esa-sceva/satcom-chunk-collection huggingface dataset:
investigated an emerging structured light reflectance imaging technique to extract early decay defect characteristics of navel oranges [23]. [PERSON] et al. proposed an X-ray based machine vision classification method for detecting avocado stem end rot defects [24]. [PERSON] et al. utilized a mask region-based convolutional neural network method to detect bruise scar features in strawberry images imaged under incandescent and ultraviolet irradiation [25]. [PERSON] et al. proposed an Attention Feature Fusion Network based on the U-Net architecture with the addition of a mixing loss and residual refinement module (RRM) to extract the cracking features of jujuibe [26]. [PERSON] et al. proposed a diameter correction method to reduce the effect of fruit size on the projected spectra and then used a one-dimensional convolutional neural network to detect citrus frostbite features [27]. [PERSON] et al. described hyperspectral imagingfor defective feature detection and extraction in other berries, such as citrus, apple, and peach, in the early stages of fungal infection [28]. [PERSON] et al. presented the advantages and limitations of NDT techniques, including bio-scattering, X-ray imaging, hyperspectral imaging, and thermal imaging for defect feature extraction [29]. Special imaging modalities can be beneficial in highlighting defect features. However, the main weakness of the method is that hardware imaging systems are not easily optimized once they are in place, and imaging conditions are costly to maintain. Therefore, a method for highlighting defect features under simple imaging conditions is needed to enhance the applicability of the inspection system. Based on this, this paper proposed a defect detection method that relies on image segmentation and stem/calyx identification. The proposed method should not only solve the defect detection problem under uneven light reflectivity but also eliminate the interference of the peduncle calyx on the final results. The main contributions of this paper are as follows: (1) An apple image surface defected segmentation method based on improved SSR image enhancement and region-growing algorithm was proposed. The method could better cope with the uneven light reflectivity due to the spherical surface of apples and improve the segmentation accuracy. (2) A stem/calyx recognition method based on improved SSR image enhancement and gray level co-occurrence matrix was proposed. The method highlighted the stem/calyx texture through improved SSR image enhancement, then collected the texture featured through a gray level co-occurrence matrix, and later classified the obtained feature data through a support vector machine to achieve the purpose of stem/calyx recognition. Experimentally, it was proved that improved SSR image enhancement could amplify the texture difference between the stem/calyx and the scar and improve the classification accuracy. The rest of the paper is organized as follows. The second part describes the materials used in this paper and the methods proposed in this paper. Experimental results with specific analysis are given in the third part. Finally, conclusions are drawn in the fourth part.
# Assessment and Spatial Modelling of Agrochernozem Properties for Reclamation Measurements
## 2 Materials and Methods
### Validation and Statistical Analyses  
A leave-one-out cross-validation (LOOCV) approach was applied to evaluate the prediction performance of RF models. LOOCV cross-validation is appropriate for a small dataset and consists of using all training data leaving one out. The accuracy of the predictive model was estimated using a mean error (ME) and a root-mean-square error (RMSE) (Equations (1) and (2)):  
\[ME=\frac{\sum_{i=0}^{n}(O_{i}-P_{i})^{2}}{n} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environmental Covariates** & **Acronym** & **Spatial Resolution (m)** & **Definition** & **Central Wavelength** \\ \hline \multirow{8}{*}{Sentinel-2A bands and indices} & B2 & 10 & Blue & 492.4 \\  & B3 & 10 & Green & 559.8 \\ \cline{1-1}  & B4 & 10 & Red & 664.6 \\ \cline{1-1}  & B5 & 20 & Red edge 1 & 704.1 \\ \cline{1-1}  & B6 & 20 & Red edge 2 & 740.5 \\ \cline{1-1}  & B7 & 20 & Red edge 3 & 782.8 \\ \cline{1-1}  & B8 & 10 & NIR 1 & 832.8 \\ \cline{1-1}  & B8a & 20 & NIR 2 & 864.7 \\ \cline{1-1}  & B11 & 20 & SWIR 1 & 1613.7 \\ \cline{1-1}  & B12 & 20 & SWIR 2 & 2202.4 \\ \cline{1-1}  & NDVI & 10 & Normalized Difference & - \\ \cline{1-1} \cline{2-5}  & El & 30 & Elevation (m) & - \\ \cline{1-1}  & Aspect & 30 & Aspect (\%) & - \\ \cline{1-1}  & Slope & 30 & Slope angle (\%) & - \\ \cline{1-1} Topographic attributes (SRTM) & MrRTF & 30 & Multiresolution of & - \\ \cline{1-1}  & & ridge top flatness index & - \\ \cline{1-1}  & MrVBF & 30 & Multiresolution Valley & - \\ \cline{1-1}  & & Bottom Flatness index & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Environmental covariates used for digital mapping.  
