esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 2 Materials  
### Study Area  
The BARC site (Figure 1) was selected for developing the cover crop termination algorithm because it represents a large diversity of cover crop types and termination dates, which is ideal for algorithm development and validation. BARC is a United States Department of Agriculture (USDA) Long-Term Agroecosystem Research (LTAR) network research site in Maryland that includes \(\sim\)2670 - hectares of agricultural fields, pastures, woodlands, and wetlands [28]. Agricultural field sizes vary from 1 to more than 20 hectares. Both winter and summer crops are planted at BARC. Major winter crops are wheat, barley, and rye grown for grain or as winter cover crops. Major summer crops are corn and soybean. Alfalfa grows through the entire year and can be harvested multiple times during the growing season. Winter cover crops are planted in fall after summer crops have been harvested in the previous year. Winter cover crops emerge in fall, grow through winter, and often are abundantly green by the next spring. Winter crops and weeds are killed with herbicide application and/or moving prior to or shortly after summer crops are planted. The BARC site keeps a comprehensive record of field operations, such as planting and termination/harvesting dates.
## 4 Numerical Simulation Method
### Boundary Conditions  
For all the problems studied, the following boundary conditions were assumed: the inlet boundary condition was \"velocity-inlet\", the outlet boundary condition was \"outflow\", and the wall boundary condition was default.  
walls \[U=0,W=0,V=0\] (8)  
inlet \[U=U_{0}\frac{(n+1)\cdot(2n+1)}{2n^{2}}\left(1-\frac{\sqrt{\left(x+y\right)^{2} }}{D}\right)^{\frac{1}{n}},V=0,W=0\] (9)  
\[k=\frac{3}{2}\left[U_{m}\cdot 0.16(\mathrm{Re})^{-\frac{1}{8}}\right]^{2}, \varepsilon=0.164\frac{k^{\frac{3}{2}}}{0.07D} \tag{10}\]  
outlet  
\[\frac{\partial}{\partial z}(U,V,W)=0 \tag{11}\]  
where \(n\) was the well-known function of Reynolds number, while \(k\) and \(\varepsilon\) could be evaluated from the turbulent intensity and length scale. For the simulation of a viscous layer near the walls, simplified models were used as well as the standard wall functions, in which a velocity profile was assumed on the basis of well-known empirical relations. The equations for the turbulent flow were solved outside this layer (1976).  
The code adopted for the numerical solution of the problem described above was based on the finite volumes technique. Finite volumes allowed, respectively to traditional finite difference schemes, modeling of complex geometry by using unstructured grids (1976).  
The discretized algebraic equations obtained from the finite volume procedure were solved using a semi-implicit algorithm, SIMPLEC, which was derived from that originally devised by [PERSON] [47]. The second-order up-winding scheme was used for all velocity terms in the momentum equation, and second-order interpolation was also used for pressure.
## 4 Effective Bending Stiffness
### \(N\)-Layered Plate with Surface Stresses  
A similar approach can be applied to multi-layered plate. In order to get the effective bending stiffness, we calculate the 2D strain energy density \(\mathbb{E}\). Here, \(D_{eff}^{0}\) is given by (21), whereas \(\mathbb{S}_{S}\) can be calculated as follows  
\[\mathbb{E}_{S}= \mathcal{U}_{0}\bigg{|}_{z=-h/2}+\sum\limits_{i=1}^{N}\mathcal{U} _{i}\bigg{|}_{z=-h/2+\sum\limits_{k=1}^{i}h_{k}}, \tag{25}\]  
which results in the following formulae  
\[D_{eff}^{GM}= (2\mu_{0}^{S}+\lambda_{0}^{S})\frac{h^{2}}{4}+\sum\limits_{i=1}^{N }(2\mu_{i}^{S}+\lambda_{i}^{S})\frac{z^{2}}{4}\bigg{|}_{z=-h/2+\sum\limits_{k =1}^{i}h_{k}},\quad D_{eff}^{SO}=\sum\limits_{i=0}^{N}\left(2\eta_{i}+\chi_{ i}\right). \tag{26}\]  
As an example, let us consider a multilayered plate with layers of equal thickness and equal properties, i.e., \(h_{i}=t\equiv h/N\), \(\mu_{i}^{S}=\mu^{S}\), \(\lambda_{i}^{S}=\lambda^{S}\), \(\eta_{i}=\eta\), \(\chi_{i}=\chi\), \(i=0,1,\ldots,N\). In this case, \(D_{eff}^{0}=D\), whereas  
\[D_{eff}^{GM}=(2\mu^{S}+\lambda^{S})\frac{t^{2}}{4}\sum\limits_{i=0}^{N}(i-N/2 )^{2},\quad D_{eff}^{SO}=(N+1)\left(2\eta+\chi\right),\]  
and  
\[D_{eff}=\frac{Eh^{3}}{12(1-\
u^{2})}+\frac{1}{N}\left(\frac{1}{6}\,N^{2}+ \frac{1}{2}\,N+\frac{1}{3}\right)(2\mu^{S}+\lambda^{S})\frac{h^{2}}{8}+(N+1) \left(2\eta+\chi\right). \tag{27}\]  
where we use the formula  
\[\sum\limits_{i=0}^{N}(i-N/2)^{2}=\frac{1}{12}\,N^{3}+\frac{1}{4}\,N^{2}+\frac{ 1}{6}\,N.\]  
For \(N=1\) and \(N=3\), i.e., for homogeneous and three-layered plates, Equation (27) reduces to (20) and to (24), respectively. The characteristic dependencies of the dimensionless stiffness \(\overline{D}\) on \(N\) are given in Figure 3 for \(N=1,5,10,20\). The horizontal dashed lines have the same meaning as in Figure 2.
