esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Soil Heterogeneity on the Contact Problems in Geotechnical Engineering
## 1 Introduction
It was observed that the maximum difference in soil moisture values could reach 30% near the surface soil layers, due to an increase in soil heterogeneity on irrigation days. Hicks and Li [23] studied the effect of the heterogeneity in undrained shear strength on a 3D slope stability. ElGhoraiby and Manzari [24] evaluated the combined effect of the spatial variability in the soil density and base motion on the lateral spread in the numerical simulation of centrifuge modeling. All this research has shown that the influence of soil heterogeneity should be considered. Therefore, in order to investigate the effect of soil heterogeneity on the elastic contact problems, a simple contact problem of an elastic body on a rigid foundation is analysed in this paper and the penalty method is taken to solve the contact problem. In addition, Young's modulus of the elastic body is considered to be heterogenous. The local average subdivision (LAS) is used to simulate the heterogeneity of Young's modulus. Compared to other methods of generating random field, such as the turning bands method and the fast Fourier transform, the LAS was shown to be more efficient and reliable [25]. Moreover, it is ideally suited for use with the finite element method (FEM). By linking the penalty method with the LAS, the disadvantage of the conventional analysis of the contact problems mentioned above can be overcome. In this paper, firstly, the penalty method and the LAS are briefly introduced. Then, an illustrative example is presented. The results of the homogeneous and heterogeneous cases are compared with each other. Finally, the influence of the statistics of Young's modulus, i.e., the standard deviation and the scale of fluctuation, on the contact force is investigated.
## 4 Discussion
### Effects of Urea-FA Solution Combined Application on Soil Available Nutrients, Maize Yield, and NUE
Compared with the U treatment, the U+FA treatment displayed higher levels of IAA and GA in maize leave at the jointing stage (Figure 3), indicating better growth and development of the leaves in this treatment. Therefore, the combining urea and FA solution resulted in higher concentrations of endogenous hormones, which stimulated the growth and development of maize. Aliphatic carboxylic acids and aromatic carboxylic acids are two structural components of FA. Similar to plant hormones [64; 65], they can induce the expression of N transporter genes (ZmNrt2.1 and ZmNrt1.1) and H\({}^{+}\)-ATPase subtypes (Mha1 and Mha2) [66]. Therefore, they can regulate N metabolism, boost the concentrations of NR, NiR, GS, and GOGAT, and enhance the nutrient absorption capacity of maize [67; 68]. The enzymes NR and NiR catalyze the transformation of NO\({}_{3}\)\({}^{-}\) to NO\({}_{2}\)\({}^{-}\) and NH\({}_{4}\)\({}^{+}\) and regulate the absorption and utilization of nitrogen by plants. The enzymes GS and GOGAT catalyze the reaction between glutamine and \(\alpha\)-ketoglutate to produce glutamate [11], which can be transported into the root tip meristem through extracellular transport, promoting root cell division and nutrient absorption [10]. Compared with the U treatment, the U+FA treatment displayed significantly enhanced concentrations of NR, NiR, GS, and GOGAT in maize root at the V12 stage. Furthermore, Mantel test revealed that the activities of N metabolism-related enzymes were significantly correlated with plant endogenous hormones and yield. In summary, the U+FA treatment increased the concentrations of N metabolism-related enzymes, which stimulated N metabolism, promoted plant growth, and contributed to yield increase.