\[RMSE=\sqrt{\frac{\sum_{i=0}^{n}(O_{i}-P_{i})^{2}}{n}} \tag{2}\]  
where \(O_{i}\) and \(P_{i}\) are observed and predicted values of soil properties, respectively, and \(n\) is the number of samples.  
Statistical data processing, digital mapping and model accuracy assessment were performed in R and RStudio.
## 6 Design Considerations for LPWAN Networks
### Reduced Device Hardware Complexity  
Reduction in hardware complexity has multiple advantages, including reduction of cost, device size reduction, and reduced power consumption. One key area of effecting hardware simplicity is the application of software reconfigurability to radio transceivers [6]. Radios need to be able to operate over multiple-frequency bands and support varying types of waveforms, as well as various air interface technologies depending upon LPWAN-related and other interfaces. They need to be reconfigured based on software updates without hardware changes. Moreover, SDR is considered one of the key technologies that enable the use of optimized spectrum access. The adoption of integrated circuit (IC) technologies and low-complexity transceiver structures, such as the direct-conversion radio (DCR) architecture [10], result in manufacturing efficiency. DCR requires neither external intermediate frequency filters nor the image rejection filter. Use of such simplification runs the risks of radio frequency imperfections, for example, in-phase and quadrature imbalance, phase noise, and amplifier nonlinearities. Several digital processing techniques that may cause energy consumption need to be applied to minimize the negative effects. These approaches usually require high complexity processing, and hence demand more energy. Therefore, there are tradeoffs in implementing the simplification techniques.
## 4 Results and Discussion
### K-Fold Cross Validation Tests  
In order to evaluate the performance of ML models on multiple, random pairs of training-testing datasets, the k-fold cross validation test is used. In a k-fold cross validation test, one fold of data is used for testing of ML models that are trained on (k \(-\) 1) folds of data, and the process is repeated k times using every single fold as the test dataset once. In this study, five-fold cross validation tests with three repetitions (with different randomization of the data in each repetition) are carried out. This repeated cross validation yields 15 different sets of results for AAR and the corresponding MAPE and MAE values. Two types of repeated five-fold cross validations are carried out: (i) considering only the training dataset for hyperparameter turning of each ML model and (ii) considering the entire dataset for final evaluation and comparison of all the ML models developed in this study (in terms of accuracy of predictions and variance in prediction errors).
# Water Basin Effect of Cofferdam Foundation Pit
## 4 Verification and Application  
The theoretical framework developed in the preceding section is validated and applied to the steel pipe pile cofferdam foundation pit engineering of the 6# pier of the Jingwei River-Rail Shared Bridge of Xi'an Metro Line 10 (China), as shown in Figure 5a. The primary sub-riverbed soil layer at this site is medium sand, characterized by specific physical-mechanical properties and permeability, as shown in Figure 5b. As illustrated in Figure 5c, the 6# pier employs a \(\Phi\) 820 \(\times\) 16 mm interlocking steel pipe pile cofferdam with a 33 m pile length, 1 m spacing, and CT-type pile interlocks. The internal supports consist mainly of HN 900 \(\times\) 300 and HM 550 \(\times\) 300 H-shaped steel sections and \(\Phi\) 630 \(\times\) 10 steel pipes.  
