esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Prospects  
The POCIS has been identified as the appropriate tool for investigative monitoring studies and for observing spatial and temporal distribution of polar organic compounds in water environments. However, the ongoing challenges for the POCIS should be focused on the following aspects before the POCIS becomes a reliable, robust, and cost-effective approach for many monitoring programs.  
The improvement of sampling procedures should be made in the immediate future. Firstly, the biofilm would be inevitably formed in actual environmental exposures of the sampler. The formation of biofilm under field conditions caused interfacial reactions among algae, metal oxides, dissolved substance, and the analytes on biofilm. Thus, the errors in calibration of the \(R_{s}\) under laboratory and field conditions would be produced due to the differences in the composition of biofilm and the physicochemical effects on the biofilm surface. To avoid biofilm formation, thereby reducing such errors in calibration of the \(R_{s}\), would improve the practical application of the device. Secondly, the membrane has been regarded as the channel for pollutant enrichment in sampler. The physicochemical properties of membrane materials have a significant impact on the \(R_{s}\). Therefore, the membrane materials should be selected and optimized to reduce the retention time of pollutants on the membrane and to enhance the adaptability of the membrane in the environment so as to improve the applicability of the device in the actual environment.  
The accuracy in quantitative analysis by the POCIS should be improved. Accurate \(R_{s}\) data are crucial factors for a quantitative determination of chemical by the POCIS. Calibrating the \(R_{s}\) by in silico approaches could provide a new way to improve the calibration accuracy of the \(R_{s}\) and reduce the uncertainty in the process of operation. Nevertheless, the reasonability of parameter selection for describing the model and the relationship between these parameters and the influence factors on the \(R_{s}\) should also be further explored.  
Writing--original draft preparation, L.W.; writing--review and editing, R.L., X.L., H.G. All authors have read and agreed to the published version of the manuscript. This work was supported by the Basic Scientific and Research Program for Central Nonprofit Research Institutes (no. 2017-YSKY-10) and the National Natural Science Foundation of China (no. 21277133). The authors declare no conflict of interest.
# Postbuckling and Free Vibration of Multilayer Imperfect Nanobeams under a Pre-Stress Load
## 2 Problem Formulation  
### Kinematic Relations  
We consider a geometrically imperfect multilayer beam of a nanoscale size whose ends are kept at a fixed distance in space. The beam has a thickness, \(H\); initial curvature of amplitude, \(a\); and a fixed length of, \(L\), as shown in Figure 1. The beam is assumed to deform in the \(x-z\) plane. The beam is assumed to be subjected to a pre-stress compressive load due to the manufacturing processes that typically induce such residual stresses upon curing due to the mismatch of thermal expansion coefficients, as shown by [PERSON] and [PERSON] [63]. It is worth noting that if the pre-stress load is beyond the buckling load, the beam will buckle and stretch because the ends are fixed in space. As a result, the effective compression at the end will reduce. The formula relating the pre-stress load and the possible buckling amplitude is different from the classical formulas assuming moving ends. This behavior will be outlined in more detail in the subsequent sections.  
Figure 1: A schematic of a curved, multilayer nanobeam with hinged boundaries.  
Based on the classical Euler-Bernoulli beam theory, the axial and transverse displacements, \(U\) and \(W\), of a point, \(p\), originally located at a distance, \(x\), from the origin at a height, \(z\), from the beam's midplane are defined as  
\[U(x,z,t)=u(x,t)-z\big{[}w^{\prime}(x)-w^{\prime}_{0}(x)\big{]} \tag{1a}\] \[W(x,z,t)=w(x,t) \tag{1b}\]  
where \(u\) and \(w\) are the axial and transverse displacements along the midplane of the beam, and \(w_{0}\) is the initial imperfection of the beam whose maximum amplitude is designated \(a\) in Figure 1. The beam is assumed to be thin in order for the Euler-Bernoulli (EB) beam theory to be justified. Nevertheless, for multilayer beams, the aspect ratio is not the only parameter that contributes to the validity of the EB theory, but it is also the moduli of elasticity in the axial and lateral directions [64]. Generally, an aspect ratio of 20 and higher can justify the application of the EB beam theory. According to [PERSON] et al. [65], the midplane stretching, which is depicted by the nonlinear von Karman strain, has a significant effect on the bending of nonlocal Euler-Bernoulli and Timoshenko nanobeams. So, the von Karman effect is considered in the current model and expressed as  
\[\varepsilon_{x}=U^{\prime}+\frac{1}{2}\big{(}{W^{\prime}}^{2}-{w^{\prime}_{0}} ^{2}\big{)}=u^{\prime}-z\big{(}w^{\prime\prime}-w^{\prime\prime}_{0}\big{)}+ \frac{1}{2}\Big{(}{w^{\prime}}^{2}-{w^{\prime}_{0}}^{2}\big{)}=\varepsilon_{0 }+zk_{0} \tag{2}\]  
where  
\[\varepsilon_{0}=u^{\prime}+\frac{1}{2}\Big{(}{w^{\prime}}^{2}-{w^{\prime}_{0}} ^{2}\big{)} \tag{3a}\] \[k_{0}=-\big{(}w^{\prime\prime}-w^{\prime\prime}_{0}\big{)} \tag{3b}\]  
are, respectively, the normal and bending strains.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 4 Conclusions  
In this research, a new robust control algorithm was proposed by altering the sliding surface of conventional SMC. The control performance of SMC, PDSMC, SMCSPO, and PDSMCSPO were compared through simulation and experimental results. The multiplication of small gain \(k_{d}\) with the velocity error term in the sliding surface reduced chattering from the system output as well as the sliding surface. During the sliding phase, this small gain was less than unity, which is multiplied with the perturbation and reduced the effect of perturbation on the system. As a result, a smaller value of switching gain was required compared to conventional SMC. The smaller the value of switching gains, the smaller the chattering. This small gain \(k_{d}\) had shrunk the pass band of the low pass filter between the sliding surfaces over perturbation. Because of that, during the sliding phase, the system remained insensitive to high-frequency perturbation.  
