esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Adaptive Motion Recognition Based on Wi-Fi Feature Enhancement
## 5 Experiments and Analysis
### Comparison with Other Methods  
In order to better confirm the improvement of the proposed algorithm, we compare the method in Section 3.1 with the other two positioning models for AoA estimation, and contrast the recognition of our algorithm with other methods based on CSI using public datasets.  
Two typical positioning models are used to estimate human body positions from CSI data collected at different locations. Figure 8 shows the cumulative error of AoA estimation for dynamic human bodies by different models. The median errors of SpotFi [43], Widar2.0 [38], and the model in this paper are 10.3\({}^{\circ}\), 5.1\({}^{\circ}\), and 3.7\({}^{\circ}\), respectively. Among them, SpotFi uses the MUSIC algorithm for positioning, and the accuracy of this method greatly reduces when the number of antennas is insufficient. Widar2.0 uses the traditional maximum likelihood and the extended SAGE algorithm. SAGE algorithm completes parameter decomposition by continuous transformation of index set and decomposes the maximization problem of AoA into the corresponding maximization problems of index set  
Figure 5: The correction of phase noise. (**a**) Original CSI phase. (**b**) Corrected CSI phase.  
Figure 6: Comparison diagram of action speed distribution effect.  
parameters. This method does not guarantee certain convergence to the optimal value and a local optimum usually results in not reaching the desired estimate.  
There are also many action recognition methods based on CSI; however, the corresponding of action sets are not exactly the same. For the sake of fairness, the CSI action data set in the publicly available WiAR [44] and the human activity recognition data set WiDAR [35] from the Tsinghua University team are selected. We selected CARM [19], Widar 3.0 system [35], EI [32], and our model by PRF values (precision, recall, F1-score) as indicators for comparison.  
As shown in Tables 1 and 2, CARM uses DFS as the learning feature and adopts the HMM model, so the performance is the worst when human motion data in different environments and different positions are added to the datasets. EI combines the adversarial network and specially trains the loss, and additionally utilizes the features of the unlabeled data in the target domain, and its recognition effect is better, but the adversarial network model is uncontrollable and the training is difficult. Widar 3.0 uses CNN to extract signal spatial features and GRU to extract temporal features. The time normalization of action features must be strictly performed and the training time is long, otherwise the recognition effect cannot reach expectations. Our model transforms the DFS into the three-dimensional velocity distribution of human actions, which improves the efficiency and accuracy of action recognition compared with other recognition models.  
Figure 8: AOA cumulative error of different models.  
Figure 7: Classification performance. (**a**) Model performance without Convolution module and Gaussian prior. (**b**) Model performance with Convolution module and Gaussian prior.
## 4 Parametric Analysis of Side Joints
### Cracking and Deflection under Load Case 2
\(\delta_{URSP}\) and \(\delta_{Stud}\) are the deflection of the composite frame beam when using URSP connectors and studs, respectively, which are obtained by deflection of the composite frame beam under a uniformly distributed load before cracking occurs. In order to study the effect of the length of URSP connectors on the cracking performance of the side beam under different conditions, seven additional critical parameters varying in a usual range in practice are considered in the elaborate finite element models depicted in Figure 4, as shown in Table 2.  
Similar trends to those shown in Figure 18 are observed after applying 0-0.5\(L\) URSP connectors when other variable parameters are adopted. Choosing the parameters slab thickness \(h_{\text{c}}\) and steel beam height \(h_{\text{s}}\), which greatly influence the performance of composite structures, as the main variables in this section, the variation of \(R_{F}\) and \(R_{I}\) of composite beams with different URSP connectors arrangement lengths \(L_{\text{U}S}\) is illustrated in Figure 19. \begin{table}
\begin{tabular}{c c} \hline \hline
**Design Parameter** & **Considered Values** \\ \hline URSP length \(L_{U}\) (\(L\)) & 0 *, 0.1, 0.2, 0.3, 0.4, 0.5 \\ Slab thickness \(h_{\text{c}}\) (mm) & 75, **100**, 125, 150 \\ Slab width \(b\) (mm) & 1000, **2000**, 3000 \\ Steel beam height \(h_{\text{s}}\) (mm) & 300, 350, **400**, 450 \\ Steel beam flange width \(b_{\text{f}}\) (mm) & 100, **200**, 300 \\ Steel beam flange thickness \(t_{\text{f}}\) (mm) & **12**, 16, 20 \\ Steel beam web thickness \(t_{\text{w}}\) (mm) & 6, 8, 10 \\ Column dimension \(c\) (mm) & 300, **400**, 500 \\ \hline \hline \end{tabular} Notes. * Bold font stands for the initial values. \(L_{U}\) is the length of regions where URSP connectors are placed along the beam, as illustrated in Figure 3.  