Construction process of the cofferdam foundation pit:  
1. Underwater base cleaning to elevation 354.80 m.
2. Install first and second steel pipe pile rings underwater, using the first ring as a guide for driving interlocking steel pipe piles.
3. Excavate the pit interior from elevation 354.80 m to 340.60 m and construct underwater bottom sealing concrete.
4. Drain water to 1 m below the first ring (360.82 m) and install the first-level internal support at 361.82 m.
5. Drain to 1 m below the second ring (354.82 m) and install the second-level internal support at 355.82 m.
6. Drain to 1 m below the third ring (349.82 m), install the third ring and corresponding internal support at 350.82 m.
7. Drain remaining water to 344.90 m, construct the cap, then remove the third ring and support, followed by main pier construction.
8. After pier completion, refill the cofferdam to 1 m below the second ring and remove the second ring and support. Continue refilling to the river surface level and dismantle the first ring and support.  
Field measurements for the cofferdam foundation pit engineering project primarily involve monitoring the horizontal displacement of the steel pipe pile top using a total station with an accuracy of 0.5\({}^{\prime\prime}\) and 0.8 mm + 1 ppm, the vertical displacement via a leveling instrument with a precision of 0.3 mm/km, the deep horizontal displacement through an inclinometer with a sensitivity of \(\pm\)0.01 mm per 500 mm measurement interval, and the pit edge surface subsidence by the same leveling instrument, complemented by internal support axial force and groundwater level monitoring. Displacement observation points are strategically positioned at the midpoints of the cofferdam's four sides, while vertical observation points for deep horizontal deformation analysis of steel pipe piles are spaced  
Figure 5: (**a**) Outdoor scene, (**b**) stratum distribution, (**c**) structural design, and (**d**) numerical model of 6\(\#\) pier steel pipe pile cofferdam.  
at 1.0 m intervals, with all measurement procedures and methodologies strictly adhering to the protocols specified in relevant industry standards and technical specifications.
## 3 Dataset
### Noise-Network-Distorted Dataset  
In VoIP applications, speech distortion combines captured environmental noise, encoding-decoding, and transmission network distortions. The dataset for the combined effect of environmental noise, encoding-decoding, and transmission network distortions used to study the performance of automatic speech recognition systems on integrated noise-network-distorted speech [30] was used in this study. The dataset was generated by artificially corrupting the clean speech dataset with different environment noise at different signal-to-noise ratios and then transmitting the noise-mixed speech signal through the emulated network. As a result, the distorted noise-network dataset contains information on environmental noise conditions such as signal-to-noise ratios and noise types, and network condition parameters such as packet loss, delay, and jitter, as the data were generated in a controlled environment.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 2 The Dual-Platform GMTI Method against Nonuniform Environment
### Analysis of the Influence of Heterogeneous Environment in STAP
#### 2.2.2 Hot Clutter  
Due to the design of the radar system and for environmental reasons, many factors may lead to fluctuations between small pulses of clutter echo in practice. The dynamics of scanning antennas and any pulse-to-pulse instability in radar system components will produce fluctuations. The natural variations in clutter reflectivity may occur in land clutter, or in ocean clutter due to the action of wind or the movement of sea waves. Any wave source will cause the Doppler spectrum of a single clutter echo to widen. The fluctuation between pulses caused by any of these sources is called intrinsic clutter motion (ICM). The existence of ICM requires a wider clutter gap, or more adaptive degrees of freedom, to effectively be eliminated [34].  
