esa-sceva/satcom-chunk-collection huggingface dataset:
# Methods for Improving Image Quality for Contour and Textures Analysis Using New Wavelet Methods
## 2 Materials and Methods
### Reconstruction of Contours from Images Using Nonlinear Cellular Network (NCN) Algorithm
Thus, the minimization of the energy \(\mathrm{E}\), implies the minimization of the two terms: the term that imposes a continuity in the plane of the image I and the term that favors or penalizes the discontinuities. The latter term is implemented, in most cases, by means of a binary field of lines G, and \(\lambda\) is a scalar parameter. Thus, depending on the chosen parameters, the energy function in Equation (32) formalizes a compromise between the uniformity of the image (favoring the elimination of noise from image) and an improvement and restoration of the contours (accentuate edges). To be able to implement the procedure on a single-layer cellular neural network structure, in a first approximation, in relation (32) it is considered that the function G ensures the preservation of the similarity (fidelity) of the processed image with the initial image. In the initial design conditions, by minimizing the mediation term from energy \(E\), it results in the template A, which includes only the operator A:  
\[\text{A}\ =\ \left( \begin{array}{ccc}0&0.25&0\\ 0.25&0&0.25\\ 0&0.25&0\end{array} \right) \tag{32}\]  
In principle, the cost function corresponding to the penalty (accentuation) term of the edges can have a similar shape as the mediation term, obviously with a changed sign. Under the same design conditions, minimizing this component results in template B which includes only non-zero operator B (A = 0 and z = 0):  
\[\text{B}\ =\ \left( \begin{array}{ccc}0&-1&0\\ -1&4&-1\\ 0&-1&0\end{array} \right) \tag{33}\]  
Consequently, for the improvement of the algorithm with nonlinear cellular network of images based on variational calculation (relations (26), (27), (32)), the final AB template of the form results:  
\[\text{A}\ =\ \left( \begin{array}{ccc}0&0.25&0\\ 0.25&0&0.25\\ 0&0.25&0\end{array} \right)\text{B}\ =\ \left( \begin{array}{ccc}0&-\lambda&0\\ -\lambda&4\lambda&-\lambda\\ 0&-\lambda&0\end{array} \right)\text{z}\ =\ 0 \tag{34}\]  
where \(\lambda\) is a scalar parameter that determines the ratio between uniformity and improvement contrast and contours, respectively. Uniformity is dominant for its subunit values \(\lambda\), while for supraunitary values the improvement of the contrast predominates.
## 3 Results
### Performances of DNNs for Corn and Soybean Yield Prediction
The interquartile range (IQR) and overall range of R\({}^{2}\) of 'EVI + Climate' and 'SIF + EVI + Climate' models were noticeably smaller than those of the 'Climate' models for both corn and soybean (Table S4), indicating that the overall performance of 'EVI + Climate' and 'SIF + EVI + Climate' models varied at a lower degree among different experiments than the 'Climate' model. This result demonstrated that the addition of EVI and SIF data into the climate-based models enhanced the stability of DNN performances and the reliability of yield estimates under different conditions. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**N**} & \multicolumn{4}{c}{**Corn**} & \multicolumn{4}{c}{**Soybean**} \\ \cline{3-8}  & & **R\({}^{2}\)** & **MAE** & **MAPE (\%)** & **R\({}^{2}\)** & **MAE** & **MAPE (\%)** \\ \hline SIF & 5 & 0.43 & 16.50 & 8.99 & 0.53 & 4.73 & 8.90 \\ EVI & 5 & 0.74 & 10.26 & 5.59 & 0.70 & 3.51 & 6.79 \\ SIF + EVI & 10 & 0.76 & 9.62 & 5.24 & 0.79 & 3.05 & 5.89 \\ Climate & 15 & 0.76 & 9.76 & 5.34 & 0.82 & 2.87 & 5.55 \\ SIF + Climate & 20 & 0.80 & 8.98 & 4.90 & 0.83 & 2.68 & 5.18 \\ EVI + Climate & 20 & 0.86 & 7.27 & 3.97 & 0.87 & 2.36 & 4.56 \\ SIF + EVI + Climate & 25 & 0.85 & 7.49 & 4.08 & 0.87 & 2.30 & 4.45 \\ \hline \hline \end{tabular}  
* The R\({}^{2}\) values are the adjusted R\({}^{2}\). The unit of MAE is bushels per acre. N is the number of predictors in each model.  