## 3 Results  
### Accuracy  
An overall number of 88 implant positions were compared with the relative pre-operative planned ones. An overall significant level of accuracy resulted for the first operator, compared to the second, from the mean values from the three evaluation methods (_p_ < 0.05) (Table 1). According to the method nr.1, _Ed_ = 0.8 \(\pm\) 0.43 mm, _Ad_ = 0.85 \(\pm\) 0.39 mm, and _Ang_ = 1.05 \(\pm\) 0.53\({}^{\circ}\) were reached by the first operator; and _Ed_ = 0.91 \(\pm\) 46, _Ad_ = 1.02 \(\pm\) 0.45, and _Ang_ = 1.78 \(\pm\) 1.12\({}^{\circ}\) by the second one. The overall mean values were: _Ed_ = 0.86 \(\pm\) 44, _Ad_ = 0.93 \(\pm\) 43, and _Ang_ = 1.41 \(\pm\) 0.95\({}^{\circ}\). Considering each vectorial component, the second operator obtained more minimal values than the first one for all the variables except the _Ay_ one, all compensated by the higher maximum data. According to the method nr.2, _Ed_ = 0.45 \(\pm\) 0.23 mm, _Ad_ = 0.48 \(\pm\) 0.24 mm, and _Ang_ = 1.03 \(\pm\) 0.47\({}^{\circ}\) were reached by the first operator; and _Ed_ = 0.87 \(\pm\) 45, _Ad_ = 0.95 \(\pm\) 0.42, and _Ang_ = 1.58 \(\pm\) 0.97\({}^{\circ}\) by the second one, with _Ed_ = 0.66 \(\pm\) 41, _Ad_ = 0.72 \(\pm\) 42, and _Ang_ = 1.31 \(\pm\) 0.81\({}^{\circ}\) as the mean values between each of them. The second operator obtained more minimal deviations than the first one, but for fewer variables, e.g., _Ed_, _Adx_, _Adv_, and _Ang_, than in method nr.1. According to method nr.3, _Ed_ = 0.47 \(\pm\) 0.27 mm, _Ad_ = 0.49 \(\pm\) 0.25 mm; and _Ang_ = 1.05 \(\pm\) 0.57\({}^{\circ}\) were reached by the first operator; and _Ed_ = 0.77 \(\pm\) 34, _Ad_ = 0.89 \(\pm\) 0.36, and _Ang_ = 1.58 \(\pm\) 0.83\({}^{\circ}\) by the second one, with _Ed_ = 0.62 \(\pm\) 34, _Ad_ = 0.69 \(\pm\) 37, and _Ang_ = 1.31 \(\pm\) 0.76\({}^{\circ}\) as the mean values between each of them. An identical value of _Edx_, 0.15 \(\pm\) 0.1, was identified for both operators. The second operator's minimal deviations, as reported above with the previous methods, regarded the _Edx_, _Ed_, and _Ad_ variables (Table 2).
## 2 Materials and Methods
### Osmotic Dehydration (OD) of Cassava Bagasse  
The operating parameters and levels to be assessed were initially selected according to the scientific literature consistent with a technological surveillance study referring to the application of OD as a pretreatment of the convective drying of different agro-industrial products and later adjusted according to the particular behavior of bagasse when it is subjected to this type of processes.  
The OD operation was performed following the methodology described by [PERSON] et al. [29] and [PERSON] et al. [30]. Samples of 300 g of fresh cassava bagasse were taken, and osmotic solutions were prepared with concentrations of 25%, 50%, and 75% NaCl; then, the mixture of cassava bagasse-osmotic solution was made considering the relative solution to sample ratio of 4:1, 6:1, and 8:1 respectively. Samples were constantly shaken during the entire immersion time. The stirring speed in revolutions per minute used was defined experimentally based on the need for the mixture to remain homogeneous under environmental conditions of 1 atm and approximately 30 \({}^{\circ}\)C; testing stirring speeds between 250 rpm and 500 rpm. The OD operation time evaluated was 1.5, 3, and 4.5 h. The moisture of the samples was determined according to the oven method 977.11 AOAC [27]. A drainage water test was performed to determine the mesh number and adequate drain time for the sample. For this, solution loss tests as a function of time were carried out, working with Tyler sieves with different numbers and mesh openings. Mesh number and draining time were determined from the capacity with which the sieves allowed the passage of the solution and efficient retention of solids or bagasse. For this, Tyler #30, #20, and #10 scale sieves with mesh openings of 0.595 mm, 0.841 mm, and 2 mm, respectively, were used. Next, 350 g of cassava bagasse were taken per sample and spread over each of the sieves; then they were submerged in a container with the osmotic solution for the time defined for the OD. At the end of this time, they were removed and allowed to drain until no water migration was observed through the sieve bottom (approximately 2 min). Finally, sieves were weighed to determine the solution loss in each sample at time intervals of 10, 15, 20, 25, and 30 min.  
After the draining operation, samples were carefully treated with absorbent paper, to then measure the moisture by the oven method according to the AOAC [27].  