The space-time covariance matrix of the \(k\)th clutter patch for a single clutter patch including ICM denoted by \(\mathbf{R}_{k}\), \(k=1,2,\cdots,N_{c}\) is then given by  
\[\mathbf{R}_{k}=\xi_{k}(\mathbf{\Gamma}_{k}*\mathbf{b}_{k}\mathbf{b}_{k}^{H})\otimes\mathbf{a}_{k} \mathbf{a}_{k}^{H}, \tag{9}\]  
where \(\xi_{k}\) is the clutter CNR and \(\mathbf{\Gamma}_{k}\) is the covariance matrix of the fluctions for the \(k\)th patch, defined by  
\[\mathbf{\Gamma}_{k}=E\{\mathbf{a}_{k}\mathbf{a}_{k}^{H}\}=Toeplitz(\mathbf{\gamma}_{c}(0); \cdots;\mathbf{\gamma}_{c}(K-1). \tag{10}\]  
Superposition is invoked to yield the result when many independent clutter sources are present:  
\[\mathbf{R}_{c}=\sum_{k=1}^{N_{c}}\xi_{k}(\mathbf{\Gamma}_{k}*\mathbf{b}_{k}\mathbf{b}_{k}^{H}) \otimes\mathbf{a}_{k}\mathbf{a}_{k}^{H}. \tag{11}\]  
Adding 37 micromoving targets with a random velocity between 0 and 1 m/s (the motion direction is also random) will increase the heterogeneity of clutter, which was used to simulate the hot clutter. Figure 2 shows the clutter ridge without hot clutter and the clutter ridge with disturbance, respectively. It can be clearly seen that the hot clutter widens the clutter ridge. Generally, moving targets exist in the cold clutter area, so if the training sample contains hot clutter, this will affect the estimation of CCM, thus affecting the detection performance.  
Figure 2: Clutter ridge (**a**) the clutter ridge without hot clutter (**b**) the clutter ridge with hot clutter.
## 3 Results and Discussion
It can be hypothesised that the artist made the use of lakes (with powdered glass anew) or of ivory/bone black, though its presence is difficult to confirm with XRF alone, as will be further discussed. Cu-based pigments and vermili were also employed in light blue/grey and pink background architectures, respectively (see Figure S3). Flesh tones were painted using lead white and vermili with details and outlines obtained with Fe-based pigments. Cu-based compounds were employed, in small amounts, in the shadows and eyes (Figures 5, 6, S2 and S6; the latter at higher spatial resolution where the use of vermiliion was highlighted in tiny detail such as the red lacrimal caruncle of the eye). Hair was obtained with a complex mixture of vermiliion, Fe- and Cu- based compounds, as well as lead-tin yellow (Figures 6, S2 and S5), where different hair tones were obtained with various mixtures of these pigments. In grey hair, such as that of [PERSON] Arimathea, mainly Cu-based pigments and lead white were employed, together with small quantities of lead-tin yellow (Figure 6). The blood drops on [PERSON]'s forehead were painted with vermiliion (Figure 6). Golden lines representing the halos or rays around the character's heads were realised with gold (Figures 6 and S2). Any distribution of other elements leads to the thought of the presence of a preparation layer, though the golden lines are so thin that associations with their distribution could not be properly evaluated. Upon visual inspection, gildings were likely applied using the mission technique. The rocks and cave of the burial were painted in grey/dark tones. Cu is characteristic of these areas (Figure 6) together with Zn traces, as previously discussed for the blue areas. Darker tones of the cave entrance were painted with the evident addition of Fe-based pigments (Figures 5 and 6). The black hat of [PERSON] was painted with a similar mixture, with the addition of a significant amount of Ca (Figure 5), which was also detected in the darker zones of the cave (Figure 5). P could not be detected in these areas; thus, it seems unlikely that [PERSON] used bone/ivory black, although its presence cannot be conclusively excluded. P XRF detection efficiency is indeed rather low, especially on varnished paintings. In addition, there was a partial overlap of the escape peak of the Ca K\(\alpha\) X-ray line with that of P, further encumbering its detection. It is also possible that the artist added some lakes, with Ca-rich glass, to warm this black hue. Figure 5: **(a)** Detail of Nicodemus, with relative Cu(K\(\alpha\)), Sn(L\(\alpha\)), Ca (K\(\alpha\)), Fe(K\(\alpha\)), and Hg(L\(\alpha\)) XRF maps; **(b)** detail of the upper left landscape, with relative Cu(K\(\alpha\)) and Sn(L\(\alpha\)) XRF maps; **(c)** spectra of the green and yellowish landscape, relative to the areas indicated in detail **(b)**. The two ROI are roughly of the same area in pixels.