# Cavity Detection in Steel-Pipe Culverts Using Infrared Thermography +
## 5 Experimental Results
### Experiment 3: Specimen 2, Cavity Configuration 4  
The goal of this experiment was to verify the possibility of detecting air pockets behind the real specimen using PCT and ATC. In this test, we could not detect any clear indication of a cavity using either of these techniques; an example of the results is given in Figure 12b. As the figure shows, there is no clear difference between sound and defective areas because the recorded thermal images do not carry sufficient information to distinguish between areas with the cavity and the rest. After further investigation we noticed that three matters underlie this problem: low thermal emissivity, high thermal reflection, and complex surface. Specimen 2 is a cut from a corrugated pipe with a series of ridges and grooves. The presence of these folds causes nonuniform heating of the specimen surface. Specifically, the ridges receive the largest portion of heat whereas slopes receive the smallest. This causes a relatively regular nonuniformity in the thermograms recorded by the IR camera. Consequently, the expected variations in the thermal pattern of areas with a cavity, which are considerably smaller than these effects, are lost.  
To handle the first two issues with the real specimen (i.e., specimen 2), its internal layer (toward the camera) was coated by flat black paint and another experiment under exactly the same conditions was carried out. Figures 12c,d and 13 show the best results achieved for the LPT analysis, including PCT and ATC. Although the thermal contrast is not as significant as for experiment 1, it is still sufficient to notice the cavity as well as estimate its size, location, and even its shape.  
Figure 11: Temperature comparison; experiment 2. (**a**) Selected points. (**b**) Temperature-time graph for non-cavity regions vs. different cavities.  
Figure 12: LPT analysis; Experiment 3. (**a**) LPT with no specimen. (**b**) LPT with original specimen 2. (**c**) LPT with painted (flat) specimen 2. (**d**) PCT for painted (flat) specimen 2. The same cavity configuration was used for (**a–d**).
# Mechanics of Screw Joints Solved as Beams Placed in a Tangential Elastic Foundation
## 4 Experiment (Pull-Out Test for Screws) and Its Evaluation
1: Dependence of tensile force F on screw elongation \(u_{\text{B}}\) (the experiment and its approximation by regression function \(\text{F}=N_{\text{B}}=\frac{F_{\text{E}}}{2^{m}}\left[1-\,\cos\left(\frac{\pi \,u_{\text{B}}}{u_{\text{E}}}\right)\right]^{m}\) for interval \(u_{\text{B}}\in\left\langle 0;u_{\text{E}}\right\rangle\)). From the knowledge of mechanics, relationship (8) can be generalized. Then, an important finding applies for the general value of the normal force \(N\) that  
\[N=\frac{\text{F}_{\text{E}}}{2^{m}}\left[1-\cos\left(\frac{\pi\,u}{u_{\text{E}}} \right)\right]^{m} \tag{9}\]  
Equation (9) applies for the entire length of the screwed thread \(\text{L}_{2}=0.09\) m (see Figure 4), i.e., for the elongation \(u\in\langle u_{\text{B}};\,u_{\text{C}}\rangle\), where points B and C are the ends of the screwed portion of the screw section and their displacements are \(u_{\text{B}}\) and \(u_{\text{C}}\). The relatively \"simple\" dependence ((8) or (9)) is encumbered by a minor error towards the experiment, and therefore is accurate enough. For other needs, it is also appropriate to determine the derivative of the normal force  
\[\frac{dN}{du}=\frac{\pi\text{F}_{\text{E}}m}{u_{\text{E}}2^{m}}\text{sin} \bigg{(}\frac{\pi\,u}{u_{\text{E}}}\bigg{)}\bigg{[}1-\cos\bigg{(}\frac{\pi\,u}{ u_{\text{E}}}\bigg{)}\bigg{]}^{m-1}=\frac{\pi\,m\,\text{F}_{\text{E}}\,\text{sin} \bigg{(}\frac{\pi\,u}{u_{\text{E}}}\bigg{)}}{u_{\text{E}}\bigg{[}1-\cos\bigg{(} \frac{\pi\,u}{u_{\text{E}}}\bigg{)}\bigg{]}}. \tag{10}\]  
Relationship (6) then results in the relationship for the continuously distributed reaction force  