\end{table} 
Table 2: Design parameters for load case 2. It can be seen from Figure 19a,c that the amplification factor of the cracking load \(R_{F}\) increases with the increase of URSP length, and the effect of cracking delay is more obvious and effective with the increase of URSP length. As illustrated in Figure 19b,d, the ratio of the stiffness amplification coefficient \(R_{I}\) decreases with the increase of URSP length. In other words, the stiffness amplification coefficient decreases more with the increase of URSP length. Figure 19a indicates that the amplification factor of the cracking load \(R_{F}\) almost decreases with the increase of slab thickness \(h_{c}\), which implies that the cracking load can be effectively enhanced with a relatively lower slab thickness.
## 3 Results and Discussion
### Production of Oil Pollution Risk Maps Using GIS Interpolation Techniques  
As an advanced step in the spatial analysis of pollutant distribution, threshold exceedance maps are produced using point data obtained from soil sampling in the borehole  
\begin{table}
\begin{tabular}{c c c c c} \hline Sample point 7 (515 Bors) & & & & \\ \hline P1 & 204 & 1000 & 260 & 408 \\ \hline P2 & 68 & 1667 & 52,000 & 120 \\ \hline P3 & 48 & 164,000 & 38,400 & 4800 \\ \hline P4 & 240 & 120 & 96 & 160 \\ \hline P5 & 52 & 1240 & 56 & 40 \\ \hline Sample point 8 (805 Scarisoara Noua) & & & & \\ \hline P1 & 184 & 100 & 30.5 & 30.4 \\ \hline P2 & 124 & 41 & 38.3 & 34.4 \\ \hline P3 & 87 & 30.2 & 30 & <27 \\ \hline P4 & 97 & \textless{}27 & \textless{}27 & \textless{}27 \\ \hline P5 & 369 & 37.5 & \textless{}27 & \textless{}27 \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._and estimating the distribution of TPH concentrations at points in the vicinity of the sampling locations. This method has been used successfully for industrial sites in Italy. This geostatistical analysis using GIS techniques underpins environmental risk assessment and the implementation of best bioremediation techniques [33].  
The results thus obtained are dependent on the heterogeneity of soil characteristics. Thus, rock type, physicochemical characteristics of soils, and land use influence the dispersion of pollutants and their migration in vertical and horizontal planes (Supplementary Files S1).  
Kriging methods [34] are thus used as interpolation methods, but many soil profiles are required in this case. In the present case, this method was tested, but the validation rate using known TPH concentrations obtained a low validation rate, and overestimations for safe points of more than 200% were identified, which is why the IDW (Inverse Distance Weighting) interpolation method was used, which considers the distance between safe points and reduces uncertainties.  
To derive the probability of exceeding the threshold limits, the local spatial variability of pollutant concentrations is analysed [35]. In this case, the local spatial variability of TPH for the points sampled in the borehole. This way, probable values are estimated for points near the soil sampling locations needed to estimate the soil volume, requiring bioremediation techniques.
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 4 Generalized Design Approach
### Systems Engineering and Practical Implementation
#### 4.3.1 Toolchains and Design Procedures  
To enable the practical application of this design methodology, specific simulation and prototyping tools are aligned with each phase of the flow. System Modeling and Abstraction: MATLAB/Simulink and RF System Toolbox for top-level link budgets, modulation format simulations, and system trade-off analysis can be used. SysML-based platforms such as Cameo Systems Modeler or IBM Rhapsody can be used for documenting design intent, requirement hierarchies, and performance links.  
Analytical and Circuit-Level Design (Horizontal Flow): Keysight ADS and PathWave can be used for harmonic balance, waveform engineering, and load pull optimization [28; 33]. MATLAB Symbolic Toolbox and custom Python scripts can be used for analytical derivation of voltage and current waveforms (e.g., Class E/F, J/ B continuum) [22; 23].  
EM Simulation and Realization (Vertical Flow): CST Studio Suite and Ansys HFSS can be used for simulating matching networks, bond wires, and packaging effects in 3D [41]. Non-linear device simulation using Cadence SpectreRF or LTspice can be used for transient validation. Prototyping and Validation: Hardware-in-the-loop (HIL) testbeds using NI LabVIEW, PXI systems, or custom SDR platforms can be used to validate performance under real-time operating scenarios [57]. Load pull systems and vector network analyzers (VNAs) can be used for the empirical validation of impedance trajectories and performance envelopes.
