}(r,r^{\prime})\Big{(}\overline{\overline{ \mathbf{Y}}}_{mn}(\theta^{\prime},\varphi^{\prime})\Big{)}^{T}u(\mathbf{r}^{ \prime})\overline{\overline{\overline{\mathbf{Z}}}}(\mathbf{r}^{\prime})\mathbf{E} _{m^{\prime}n^{\prime}}(\mathbf{r}^{\prime})\end{array}  \tag{20}\]For further simplification, we define the vector amplitude density function \(\mathbf{F}_{mmm^{\prime}n^{\prime}}(r)\) as:  
\[\mathbf{F}_{mmm^{\prime}n^{\prime}}(r)=\int\limits_{\Omega}\left(\overline{ \mathbf{\overline{Y}}}_{mm}(\theta,\varphi)\right)^{\!\!T}\!\!u(\mathbf{r}) \overline{\mathbf{\overline{Z}}}(\mathbf{r})\mathbf{E}_{m^{\prime}n^{\prime}} (\mathbf{r})d\Omega \tag{21}\]  
This parameter is related to the electric current density and can be thought of as the source of the scattered electric wave.
## 1 Introduction
### Contribution of This Paper  
In this paper, we conduct a comprehensive channel characterization and comparison in the industrial scenario from the sub-6 GHz to visible light bands. The main contributions are as follows:  
(1) The multi-frequency channel simulations were conducted in the same industrial scenario with the same configuration for both the electronic bands and the visible light bands. The frequency range covered the sub-6 GHz, mmWave, THz (i.e., 3.5 GHz, 6 GHz, 28 GHz, 60 GHz, 220 GHz, 260 GHz, 300 GHz) and visible light bands, which could provide channel data for a comprehensive channel characterization and comparison in the industrial scenario.  
(2) We made a comprehensive comparison of the channel characteristics for multiple electronic bands and visible light bands in the industrial scenario, including the path loss, shadow fading, RMS DS, and AS. The frequency dependence and the distance dependence were analyzed based on the industrial features and frequency properties.  
(3) The statistical characteristics of the channel properties were modeled and compared with the 3 GPP TR 38.901 standard [28]. Furthermore, we extended the 3 GPP model to the THz and visible light bands and provided the statistical parameters of the channel characteristics for all the frequency bands.
## 5 Experiments and Results
### Statistical Results and Convergence Speed  
The previous section showed the accuracy of the DLMVO algorithm for estimating model parameters. This section mainly introduces the robustness and converge speed of different algorithms. We used the [PERSON]'s signed-rank test with a significance level of 0.05 to compare the significance between the DLMVO and every competitor algorithm [49]. Symbols \"+\", \"-\", and \"\(\approx\)\" show that the performance of the DLMVO is significantly better than, worse than, or similar to the corresponding algorithm, respectively. The experimental results over 30 independent runs of three case studies are provided in Table 6, which include minimum RMSE (Min), maximum RMSE (Max), mean RMSE (Mean), and standard deviation of RMSE (Std), where the best result is highlighted in boldface. From Table 6, for SDM, although Kr-JADE, IWOA, PGJAYA, and SATLBO could obtain the minimum RMSE, their performance was not as good as the DLMVO considering the mean RMSE and standard deviation of RMSE. For DDM, Rcr-JADE could also obtain the most negligible RMSE value, but it is still less stable than DLMVO. For SMM, although most competing algorithms can obtain the minimum RMSE, the standard deviation of RMSE by the DLMVO remained better than others. Meanwhile, according to the result of the statistical test (Sig), the DLMVO algorithm is the most reliable algorithm in parameter identification for any model. To more intuitively show the distribution of results obtained by different algorithms in 30 independent runs, the box diagram of three cases by each algorithm is shown in Figure 9. By comparing the distribution of optimal solutions, we found that the DLMVO was better than other compared algorithms in terms of accuracy and robustness.