\[\text{q}_{N}=\frac{N}{EA}\frac{dN}{du}=\frac{\pi\,m\,\,N^{2}\sin\left(\frac{ \pi\,u}{u_{\text{E}}}\right)}{EAu_{\text{E}}\bigg{[}1-\cos\bigg{(}\frac{\pi\, u}{u_{\text{E}}}\bigg{)}\bigg{]}}=\frac{\pi\,\,m\,\,\text{F}_{\text{E}}^{2}\sin \left(\frac{\pi\,u}{u_{\text{E}}}\right)\bigg{[}1-\cos\left(\frac{\pi\,u}{u_{ \text{E}}}\right)\bigg{]}^{2m-1}}{4^{m}EAu_{\text{E}}}. \tag{11}\]  
The determined dependence \(\text{q}_{N}\) on the surface (in point B), i.e., the function \(\text{q}_{N}=f(u_{\text{B}})\), is shown in Figure 9. It is obvious that at the moment of the screw pulling, i.e., when \(u_{\text{B}}=u_{\text{E}}\), the \(\text{q}_{N}=0\), which is in line with reality (disintegration of screw joints, confirmation of the theory of mechanics). The position of the extreme value \(\text{q}_{N}\) is given by the zero value of the first derivative (when \(\frac{dq_{N}}{dx}=0\)), i.e., \(u_{\text{q}_{N}}=\frac{u_{\text{E}}}{\pi}\,\text{acos}\left(\frac{1-2m}{2m}\right)\) (see Figure 9).
## 4 Emerging Microwave NDT Techniques
### Microwave Microscopy  
Microwave microscopy and near-field MNDT are intricately linked by utilizing localized microwave fields in the near-field region to conduct material evaluations. Microwave microscopy is a specialized subset within the expensive realm of near-field MNDT techniques. It is distinguishable by its targeted emphasis on achieving high-resolution material characterization and imaging, extending to the microscopic scale. This precision enables a deeper and more detailed analysis of material properties and internal structures, making microwave microscopy a powerful tool for detecting minute defects and inhomogeneities within various materials. In microwave microscopy, probes are designed with exceptionally small tips, often integrated with a resonator, to attain super-resolution capabilities. This implementation allows for a thorough and detailed examination of surface properties across various materials, including metals, semiconductors, and dielectrics. Although the penetration depth of microwave microscopy is inherently limited, its precision in characterizing surface properties is unparalleled, contingent upon maintaining a consistent stand-off distance throughout the testing process [111].  
One of the various works on microwave microscopy is that of [PERSON] et al. [112], who developed an advanced high-temperature MNDT system that incorporates a quartz lamp radiation module for heating and an in-house microwave microscope probe for real-time, high-temperature inspections. This system, shown in Figure 11, demonstrated exceptional performance across multiple frequency bands, achieving super-resolution imaging. Notably, it resolved a metal grating with 0.5 mm features at 1 GHz, achieving a remarkable \(\lambda/600\) super-resolution [112]. The system's effectiveness was further shown through the high-temperature examination of external cracks on aluminum and GFRP samples, complemented by a post-processing algorithm that fixed non-uniform background lighting issues. The system also successfully visualized subsurface structures like honeycomb cores beneath a 1.0 mm panel, showcasing its ability for detailed subsurface analysis and accurate assessment of core wall positions and bonding thicknesses.  
Another study on microwave microscopy is that of [PERSON] et al. [113], who investigated the effect of various parameters on the performance of a custom-built near-field scanning microwave microscope. The device was designed using a vertical coaxial evanescent probe. A matching network, which utilized the interferometric method, was integrated to enhance measurement sensitivity and accuracy in the 2 to 20 GHz frequency range [113]. This work's key findings included identifying optimized key parameters for microwave microscopy, which significantly affect the measurement precision and stability. An example of such a parameter is the intermediate frequency bandwidth.