## 3 Results
### Relationships under Different Sky Conditions
\begin{table}
\begin{tabular}{l l l c c c c c c c c c c c c c c} \hline  & & & **Sky Conditions** & \multicolumn{3}{c}{**All Sky**} & \multicolumn{3}{c}{**Cloudy**} & \multicolumn{3}{c}{**Parity Cloudy_D**} & \multicolumn{3}{c}{**Parity Cloudy_B**} & \multicolumn{3}{c}{**Clear**} \\ \hline
**Station** & **Period** & **Variables** & **a** & **b** & **R\({}^{2}\)** & **a** & **b** & **R\({}^{2}\)** & **a** & **b** & **R\({}^{2}\)** & **a** & **b** & **R\({}^{2}\)** & **a** & **b** & **R\({}^{2}\)** & **a** & **b** & **R\({}^{2}\)** \\ \hline Larmaca & 2009-2012 & CS vs. GI & 0.698 & 0.922 & 0.960 & -0.112 & 1.107 & 0.771 & -0.277 & 1.037 & 0.764 & 1.899 & 0.786 & 0.650 & 1.266 & 0.870 & 0.915 \\ Larmaca & 2009-2012 & CS vs. CS30\_Menn & 1.038 & 0.915 & 0.918 & 0.112 & 0.934 & 0.658 & 1.635 & 0.715 & 0.622 & 3.495 & 0.991 & 0.528 & 2.582 & 0.780 & 0.800 \\ Larmaca & 2009-2012 & CS vs. CS30\_Pent & 0.229 & 0.944 & 0.843 & 0.583 & 0.351 & 0.481 & 1.778 & 0.515 & 0.665 & 3.931 & 0.452 & 0.430 & 2.141 & 0.803 & 0.753 \\ \hline Palos & 2009-2012 & CS vs. GI & 0.911 & 0.952 & 0.938 & 0.247 & 0.997 & 0.583 & 1.001 & 0.959 & 0.632 & 1.948 & 0.847 & 0.611 & 1.376 & 0.905 & 0.807 \\ \hline Athalassa & 2012-2015 & CS vs. Pr & 0.810 & 0.954 & 0.955 & 0.159 & 1.168 & 0.861 & 0.465 & 1.045 & 0.819 & 1.058 & 0.951 & 0.803 & 1.459 & 0.888 & 0.877 \\ \hline \end{tabular}
\end{table} 
Table 7: Regression parameters for different pairs of sunshine recorders under four categories of sky conditions, at the three synoptic stations for different time periods. Response variable is the CS recorder. The average daily sum of actual SD under 'partly cloudy' sky conditions, are higher for CS than the automatic devices, as shown in Table 8. For example, in the case of Larnaca (2009-2012) the average of CS under 'partly cloudy' conditions with predominance of diffuse radiation is 4.81 h, while Gl is 4.33 h. Similarly, in the case of 'partly cloudy' conditions with predominance of direct radiation the average of CS is 7.70 h, while Gl is 7.38 h. However, under 'clear' or 'all-sky' conditions the automatic devices have slightly higher values. The mean difference varied from 0.1 to 0.3 h under 'cloudy' and 'clear' sky conditions. The highest RMSD was obtained under 'partly cloudy' conditions in most of the cases. Higher R\({}^{2}\) are obtained under 'clear' and 'all-sky' conditions. Comparing the pyranometric method (Gl) and manual (CS) pair at Larnaca during the period 2009-2012, it was found that the largest frequencies of negative differences were obtained under'mostly cloudy' sky conditions which were approximately about 60% (Table 9). However, under 'cloudy' and 'clear' sky conditions the frequencies of positive differences reached the value of 68% and 74%, respectively. In the case of Pafos (2009-2012), for the same pair (Gl-CS), the negative differences reached the value of 78% under 'partly cloudy' sky conditions, while under 'cloudy' and 'clear' sky conditions they are approximately 31% and 45%, respectively.