The integration of PDSMC in SPO generated a more robust control scheme that used a perturbation compensation technique. It was observed that the convergence of output to the desired states was faster in PDSMCSPO compared with conventional SMCSPO. In this new scheme, the effect of estimation error of state on the estimated sliding surface was reduced. During the sliding phase, the system remained insensitive from perturbation because the Bode plot between error and perturbation had more attenuation capability compared with conventional SMCSPO. The bode plot between the sliding surface and perturbation also has more attenuation capacity compared with previous techniques. This new robust control scheme shows outstanding performance which includes faster convergence to the desired state and less error in both output and sliding surface when compared with SMCSPO in both simulation and experimental results.  
Conceptualization, S.J.A. and M.C.L.; Formal analysis, S.J.A., K.D.K.; Funding acquisition, M.C.L.; Methodology, S.J.A.; Software S.J.A., K.D.K.; Supervision, M.C.L.; Writing-original draft, S.J.A., Writing-review & editing, S.J.A., K.D.K.  
This research was funded and conducted under the Competency Development Program for Industry Specialists of the Korean Ministry of Trade, Industry and Energy (MOTIE), operated by Korea Institute for Advancement of Technology (KIAT). (The development of high skilled and innovative manpower to lead the Innovation based on Robot).  
This research was supported by the MOTIE (Ministry of Trade, Industry & Energy), Korea, under the Industry Convergence Liaison Robotics Creative Graduates Education Program supervised by the KIAT (N0001126). This research was supported by the Technology Innovation Program(10073147, Development of Robot Manipulation Technology by Using Artificial Intelligence) funded by the Ministry of Trade, Industry, and Energy(MOTIE, Korea).  
The authors declare no conflict of interest.  
Figure 17: Error trajectories of both control schemes (lambda = 20).
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 3 Background
### The Acceleration Technique in the FFT Based on the Cooley-Tukey Algorithm  
Due to the high computational complexity of solving Formula (2) directly, in engineering the Fast Fourier Transform (FFT) method is generally used. The FFT is a general term for techniques used to calculate DFT efficiently and quickly on computers. The most classic method in the FFT is the Cooley-Tukey algorithm [30]. The Cooley-Tukey algorithm reduces the complexity to \(O\left(N\log_{2}N\right)\) by employing a divide-and-conquer strategy. The core idea of the Cooley-Tukey algorithm is to decompose an input vector of length \(N\) into two input vectors of length \(N/2\) and recursively compute their Fourier Transforms with a similar method. The transformed input vectors are then combined to obtain the Fourier Transform result of the original input vector of length n. This combination process is achieved using butterfly operations. By repeatedly applying this decomposition and combination process, the Cooley-Tukey algorithm efficiently calculates the Fourier Transform with less computation. The acceleration performance of the Cooley-Tukey algorithm has been validated through numerous instances on both CPUs and GPUs.  
However, implementing the Cooley-Tukey algorithm directly on NPUs poses a significant challenge due to the NPUs' inability to manipulate fine-grained data blocks, which is essential in the execution of that algorithm. This is because the Cooley-Tukey algorithm involves the butterfly operation. During the first several iteration cycles, the operation directly manipulates individual elements rather than data blocks consisting of at least 32 bytes, which is difficult for NPUs. In contrast, the major computing units of CPUs or GPUs can operate on a byte. Therefore, it is not suitable to utilize the NPU's vector units to accelerate the FFT based on the Cooley-Tukey algorithm.