## 4 Results
However, we observe short-scale structures with positive slopes. Those appear as smaller, bright regions in the 2D histograms and may be relevant with the polytropic relationship that governs the plasma density and temperature fluctuations within plasma streamlines. We also observe that the histogram of \(\log(n)\) and \(\log(T)\) has many more similarities with the histogram of \(\log(n)\) and \(\log(T_{\perp})\) than the histogram of \(\log(n)\) and \(\log(T_{\parallel})\). Figure 3: (Left) \(\log(T)\) as a function of \(\log(T_{\perp})\); (middle) \(\log(T)\) as a function of \(\log\Bigl{(}T_{\parallel}\Bigr{)}\); and (right) \(\log(T_{\perp})\) as a function of \(\log\Bigl{(}T_{\parallel}\Bigr{)}\) for (top panels) slow solar wind with \(V<500\) kms\({}^{-1}\) and (bottom panels) for fast solar wind with \(V>500\) kms\({}^{-1}\). In each panel, we show the corresponding Pearson correlation coefficients between the plotted parameters, and the red dashed line shows the identity function. Following a similar technique as in [19; 20; 21; 22; 28; 29], we determine \(\gamma\) by fitting the polytropic model to \(\ln(T)\) vs. \(\ln(n)\) observations within short-scale subintervals with small (\(<\)0.1) relative deviation of the Bernoulli integral. We further determine \(\gamma_{\perp}\) and \(\gamma_{\parallel}\) from the slope of a linear fitting to \(\ln(T_{\perp})\) vs. \(\ln(n)\) and \(\ln(T_{\parallel})\) vs. \(\ln(n)\), respectively. The left panel of Figure 5 shows the histograms of \(\gamma\), \(\gamma_{\perp}\) and \(\gamma_{\parallel}\) as derived from the 1995 observations. The right panel shows the histograms of the differences \(\gamma_{\perp}-\gamma\) and \(\gamma_{\parallel}-\gamma\). Table 1 shows the mean and standard deviation values for each histogram. Our results suggest that the linear regression between either \(\log(T_{\perp})\) and \(\log(n)\), or \(\log\Bigl{(}T_{\parallel}\Bigr{)}\) and \(\log(n)\), in selected short intervals of solar wind protons at \(\sim\)1 au, with a quasi-stable Bernoulli integral, can estimate the average actual value of \(\gamma\). The derived \(\gamma_{\perp}\) is
## 4 Results and Discussion  
This section begins by showing the use of AVO and BLI inversions applied to the dataset (seismic data, well logs, etc.) obtained from the Norne field in the North Sea. Initially, we worked with data obtained in 1D and then expanded to a synthetic case of 2D dimensionality, to evaluate the method's effectiveness and reduce the uncertainty inherent in changing dimensionality from 1D to 2D. Finally, we demonstrate how porosity inversion and estimation can be applied to a real dataset.
## 3 Results and Discussion
### Journal Publication Contribution to ZEBs and Energy Efficiency towards Sustainability  
Academic publications are immensely significant in any subject of study, as various studies have demonstrated and made clear by their conclusions. The purpose of journal contributions is to elaborate on which journals have paid the most attention to sustainability. According to the findings, _Energy and Buildings_ had the most significant impact on energy efficiency, followed by _Building Research and Information_, and _Building and Environment_. This will assist practitioners, researchers, and students who wish to pursue more research on ZEBs and energy efficiency in the pursuit of sustainability, with a focus on _Energy and Buildings_ due to its significant contribution in that sector. Furthermore, the contribution of _Energy and Buildings_ reflects the energy efficiency of buildings throughout their life cycle, energy efficiency improvement techniques, and energy demand and consumption. However, aside from _Energy and Buildings_, which has the most contributions, all of the others (_Applied Energy, Energy Policy, Habitat International, Sustainability, Journal of Cleaner Production, Sustainable Cities and Society, Renewable Energy, Journal of Green Building, Construction and Building Materials, Environmental Science and Technology_) may cover a wider range of topics. Figure 3 elaborates on journal publication contributions to ZEBs and energy efficiency in the pursuit of sustainability.  
Figure 3: Journal publication contributions.