## 5 Fatigue Damage Estimation Based on a Small Number of Sensors
In each box-and-whisker plot, the central mark and the edges of the box represent the median, 25 th percentile, and 75 th percentile of the data; the upper and lower whiskers extend out to the minimum and the maximum values. A shorter bar indicates low variability or greater precision in the estimation of the variability factors; vertically, the closer the bar is to unity, the more accurate is the estimation. Preferred combinations that ensure precise and accurate estimation of the fatigue damage rate for all the four data sets are indicated by green arrows. When employing the WWA method, the use of eight sensors distributed over a greater portion of the riser, e.g., placing four sensors near one end and four sensors near the other end, such as in combinations nos. 25 or 33, generally results in more accurate and precise fatigue damage estimation than does the use of eight clustered sensors such as in combination nos. 1 to 16. Variability factors estimated by the hybrid method based on eight sensors (using four modes) in each of the thirty-four combinations are illustrated by box-and-whisker plots in Figure 12a,b, which summarize results for the two uniform current data sets and the two sheared current data sets,  
Figure 10: Thirty-four combinations of eight input strain sensors chosen for fatigue damage analysis studies using cross-validation against measurements. respectively. Direct comparison with the results based on the WWA method (see Figure 11) suggest that, among the thirty-four combinations, those that were identified as more precise and accurate based on results with the WWA method were also found to be so with the hybrid method. For all of the thirty-four combinations, the hybrid method was found to generally provide more accurate and precise estimations of the fatigue damage rates than the WWA method. Figure 11: Variability factors estimated by the WWA method from measurements based on thirty-four combinations involving eight strain sensors: (**a**) two uniform current data sets; and (**b**) two sheared current data sets. Figure 12: \(Cont\).
# Causality in Control Systems Based on Data-Driven Oscillation Identification
## 2 Proposed Methodology
### Statistical Analysis  
Actually one should remember that industrial datasets can include many observations and therefore the constraint of the oscillation detection algorithm (datasets cannot be too long) might limit the analysis. Splitting of the long datasets into smaller subsets is only one of the solutions to overcome a limitation of the MEEMD method described in Section 2.2. Statistical analysis suggests that it is natural to use an arithmetic mean. Analyzing datasets/signal values based on the arithmetic mean is not always producing accurate results. It is known that using the arithmetic mean, adequate (true for a given sample) information is obtained only when the sample comes from a population with a normal distribution or close to it. On the other hand, if the population distribution is not close to a normal distribution, the median is used to infer the sample mean value. If industrial data is considered as a time series, the question arises of whether the generated noise can be smoothed using multi-period smoothing (averaging filters). However, with their construction, some information is lost, as a result of which the extremes of the smoothed signal may appear in inappropriate/illogical places, contrary to real conditions. As a result, the information obtained would be highly incorrect or may even be distorted. Previous papers show that raw datasets collected from real-scale industrial processes do not have normal distributions. Considering the above, the use of a median filter is justified.  
Several consecutive samples are taken and their median is determined in the classical median filter. A modified sample is obtained as a result of this operation. Working with datasets of a given amount is still an insufficient solution for further analysis and inference. Therefore, such an \(n\)-element dataset is divided into \(i\)-th windows with a length corresponding to natural divisors of number \(n\) (in this case 10, 25, 50, and 100, respectively).  
In each window, we have \(k\) samples, which are arranged in a non-decreasing manner, and for each already ordered window we determine the median as follows [25]:  
\[Me=\begin{cases}x_{(k+1)/2}&\text{- when $k$ is odd,}\\ \frac{x_{(k/2)}+x_{(k/2)+1}}{2}&\text{- when $k$ is even,}\end{cases} \tag{5}\]  
where \(x_{k}\) is the value of the \(i\)-th dataset. The position of the median (i.e., the middle unit) in each of the windows is set at half the size of the window under consideration:  
\[N_{Me}=\frac{k}{2}. \tag{6}\]  
In this way, based on the medians from each of the windows, a shorter, filtered sample is obtained.