The factors significantly influencing moisture loss during the OD process were determined from a sieving design 23 corresponding to 3 factors with the respective maximum and minimum levels selected. Factor 1: Osmotic agent concentration: 25% and 75% NaCl; Factor 2: Solution to sample ratio: 4:1 and 8:1 g solution/g sample; Factor 3: Immersion time: 1.5 h and 4.5 h. Data processing was carried out through the STATGRAPHICS Centurion XIX software v. 19.1.01, with a 95% confidence.
## 2 Materials and Methods
### Phytoplankton Community Identification and Enumeration  
Identification and counting of preserved phytoplankton samples was carried out following the technique developed by [PERSON] [80]. Preserved phytoplankton samples were well mixed before transferring them to a 25-mL sedimentation chamber. Chambers were settled for at least 18 h. Identification and enumeration were performed under an inverted microscope (Leica(r) Microsystems, Ireland). A camera linked with the microscope enabled accurate phytoplankton measurements from images by using a calibrated stage micrometre available in the Leica Application Suite imaging software (LAS V4.12). Two counts were carried out on each sample, a count of large species at x40 magnification, and a high magnification count at x400 for small species. At x40 magnification, the whole chamber was counted to include large species or colonies which may not be numerous enough to be captured by the subsequent count at high magnification. Counts at x400 magnification were carried out over two transects of the chamber base. Samples were identified to genus and species level when possible [81; 82]. Biovolumes were then calculated in order to compare the relative contributions of different algae samples [83]. This was done by multiplying the number of cells for each species present in a sample by its average cell volume. Average cell biovolume was calculated by measuring 10-20 individuals of each species from each sample. Fewer individuals were measured for rare species.
## 4 Proposed Adaptive Scheduling Technique
To accommodate the high traffic generated by nodes one hop away from the root, segment 0 can be utilized additionally alongside the last segment. This strategy provides an extra opportunity for such nodes to forward packets without causing interference with pre-allocated transmissions. By using this segmentation approach, cells are scheduled in consecutive segments according to the node's hop distance from the root. The minimal-length slotframe segment is determined based on the number of pending packets in the transmission queues and the number of active links at a given level of the RPL DODAG tree. In this work, the traffic load is predicted by using the current queue backlog at each node along with the hop distance traffic generation in the RPL topology. Since nodes closer to the root experience higher traffic due to forwarding packets of their descendants, our algorithm assumes traffic increases proportionally as the hop distance to the root decreases. The collective traffic arriving at a node, from its children and itself, is an effective predictor of upcoming load. This estimated load impacts the number of cells allocated, using the logarithmic mapping in Equation (4) to scale scheduling based on queue occupancy and links PDR. The channel offset for the application data packet is determined by using the sender's MAC address and the total number of available channel offsets, as expressed by  
\[\mathit{co}_{uc}(k)=\mathit{hash}[k_{MAC}]\%\mathit{C}_{mu}+1 \tag{9}\]  
The technique utilizes channel offsets \(1,\;2,\) and \(3\) for application data. The channel offset for the EB frame is fixed at \(0\), while for RPL it is set to \(1\). The time offset for the EB frame is determined based on the sender's MAC address, whereas the time offset for RPL remains constant at \(0\).
## 2 Materials and Methods
### Data Preprocessing
#### 2.3.3 Elimination of Low-Frequency Component  
It is crucial to acknowledge that the signal of a helix deviation often comprises a waviness component, which pertains to a low-frequency element. As significant low-frequency components of the signal can substantially impact the outcome of the correlation function analysis, it is crucial to consider the elimination of the low-frequency component of the helix deviation signal. Equation (3) illustrates the Fourier transform employed on the helix deviation curve, \(f_{Hgi}(x)\), following the removal of the slope component.  
\[F_{Hgi}(\omega/2\pi)=\int_{-\infty}^{\infty}f_{Hgi}(x)\cdot e^{-i\cdot\omega x}dx \tag{3}\]  
Subsequently, the largest waviness component, \(F_{Hgi\;low}\), of the tooth helix deviation after being Fourier transformed is then eliminated by utilizing the following Equation (4). The Fourier transformed helix deviation signal following the elimination of the largest waviness component is regarded as \(F_{gi\;eli}\) as illustrated in Equation (4).  