## 3 Literature Survey and Critical Analysis
### Taxonomies and Literature Analysis
#### 3.2.1 Taxonomy 1: Problem Features
This objective can conflict with the energy objective, since a shorter production time may require higher production speeds or the use of more machines, thereby consuming more energy. This is the most addressed case, although it is possible to find a global optimum that guarantees the minimum makespan and the minimum energy, wherever waiting times between operations are the only aspect of the plant that allows energy to be minimized. However, this is not generally the norm, paving the way for the adoption of multi-objective optimization solvers. The next most important secondary objective is the total tardiness, which was accounted for in 14.76% of the works organized in the taxonomy. Total tardiness refers to the delay time and is critical for production, as it can incur serious production cost overruns due to late deliveries. This objective is related to the makespan, and if we consider both together, around 90% of the papers included production efficiency in one form or another as a secondary objective. The total workload appeared as the next-most-addressed secondary objective, appearing in 4.92% of the jobs. Finally, the remaining 22.96% included other key performance metrics not necessarily related to production efficiency, such as noise, worker costs, and product quality (among others), which have less impact on production when searching for an optimal scheduling plan. We end the analysis of our first proposed taxonomy by pausing on the type of JSP formulated in every work. In most contributions, the authors defined each use case by mapping it to a specific type of problem. Although all of them were related to JSP problems, in many cases, they showed problem types that were too specific to be generalized to other practical use cases. This implies that many reported cases described by the authors are only applicable under the conditions of the particular setup they considered in their work. This could be observed in 57.38% of the papers reviewed, in which the type of problem was unique for that study and defined under acronyms that do not provide any relevant information to the reader. The next largest group referred to the flexible JSP (FISP), which was described in 21.32% of the contributions. Similarly, 16.40% of the cases referred to the hybrid flow shop scheduling problem (HFSP), whereas 4.92% were about a naive JSP problem, without any further formulation specifics whatsoever.
# Time Domain Room Acoustic Solver with Fourth-Order Explicit FEM Using Modified Time Integration
## 4 Discretization Error Characteristics
### Numerical Dispersion Error Characteristics  
The numerical performance of explicit TD-FEM using modified Adams method is tested using a numerical simulation of circular wave propagation in a 2D free field. We calculated waveforms at two receiving points 2 m distant from the sound source in the axial and diagonal directions. The modulated Gaussian pulse [10; 19] of the upper-limit frequency of 2.5 kHz was used as a source signal. The upper-limit frequency is a frequency with -3 dB gain in the frequency spectrum. The waveform and its frequency characteristics of the sound source signal are presented in Figure 3. We used three FE meshes (Mesh-1, -2 and -3) discretized with square elements of different sizes. Table 1 presents the element sizes and the number of PPW for the three meshes. Mesh-1 has lower spatial resolution than the well known rule of thumb for linear elements: 10 PPW [34]. Mesh-2 and Mesh-3 are, respectively, two and four times finer meshes. The critical time intervals were used with each mesh. The computed waveforms were compared with the reference solution calculated using fourth-order accurate implicit TDFEM [19; 31] in both space and time with sufficiently fine mesh of Mesh-3. Figure 4 presents comparisons of the computed waveforms with the reference solution for the three meshes, where the upper and lower panels respectively show the results at the receiver in axial direction and in diagonal direction. The results demonstrate that the proposed method shows considerably good agreement with the reference solutions even when using the coarsest mesh. Slight wave fluctuation can be found in only the result of Mesh-1 at the axial direction, where the proposed method has the maximum error, as shown in Figure 2a. The theoretical results in Section 4.1 show that the results in Mesh-1 include approximately 0.5% of dispersion. Conventional TD-FEM using linear elements cannot achieve this level of accuracy with a mesh that does not fulfill the rule of thumb. The proposed method also uses low-order FEs, but it can produce much better results even in such a condition, as shown in this section.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mesh** & \(\mathbf{h,\,\mathrm{m}}\) & **PPW** \\ \hline Mesh-1 & 0.02 & 6.87 \\ Mesh-2 & 0.01 & 13.75 \\ Mesh-3 & 0.005 & 27.45 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of three FE meshes (Mesh-1, -2 and -3) for convergence test: \(h\) and PPW respectively denote the length of square FE and the number of points per wavelength.  
Figure 2: Dispersion characteristics of explicit time domain finite element method (TD-FEM) using modified Adams method: (**a**) anisotropic characteristics in the dispersion error in terms of sound propagation directions with two time interval values of \(\Delta t=\Delta t_{\text{limit}}\) and \(\Delta t=0.01\Delta t_{\text{limit}}\) and (**b**) convergence of dispersion error relative to spatial resolution points per wavelength (PPW).