\end{table} 
Table 1: Averaged performances of DNNs models for predicting county-level corn and soybean yields in the Midwestern 12 states. 1  
Figure 4: The box plot of adjusted R\({}^{2}\) of 100 experiments for different DNNs. The boxes show the interquartile range (IQR), from 25 th percentile to 75 th percentile, of the R\({}^{2}\) distribution, while the whiskers extend the low and high quartiles by 1.5 times IQR values. The black points outside the boxplots are the outlier experiments that were outside the range of plot whiskers.
## 6 Results
### Average Energy Consumption  
Average energy consumption of the network for Scenario #1 and Scenario #2 of all three variants of EOICHD, LEACH, and LEACH-C is shown in Figures 9 and 10, respectively. The figures show the energy consumption pattern of the network over the simulation period. As per the simulation setting, each node is equipped with 2 Joules of energy. As the simulation progresses, the energy of nodes depletes due to sensing, data transmission, communication with other nodes, etc. The plots shown in Figures 9 and 10, show the cumulative energy of the nodes that is getting utilized as the simulation progresses. From Figures 9 and 10, it is observed that \(EOICHD\)\(3\times 3\) is capable of extending the network lifetime up to 1128 s and 1124 s for Scenario #1 and Scenario #2, respectively. It clearly shows that the energy consumption pattern of \(EOICHD\)\(3\times 3\) is more efficient than other variants.
# Multi-Term Attention Networks for Skeleton-Based Action Recognition
## 1 Introduction
The Multi-Term Temporal Sliding LSTM (MT-TS-LSTM) is introduced in MTA-RNN for extracting features with different time scales. The Attention Recalibration Module (ARM) is employed to find keyframes. Then, the output of the three MTA-RNN will be treated as a 3-channel image and fed to the ST-CNN for extracting the spatio-temporal features. Figure 1: The structure of Multi-Term Attention Networks (MTANs), includes the Multi-Term Attention Recurrent Neural Network (MTA-RNN) and Spatial-Temporal Convolutional Neural Network (ST-CNN). The MT-TS-LSTM includes long-term, medium-term, and short-term TS-LSTM networks, which helps to obtain the temporal dependencies from different types of continuous actions. After weighted fusion of temporal features based on Analytic Hierarchy Process (AHP) [10], the fusion features are recalibrated through the attention mechanism. When facing the situation that the time taken by different persons for completing the same action is different, the MT-TS-LSTM can extract richer temporal features than straightforward RNNs. Besides, the adding of the attention mechanism also makes the obtained temporal features robust. The main contributions of this work are summarized as follows:  
* We propose a general Multi-Term Attention Networks (MTANs) for skeleton-based action recognition. It introduces a method for fusing multi-term temporal features to solve the action recognitions problem of actions with large time-scale differences. * We propose a novel strategy for applying attention recalibration method to fusion features. The extracted temporal features with different time scales are fused according to the corresponding weights and recalibrated by attention mechanism. Networks with our strategy are able to reinforce temporal features for classifying actions.
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DF & decode-and-forward \\ SWIPT & simultaneous wireless information and power transfer \\ SPNC & selective physical-layer network coding \\ SND & single node detection \\ PS & power splitting \\ PNC & physical-layer network coding \\ ILS & infrequent light shadowing \\ AS & average shadowing \\ FHS & frequent heavy shadowing \\ LMS & land mobile satellite \\ HSTN & hybrid satellite-terrestrial network \\ PDF & probability density function \\ CDF & cumulative distribution function \\ SNR & signal-to-noise ratio \\ ANC & analog network coding \\ XOR & exclusive OR \\ TS & time switching \\ EE & energy efficiency \\ LOS & line of sight \\ RV & random variable \\ CSI & channel state information \\ ML & maximum-likelihood \\ \end{tabular}
## 3 Results and Discussion
### Self-Heating by Joule's Effect
In addition, it has been proven that the temperature homogeneity in the printed circuits does not vary (see the IR images taken at \(15\) and \(120\) min), proving their excellent performance when heating for long periods of time. In summary, 3D printing of nanodoped circuits has emerged as an easy procedure to convert any structure in an efficient surface heater with low power consumption. Figure 9 shows the deicing test carried out for the four-layer circuit. Here, it can be observed that the heating circuit (right), which was connected to the power source at \(200\) V, was able to remove an approximately \(2.5\) mm thick ice layer in \(4\) min and \(4\) s, whereas the ice layer on the reference circuit (left) remained almost unaltered. The deicing time was set as the time when the ice layer slides off the circuit (see IR images), correlated to the increase in the temperature of the circuit's area above \(0\)\({}^{\circ}\)C.  