\[F_{gi\;eli}=F_{Hgi}(\omega/2\pi)-F_{gi\;low} \tag{4}\]  
In consequence, the helix deviation after the elimination of the largest waviness component, \(F_{gi\;high}(x)\), is then derived by applying inverse Fourier transform to the Fourier transformed helix deviation signal following the elimination of the largest waviness component \(F_{gi\;eli}\) as illustrated in the following Equation (5).  
\[F_{gi\;high}(x)=\int_{-\infty}^{\infty}F_{gi\;eli}\cdot e^{i\cdot\omega x}d\omega \tag{5}\]
## 3 Results  
Figures 4 and 5 showcase the 3D-printed chest phantom, while Figure 6 presents a comparative CT slice of the phantom alongside that of the patient. Figure 7 presents the measurement errors between patient and phantom scans. The mean error in anatomical measurements was \(-\)0.03 \(\pm\) 0.14 cm, which represents a 1.02% disparity. Table 3 presents HU measured for each of the printed thoracic tissues compared to those of the patient. The complete phantom weighed 22 kg.  
Figure 4: 3D-printed chest phantom. (**A**): Frontal view of the 3D-printed model with ribs and muscle exterior visible. (**B**): Interior of 3D-printed model, consisting of heart, neck vessels, trachea, hollowed posterior lung compartments, pulmonary vessels, lung nodules, diaphragm, liver, and stomach. (**C**): Superior view of complete phantom in CT scanner, with skin exterior visible. (**D**): Lateral view of complete phantom within CT scanner.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Tissue** & **Mean/Median HU in Patient** & \begin{tabular}{c} **Standard HU** \\ **Reported in the Literature** \\ \end{tabular} & \begin{tabular}{c} **Mean/Median HU in** \\ **Phantom** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Difference** \\ **Between Phantom** \\ **and Patient** \\ \end{tabular} & \(p\) **Value [95\% CI]** \\ \hline Lung & \(-853\pm 54.6\) & \(-550\) to \(-950\)[42] & \(-987\pm 124\) & \(-116\) & \(0.105\,[-143,\,35.9]\) \\ \hline Heart & \(35\pm 8.24\) & \(20\)-\(50\)[45] & \(-81.3\pm 14.1\) & \(-116\) & \(p<0.001\,[-131,-102]\) \\ \hline Blood vessels & \(48.1\pm 9.94\) & \(26.52\pm 12.52\)[46] & \(-69.7\pm 110\) & \(-114\) & \(0.024\,[-168,-12.7]\) \\ \hline Diaphragm & \(45.6\pm 32.4\) & \(30\) to \(150\)[47] & \(-65.7\pm 93.6\) & \(-111\) & \(0.066\,[-232,\,9.96]\) \\ \hline Bone & \(273\pm 87.3\) & \(226\)-\(3071\)[40] & \(77.1\pm 38.5\) & \(-196\) & \(p<0.001\,[-283,-110]\) \\ \hline Lung nodules & \(41.9\pm 28.9\) & \(-20\) to \(165\)[48] & \(36\pm 53\) & \(-4.59\) & \(0.557\,[-13.5,\,19.8]\) \\ \hline Skin & \(71\pm 14.9\) & \(70\) & \(12.5\pm 67.6\) & \(-58.5\) & \(0.006\,[-95.2,-17]\) \\ \hline \end{tabular}
\begin{tabular}{c} Mann–Whitney U tests performed for muscle, fat, lung, blood vessels, skin, and lung nodules, with corresponding averages expressed as medians \(\pm\) interquartile range (IQR). Student’s \(t\)-test performed for pulmonary vessels, airways, heart, diaphragm, and bone, with corresponding averages expressed as means \(\pm\) standard deviation (SD). \(p<0.05\) indicates statistically significant difference between mean or median values; \(p>0.05\) indicates statistically significant radiation equivalence.  