## 2 System Structure  
The proposed system is presented in Figure 1. It comprises a Receiver-and-Transmitter (Rx/Tx), server and user terminal.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 6 Conclusions  
This study combined two kinds of satellite data: HJ-1A/B remote sensing images and MOD17A2H data products as input for an improved CASA model to estimate the net primary production (NPP) during the entire 2008-2009 winter wheat growing season. The NPP-yield conversion model was then used to estimate winter wheat yield on a regional scale. The results give a determination coefficient \(R^{2}\) = 0.56 between the measured and estimated winter wheat yield, RMSE of 1.22 t ha\({}^{-1}\), and an average relative error of \(-6.01\%\). Thus, the improved CASA model combined with the NPP-yield conversion model satisfied the requirements for regional-scale estimates of winter wheat yield from remote sensing images. This approach may thus be applied in the field and can also provide a benchmark for a wide range of models for estimating crop yield.  
[PERSON]; processed and analyzed the data and drafted the manuscript. [PERSON]; guided the ideal, designed the experiment, advised on data analysis and revised the manuscript. [PERSON]; [PERSON]; L.F.; [PERSON] and [PERSON] were involved in the experiments, ground data collection, and manuscript revision. All authors read and approved the final version.  
This work was supported by the National Natural Science Foundation of China (Grant No. 41571416) and the National Key Research and Development Program (Grant No. 2017 YFD0201501).  
We appreciate [PERSON] and [PERSON] for their help during field data collection. The funders had no role in choosing the study design or in the collection, analysis and interpretation of the data, in the writing of the report, or in the decision to submit the article for publication.  
The authors declare no conflict of interest.
# Cooperative Position Aware Mobility Pattern of AUVs for Avoiding Void Zones in Underwater WSNs
## 5 Conclusions and Future Work  
We have proposed two routing protocols, PAMP and Co PAMP, to avoid void zones and achieve high network throughput. Due to the dynamic mobility pattern of gliders, void zones are avoided and energy is wasted due to the high packet drop ratio being minimized. For minimizing the uncertainty, predictable trajectories are used, and gliders stayed at static positions for a short interval to collect the data. The transmission power is adjusted according to the n-confidence region (neighboring nodes) for balanced energy consumption in estimating the s-confidence regions. In the estimation process of n-confidence and s-confidence regions, the distance between the source and the destination is considered, which helped estimating the more precise position of the glider. The simulation results show that the recent proposed schemes (PAMP and Co PAMP) performed better than the baseline scheme (QUO VADIS) in terms of packet delivery ratio, energy consumption and void zone in the network.  
In the future, we have planned to analyze the performance of gliders at different depths of the aquatic environment and the effects of interference at different depths due to noises.
## 2 Materials and Methods
### Data-Driven SIF Prediction Model Using the Random Forest Algorithm
#### 2.3.1 Explanatory Variable Selection for SIF Prediction Model
Therefore, \(\Phi_{SIF}\) mainly depends on \(NPQ\) and \(q_{L}\). Numerous studies have shown that \(NPQ\) and \(q_{L}\) vary with external conditions and stresses, such as illumination, temperature, and water stress [32; 57; 58], and that illumination or PAR is the main driving factor for them [32]. In addition, in diurnal experiments carried out on sunny days and investigations into light response, \(NPQ\) and \(q_{L}\) have been shown to have a strong relationship with the illumination [32; 58; 59]. For satellite-based observations, only the SIF on sunny days is available, and so our aim was to produce a global SIF product for clear conditions. Therefore, the cosine of the solar zenith angle at noon (cos (SZA\({}_{0}\))) was used to represent the illumination conditions in this study and for simulating the fluorescence quantum yield (\(SIF_{yield}\)) at the canopy scale. Therefore, BRDF-corrected reflectances (red, NIR, blue and green bands), NDVI, cos (SZA\({}_{0}\)), and the air temperature were selected as explanatory variables for modelling satellite \(SIF_{normalized}\) signals. Compared to other studies [21; 22; 29; 30; 31], it was hoped that adding cos (SZA\({}_{0}\)) as a new explanatory variable might improve the modelling of the contribution of \(\Phi_{SIF}\) to SIF, given the light response of \(NPQ\) and \(q_{L}\) to cos (SZA\({}_{0}\)). The data-driven model used for reconstructing the normalized SIF was:  
\[SIF_{normalized}=f(Rs,NDVI,cos(SZA_{0}),T) \tag{6}\]  
where \(NDVI\) is the normalized difference vegetation index, \(T\) is the air temperature, and \(Rs\) represents the four BRDF-corrected reflectances at the red, NIR, blue and green bands.