Figure 8: (**a**) Cyclic self-heating test and (**b**) continuous self-heating test carried out for \(2\) h.
## 2 Materials and Methods
### Methods
#### 2.2.2 Analytical Techniques  
The mortar workability was studied using a mini-slump test [26]. It was carried out using a metal mold in the form of a truncated steel cone with a height of 60 mm, a base diameter of 100 mm, and a top diameter of 70 mm. The mold was placed on a smooth, level surface, then filled with mortar in 3 layers. Each layer was tamped with stokes throughout its depth. The steel mold was removed from the mortar immediately by raising it slowly and carefully in a vertical direction. This allows the mortar to spread over the level surface. Then, the slump and the spread diameter were measured.  
The flexural and compressive strength were measured with a 3R Quantech device at 28 days using 3 samples (4 \(\times\) 4 \(\times\) 16 cm\({}^{3}\)) for each mix-design mortar. The tests were performed in accordance with standard NF EN 196-1 [25], with loading speeds of 50 and 2400 N.s\({}^{-1}\) for flexural and compressive strength tests, respectively. The resulting flexural and compressive strength values were the means of three and six individual values, respectively.  
The microstructure of mortars was observed using a Gemini 300 (ZEISS) Scanning Electron Microscope (SEM) under high vacuum, with a working distance of 9 mm and a low voltage (2 kV) to avoid sample coating.  
Mercury intrusion porosimetry (MIP) has been widely used to investigate the pore structure of cement-based materials. The pore distribution of composite samples (prism of 1 \(\times\) 1 \(\times\) 1 cm\({}^{3}\)) was tested with the AutoPore IV 9500 device. Before testing, the samples were dried to remove air and water at 45 \({}^{\circ}\)C in a vacuum oven until a constant weight. The mercury pressure increasing allowed progressive access to the low porosity. The measured pore diameters were in the range of 0.003 \(\upmu\)m to 358 \(\upmu\)m.  
The thermal conductivity and the thermal diffusivity have been identified with the use of a Hot-Disk device at 20 \({}^{\circ}\)C. The thermal properties was measured by the Transient Plane Source (TPS) methodology using prism samples (4 \(\times\) 4 \(\times\) 16 cm\({}^{3}\)). The Hot-Disk probe was placed between two prepared surfaces of the prism samples. Finally, the specific heat was calculated from the measured thermal conductivity, thermal diffusivity and density. Before testing, the 28-day aged samples were oven-dried at 45 \({}^{\circ}\)C to remove the free water. Then, each sample was placed in a sealed bag and the cooling period was started at 20 \({}^{\circ}\)C. The free water has a significant influence on the measurements as water has its own thermal properties.
## 5 Conclusions  
The Granger Causality (GC) method enabled us to investigate vegetation anomalies caused by environmental drivers on a global scale. This study was dedicated to untangling the causality from precipitation, soil moisture, solar radiation, and temperature on four principal vegetation products, i.e., the FAPAR, the LAI, SIF, and the NDVI. Biome-specificanalyses were conducted to evaluate how different ecosystems react to abiotic external factors. The main findings are summarized as follows:  
* Water availability (i.e., SM and P) is a strong driver in arid areas, especially for the LAI, which is highly sensitive (0.43 for SM\(\rightarrow\)LAI and 0.41 P\(\rightarrow\)LAI cover a fraction of G-Caused pixel arid biomes).
* SM also causes the LAI on cold and polar biomes with fractions of 0.44 and 0.5, respectively.
* Ecosystems at higher latitudes with cold and polar biomes are driven mainly by R, although R is set to cause the melting of snow, driving soil moisture dynamics. Both on cold and polar biomes, G-Caused areas cover more than 40% of the biomes' areas.
* T causality is evenly distributed amongst all biomes and VPs, with cover fractions of \(\sim\)0.1-0.2.  