\end{table}
Table 3: _Cont._
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 1 Introduction
Since tracing and investigation of the source of the green tide remains an important first step toward its early prevention and control of green tide, so another purpose of this study is to use multi-source RS data to expand the monitoring range of the Yellow Sea green tide source, such as the southeastern Yellow Sea. In terms of green tide prevention and control, in addition to tracing and early control of green tide sources, it is important to predict and assess subsequent green tide growth trends. The development of a green tide is dynamic, so an annual outbreak is not appropriate to define or assess only by some static indicators like the accumulative or the maximum cover area, which may lead to the wrong prediction and evaluation of the bloom scale of green tide [18]. Therefore, many numerical models have been introduced to predict the growth trends of green tides. For example, the physical-ecological model based on the FVCOM model is used to simulate the distribution and growth of green tides [19]. The LTRANS model, coupled with an ecological module, is constructed to predict the drift path, growth, and dissipation processes of green tide [20]. The pixel unmixing method was used to further estimate the coverage area of _U. prolifera_, so as to obtain a more accurate scale of the green tide [21]. Due to the limitations of the marine environment and resources, the growth curve of algal development is generally S-shaped [21; 22]. Therefore, the growth curve of the green tide is also widely used to model and predict algal growth trends. To predict the growth scale and duration of the Yellow Sea green tide based on Gompertz and Logistic growth curve is also one of the main objectives of this study. This work is organized as follows: Section 1--Introduction; Section 2--Description of the dataset; Section 3--Method, the methodology of the study is explained in detail; Section 4--Results, the results for different RS datasets are presented, the accuracy assessed, the green tide's growth curves calculated, forecast curve for 2022 estimated, tide's bloom times and accumulative/maximum cover area simulated; Section 5--Discussion, statistics of source sites of the green tides in period from 2008 to 2022 is analyzed, and Section 6--Conclusions.
# The Elasto-Plastic Contact Models of Cylinder-Based and Sphere-Based Fractal Rough Surfaces
###### Abstract  
The elasto-plastic contact models of cylinder-based and sphere-based fractal rough surfaces are developed. In the two models, the critical contact areas of a single asperity are scale-dependent. With an increase in the contact load and contact area, a transition from elastic, elasto-plastic to full plastic deformation takes place in this order. The truncated asperity size distribution functions of different frequency indexes in different contact zones are deduced. The relations between the total real contact area and total contact load for cylinder-based and sphere-based fractal rough surfaces are obtained. The pressure distributions in the contact zone are obtained. The results of elasto-plastic contact models show that the mechanical property of cylinder-based and sphere-based fractal rough surfaces depends on the range of the frequency index of asperities. When the first six frequency indexes are smaller than the elastic critical frequency index, the cylinder-based and sphere-based fractal rough surfaces approximately appear to have an elastic property in the complete contact process. When the minimum frequency index is greater than the elastic critical frequency index, elastic deformation first takes place in the rough surfaces. Then, elasto-plastic deformation takes place with an increase in the total contact load. In elastic deformation, the ratios of the peak pressures of present fractal models to those of Hertzian models are constant for a given range of frequency indexes. In inelastic deformation, the ratios of the peak pressures are inversely proportional to the total contact load.  
Keywords:rough surfaces; Weierstrass-Mandelbrot function; scale dependent; elasto-plastic; frequency index +
Footnote †: journal: Applied Sciences
## 4 Experiments and Results
### Comparison of the Term-Document Matrix Definition  
In this work, we investigate the optimum number of term-document matrix definitions, which is the optimum number of words to be defined in one document to allow deterministic resource allocation to be applied onto the algorithm for the sake of processing performance. A document can be defined as a sentence, a paragraph, or a fixed number of words in a sequence. In the developed system, we defined the term-document matrix based on a fixed number of words in a sequence; in this experiment, we varied the number of words to 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, and 25 words. The results of this experiment can be seen in Figures 7 and 8.  
Figure 7 shows the impact of decreasing the values of precision, recall and F-measure on increasing the number of words used in the term-document matrix. According to the human rater's assessment, the number of words in the document that should be declared plagiarism is 192 out of 4050 words.  
Figure 8: Precision, recall and F-measure results over various term–document matrix definitions.  
Figure 7: The number of true positive results using Learning Vector Quantization.  
This means that there are 3858 words that are not considered plagiarism. The system became less effective in detecting plagiarism when the term-document matrix increased. A bigger term-document matrix size will suppress the number of false positives and false negatives, but the weight of the harmonic precision and recall is reduced. Thus, the LSA precision and relevance of LSA as well as the accuracy of the detection tend to be worse when the size is larger.