# Molecular and Hormonal Mechanisms Regulating Fleshy Fruit Ripening
## 6 The Identification and Function of Major Regulator TFs
### The Nor (NAC-NOR) Mutation  
The tomato _nor_ mutation, like _rin_, produces a severe ripening-impaired phenotype (Figure 5). DNA sequencing showed that the mutation was due to a 2 bp deletion in the third exon of _NAC-NOR_[8]. This caused a frameshift, generating a transcript which is translated to produce a truncated protein of 186 amino acids (NOR186), which retains the NAC domain but lacks the C-terminal 169 amino acids (Figure 5). NAC-NOR-CRISPR gene editing experiments to generate NAC-NOR-deficient tomato plants produced a less severe phenotype [14; 19], however, as also found for MADS-RIN (Figure 5). [PERSON] et al. [19] suggested that the truncated NAC-NOR protein could cause a dominant-negative mutation, where the mutant TF could bind the DNA binding sites in target gene promoters and possibly also interact with other proteins but would be unable to transcriptionally activate its target genes. [PERSON] et al. [14] showed that NOR186 is located in the nucleus and binds to, but does not activate, the promoters of _ACS2_, _GGPPS2_ and _PL_, which are involved in ethylene biosynthesis, carotenoid accumulation and fruit cell wall softening, respectively. Furthermore, the activation of the promoters by WT NOR protein could be inhibited by the mutant NOR186 protein. In NAC-NOR-deficient fruit, ethylene synthesis, carotenoid accumulation and fruit softening were significantly inhibited compared with the WT, but much less severely affected than in the _nor_ mutant.
# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 1 Introduction
[12] used synchrotron-source X-rays to analyze bubbles in non-yeast dough. [PERSON] et al. [13] used X-ray tomography CT in both horizontal and vertical directions to study wheat, peas, and other grains and found differences in the size and number of air channels between the two directions. Although traditional image processing methods and model reconstruction can be used to observe changes in the structural characteristics of wheat grains, the processing process is cumbersome, and the defects of wheat cannot be quickly identified after CT scanning. Deep learning techniques are widely used for detecting pests and diseases in agriculture and fisheries. [PERSON] et al. [14] used the improved ResNet34 model to identify and classify images of rice pests. [PERSON] et al. [15] used the ResNet50 model to identify sick redfin oriental triggerfish through a transfer learning approach, effectively addressing the challenges of limited samples and low detection accuracy. [PERSON] et al. [16] used deep learning visual recognition technology to identify ripe citrus for yield estimation and long-term defect detection. [PERSON] et al. [17] employed deep learning to develop a Hypernet PRMF model for detecting moldy peanuts, achieving a detection accuracy of 90.35%. [PERSON] et al. [18] improved the detection of maize weeils by utilizing X-ray imaging technology in conjunction with deep learning algorithms. [PERSON] et al. [19] proposed a deep learning-based method for the rapid detection and identification of pests in mung beans. [PERSON] et al. [20] introduced a method for constructing a deep learning partitioned image dataset for pest detection in strawberries, resulting in an improved accuracy of 91.93% in detecting pests in strawberries. This paper presents a study on the internal structural changes of moldy maize kernels and methods for detecting mold using X-ray tomography. Two-dimensional cross-sectional images of kernels were obtained non-invasively. Then, 3D models of the corn kernel, embryo, endosperm, seed coat, pores, and cracks were reconstructed. Furthermore, a ResNet50 model was developed to detect corn mold by integrating it with deep learning algorithms. Qualitative and quantitative analyses were conducted to examine the structural change characteristics of moldy maize kernels. This study used the ResNet50 model to detect maize mold and classify the severity of the mold.
## 6 Conclusions  
We studied the modulation index, phase imbalance, and dispersion to observe the effects of these parameters on the performance of an RoF system through the DSB-OCS technique. The system successfully generated an optical mm-wave by using DE-MZM. Several LO frequencies have been applied to generate a wireless carrier for the data transmission through the RoF systems at different fiber lengths. The performance of the system has been tested with various modulation indexes where the maximum mm-wave signal power amplitude can be achieved with a modulation index between 0.6 and 1.1. The phase imbalance and insertion loss have been theoretically analyzed. The performance evaluations of the system show that the output mm-wave signal power increased when the modulation index of the MZM was increased. The study also shows that phase imbalance and optical splitting ratio affect the power of the generated optical mm-wave. Thus, the generated mm-wave signal power and the phase imbalance are dependent on the insertion losses and the modulation indexes of the DE-MZM. It was also found that dispersion of the optical fiber influences the performance of the system through reducing the mm-wave amplitude and SNR with the increase in fiber length. The system presented has great potential in future optical and wireless access networks.  
[PERSON] and [PERSON] studied and simulated all the data; [PERSON] consulted on the modulation index section, and review and editing; [PERSON], [PERSON] and [PERSON] consulted on the dispersion section and reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This work supported by the Fundamental Research Grant (FRGS), Ministry of Higher Education, Malaysia, No. FRGS/1/2019/TK04/UPM/02/07 and Inisiatif Putra Muda Grant by Universiti Putra Malaysia, No. GP-IPM/2017/9524100.  
The authors declare no conflict of interest.