The novelty of the presented analysis is that the role of environmental drivers was investigated in the responses of four fundamentally distinct VPs. The introduced GC method allows for a spatially explicit way to quantify different ecosystem's sensitivity, by only using two years of data. Extending the current investigation with additional multi-decadal temporal data, ELVs or ENSO/NAO teleconnections could reveal further causal effects on vegetation. Furthermore, the upcoming data streams from the FLEX mission are anticipated to mitigate the TROPOSIF-induced uncertainties and to provide a more precise representation of the causal links between variables.  
D.D.--Conceptualization, Methodology, Software, Writing--original draft. P.R.-M.--Software--original draft. E.A.--Conceptualization--review and editing. K.B.--Formal analysis, Writing--original draft. J.V.--Formal analysis, Writing--original draft. All authors have read and agreed to the published version of the manuscript.  
The research was funded by the European Research Council (ERC) under the FLEXINEL project: grant number 101086622. D.D.K., P.R.M, K.B., and J.V. were funded by the European Union (ERC, FLEXINEL, 101086622). The views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.  
The code for the processing of the global GC analysis can be accessed via [[https://github.com/daviddkovacs/Granger_Causality_global](https://github.com/daviddkovacs/Granger_Causality_global)]([https://github.com/daviddkovacs/Granger_Causality_global](https://github.com/daviddkovacs/Granger_Causality_global)) (accessed on 28 August 2023).  
The authors declare no conflict of interest.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 2 Materials and Methods
### Data Processing  
The data collected through field operation need to be processed in order to be applied in regression analysis. The slip was calculated using the GPS-based actual speed and proximity sensor-based theoretical speed, as shown in Equation (1) [15]. The target to predict in this study was the output axle of the gearbox. Since it is difficult to directly measure the torque of the gearbox output axle, it was estimated using measured each wheel axle torque in this study. Thus, it is necessary to calculate the torque of the output axle of the gearbox using the data measured on the four axles. The torque of the target axle was calculated by considering the four wheel torques measured in this study, the gear ratio, and the efficiency between the wheel and gearbox output axle, as shown in Equation (2). The number of gear pairs, gear ratio, and efficiency between each wheel of the front and rear wheels and gearbox are shown in Table 3.  
\[s=\left(\frac{V_{0}-V_{a}}{V_{0}}\right)\times 100(\%) \tag{1}\]  
where \(s\) is the slip ratio (%), \(V_{0}\) is the theoretical speed (km/h), and \(V_{a}\) is the travel speed (km/h).  
\[T_{a}=\left(T_{FL}+T_{FR}\right)/\left(G_{f}\times\eta_{f}\right)+\left(T_{RL}+ T_{RR}\right)/\left(G_{r}\times\eta_{r}\right) \tag{2}\]  
where \(T_{a}\) is the output axle torque of gearbox (Nm); \(T_{FL}\), \(T_{FR}\), \(T_{RL}\), and \(T_{RR}\) are the front left, front right, rear left, and rear right wheel torque (Nm), respectively; \(G_{f}\) is the gear ratio between the front wheel and gearbox output axle; \(G_{r}\) is the gear ratio between the rear wheel and gearbox output axle; \(\eta_{f}\) is the efficiency between the front wheel and gearbox output axle (%); and \(\eta_{r}\) is the efficiency between the rear wheel and gearbox output axle (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Width \(\times\) Length \(\times\) Height** & **Weight** & **Applicable Power Range** & **Farrow** \\ \hline W/SP-8 & \(280\times 215\times 125\) cm & 790 kg & 60–90 kW & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the moldboard plow used in this study.  
Figure 3: Tractor with a moldboard plow used in the field experiment during tillage operation.
# Electro-Optical Imaging Technology Based on Microlens Array and Fiber Interferometer
## 2 Structure Design  
The structure of the electro-optical imaging system, which consists of a microlens array, fiber interferometer, and a processing module, is shown in Figure 1. The light information is collected and coupled into the fiber interferometer by the microlens array. Then, the interference fringes are detected and analyzed by the processing module. Finally, a computer generates an image based on the amplitude and phase information extracted from the interference fringes. Each fiber interferometer includes a pair of microlenses for measuring the spectrum of one baseline.  
The spectral distribution measurement was determined by the arrangement and pairing of the microlenses. As shown in Figure 1a, the rectangular microlens array uses a centrally symmetric baseline pairing method (e.g., pentagram and pentagram, diamond and diamond, etc.). We also proposed a complex rectangular sub-aperture arrangement method to optimize the microlens-array arrangement (Figure 1b). The complex rectangular sub-aperture arrangement achieved different sampling intervals at high, intermediate, and low frequencies.  