# All About Audio Equalization: Solutions and Frontiers
## 7 Audio Content Creation
### Adaptive and Intuitive Interfaces
The early work on intelligent equalization based on intuitive descriptors was hampered by a limited set of descriptors with a limited set of training data to map those descriptors to equalizer settings. [PERSON] and [PERSON] addressed this with SocialEQ, a web-based crowdsourcing application aimed at learning the vocabulary of audio equalization descriptors [237]. To date, 633 participants have participated in a total of 1102 training sessions (one session per learned word), of which 731 sessions were deemed reliable in the sense that users were self-consistent in their answers (Personal communication with [PERSON], 2015). This resulted in 324 distinct terms, and data on these terms is made available for download. Building on the mappings from descriptors to equalization curves, [PERSON] and [PERSON] (2013) described a simple equalizer where the entire set of curves were represented in a 2-D space (similar to (231)), thus assigning spatial locations to each descriptor. Equalization is performed by the user dragging a single dot around the interface, which simultaneously manipulates 40 bands of a graphic equalizer. This approach was extended to multitrack equalization in (238), which provided an interface that, by varying simple graphic equalizers applied to each track in a multitrack, allowed the user to intuitively explore a diverse set of mixes. The concepts of perceptual control, learned from crowdsourcing, intuitive interface design and mapping of a high dimensional parameter space to a lower dimensional representation were all employed in (239; 240). This approach scaled equalizer parameters to spectral features of the input signal, then mapped the equalizer's 13 controls to a 2-D space. The system was trained with a large set of parameter space data representing warmth and brightness, measured across a range of musical instrument samples, allowing users to perform equalization using a perceptually and semantically relevant, simple interface.
## 3 Optical Error Analysis
### Optical Errors of FPI
#### 3.1.1 Optical Path Difference Error  
The frequency-shifted magnitude \(m\Delta_{FPI}\) is fundamentally determined by the cavity spacing \(d\), incidence angle \(\theta\), and refractive index \(n\). Multiple factors influence \(d\), including assembly tolerances, reflective coating thickness variations, and temperature fluctuations; \(\theta\) is predominantly affected by the tilt angle of FPI components perpendicular to the optical axis; \(n\) exhibits susceptibility to environmental variations such as temperature and pressure changes. These imperfections collectively induce drift in \(m\Delta_{FPI}\). MJI-HI leverages the FPI to generate frequency-shifted components, establishing a linear relationship between interferograms and \(I_{0}\) to reconstruct high-frequency interference information. Within this framework, \(\Delta_{FPI}\) serves as a critical parameter in formulating the linear relationship defined in Equation (8). To enable \(\Delta_{FPI}\) computation and dynamic calibration, \(\Delta_{FPI}\) must be slightly smaller than \(L\), ensuring \(I_{2}(\Delta)\) encapsulates \(I_{2}(\Delta_{FPI})\). As the interferogram \(I_{0}\) corresponds to the Fourier transform of the spectrum, its energy is predominantly concentrated in the low-frequency region near \(\Delta=0\). Consequently, Equation (6) can be approximated as follows:  
\[\left\{\begin{array}{l}I_{2}[U(0)]\approx a_{0}I_{0}[U(0)]\\ I_{2}[U(\pm\Delta_{FPI})]\approx a_{1}I_{0}[U(0)]\end{array}\right., \tag{10}\]where \(U(\Delta)\) denotes the neighborhood of \(\Delta\). The value of \(\Delta_{FPI}\) can be determined by computing the cross-correlation \(I_{2}[U(0)]\otimes I_{2}[U(\pm\Delta_{FPI})]\) and identifying the position of maximum correlation, where \(\otimes\) represents the cross-correlation operator. This methodology enables the real-time correction of \(\Delta_{FPI}\) when cavity spacing variations occur in the FPI, thereby preserving the accuracy of the linear system formulation.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 7 Issues and Challenges of Node Clustering in CR-WSNs
### Multichannel Hidden Node Problem  
CR-WSNs have to cope with both the single-channel hidden node problem and the multichannel hidden node problem. To mitigate the traditional and well-known single-channel hidden node problem, request-to-send (RTS) and clear-to-send (CTS) handshakes are used. However, if nodes utilize multiple channels, a node may miss channel negotiation packets, because they are busy communicating on another channel, and the multichannel hidden node problem occurs. Details of the multichannel hidden node problem can be found elsewhere [59; 60].
## 4 Experiment and Results
### Networking Measures
#### 4.2.1 E2E Delay  
The time series is presented in Figure 12 of the E2E delay shows that the best performance was achieved by SC-MOPSO, which produced a delay with level 0.3; NSGA-II and NSGA-III generated a higher E2E delay. In addition, we observed that the behavior of SC-MOPSO produced a transient state that slowly increased until reaching a steady state case at 0.3, while NSGA-III produced a different pattern of the transient state, which started high and slowly decreased until reaching the steady state of 0.5 s.