The performance of the fiber interferometer is closely related to the quality of interference fringes, whose core device is a 3-dB fiber coupler. Because achieving a fiber coupler coupling efficiency of 100% is difficult, the split ratio cannot be 1:1 [13]. Interference arm length differences of the fiber optic interferometer will also introduce additional phase differences, which will eventually affect the true reflection of the stripe contrast and phase.
# An Early Warning System to Detect At-Risk Students in Online Higher Education
## 3 Methods and Context
### Next Activity At-Risk Simulation  
The GAR model only provides information about whether the student has the chance to fail the course based on the last graded assessment activities. This model can be used to give information to the student about the likelihood to fail, but it is not very useful when the teacher wants to provide early and personalized feedback concerning the next assessment activities. We define the Next Activity At-risk (NAAR) simulation as the simulation to determine the minimum grade that the student has to obtain in the next assessment activity to have a chance to pass the course.  
This prediction is performed by using the submodel of the assessment activity we want to predict. The NAAR simulation uses the grades of the previous activities already graded and simulates all possible grades for the activity we want to predict for identifying when the prediction changes from failing to pass.  
**Example 2**.: _Let us take the submodel \(\mathit{Pr}_{AA1}(\text{Fail?})\) of Example 1. In order to know the minimum grade, six simulations are performed based on the possible grades the student can obtain in AA1. Each simulation will produce an output based on the chances to fail the course. An output example is shown next based on the first assessment activity of the Computer Fundamentals course that will be analyzed in Section 6._  
\[\begin{array}{l}\text{Pr}_{AA1}(\text{Fail?})=(\text{N})\quad\rightarrow\quad \text{Fail?}=\text{Yes}\\ \text{Pr}_{AA1}(\text{Fail?})=(\text{D})\quad\rightarrow\quad\text{Fail?}= \text{Yes}\\ \text{Pr}_{AA1}(\text{Fail?})=(\text{C-})\quad\rightarrow\quad\text{Fail?}= \text{Yes}\\ \text{Pr}_{AA1}(\text{Fail?})=(\text{C+})\quad\rightarrow\quad\text{Fail?}= \text{Yes}\\ \text{Pr}_{AA1}(\text{Fail?})=(\text{B})\quad\rightarrow\quad\text{Fail?}= \text{No}\\ \text{Pr}_{AA1}(\text{Fail?})=(\text{A})\quad\rightarrow\quad\text{Fail?}= \text{No}\end{array}\]  
_where we can observe that students have a high probability of passing the course when they get a B or an A grade on the first assessment activity. This means that most of the students pass when they obtain these grades._However, it is possible to pass the course with a lower mark, but less frequently. Note that, this is for the first assessment activity where there are no previous activities. On further activities, the grades of previous activities are taken into account and the prediction is better personalized for each student based on his or her previous grades.
## Materials and Methods
### Measurement of Spectral Radiant Intensity under Solar Light  
An HR2000+ spectrometer (Ocean Optics, Dunedin, FL, USA) with spectral resolution of 0.065 nm at full-width at half-maximum (FWHM) was used for spectral radiant intensity measurement. The fibre optic (fibre-HR2000+) diffuser of the HR2000+ spectrometer was set at a 5 mm distance from the leaf surface, and it was held at 45\({}^{\circ}\) to the fibre-PAM to reduce specular reflection from the leaf surface. The focal point of the fibre-HR2000+ was set at a distance of 2 mm from that of the fibre-PAM to avoid the influence of saturation pulse light and the shadow of the fibre-PAM. The integration time of the HR2000+ spectrometer was set at 0.2 s; more technical information is described in [PERSON] et al. (2015) [65]. After the leaf spectral radiant measurement, a 90% White Card (Kodak, USA) set at the same angle and position as the leaf was used as the non-fluorescent reference standard. Both the spectra of the leaf and the non-fluorescent reference were recorded and transferred to a computer via a USB port using the OPwave+ operating software. A red laser (KaLaser, 660 nm, 200-250 mW) was used as the saturation pulse light, illuminating for 0.8 s at 60\({}^{\circ}\) on the leaf from a distance of 40 cm and was set to have about a 3 cm diameter footprint.