## 4 Results Analysis and Discussions
### Stress Sensitivity of Permeability  
To analyse the sensitivity of permeability of broken coal samples to the stress change under cyclic loading and unloading conditions, the stress sensitivity coefficient (\(C_{K}\)) of the permeability was defined, which is calculated as follows:  
\[C_{K}=\left|\frac{\partial K}{\partial\sigma_{1}}\right|. \tag{5}\]  
By substituting the fitting formula shown in Table 2 into Equation (5), the relationship between the stress sensitivity of the permeability of broken coal samples and effective stress under cyclic loading and unloading conditions can be calculated, as shown in Figure 9.  
Figure 8: Relationship curve between \(D_{1}\), \(D_{2}\) and \(D_{3}\) values of coal samples and cyclic loading and unloading times.  
Figure 9: Stress sensitivity coefficients of permeability of broken coal samples under cyclic loading and unloading conditions.  
As shown in Figure 9, the stress sensitivity of permeability of broken coal samples gradually decreased with increasing effective stress during cyclic loading and unloading. Both in loading and unloading stages, the stress sensitivities of permeability of broken coal samples all gradually decreased with increased cyclic loading and unloading. During the same loading and unloading cycle, the stress sensitivity of permeability of coal samples in the loading stage was far higher than that in the unloading stage.
# Channel Estimation Based on IOTA Filter in OFDM/OQPSK and OFDM/OQAM Systems
## 3 OFDM/OQPSK System Model
### Design of the IOTA Filter

Table 2: Important parameters in the IOTA filter.
## 1 Introduction  
The estimation of solar radiation on the surface of the Earth on either horizontal or tilted planes has long been recognised an important parameter in a variety of fields such as atmospheric environment, e.g., [1], terrestrial ecosystems, e.g., [2], terrestrial climate, e.g., [3], and solar energy applications, e.g., [4]. Because of the scarcity of solar radiation platforms that may provide measurements of the total (or global) solar radiation to the above applications, solar models started appearing in the international literature as early as in 1950's. The total solar radiation is the sum of two components: the diffuse solar radiation and the direct (or beam) one. The latter can accurately be estimated via explicit atmospheric transmission relationships. It is, therefore, the diffuse component that must be simulated satisfactorily; however, this solar component is very difficult to accurately be estimated due to numerous scattering processes of the solar rays that take place in the atmosphere under a clear sky and moreover in the presence of clouds on a cloudy day. This fact has triggered a lot of research for the development of various models that pursue to estimate as accurately as possible the diffuse solar component. Such models are numerous in the international literature, e.g., [5, 6, 7]. Most of them use the diffuse fraction (\(\mathrm{k_{d}}\)), which is the ratio of diffuse-to-global solar radiation as function of other parameter (or parameters) that can easily be calculated, be measured or are known; such parameters are the clearness index (\(\mathrm{k_{t}}\)), which is the ratio of the global-to-the-extraterrestrial solar radiation, or the geographical latitude (\(\mathrm{\varphi}\)), e.g., [8]. Even though \(\mathrm{k_{t}}\) or \(\mathrm{k_{d}}\) can be computed in one way or another, it is very important for solar energy engineers to know their upper and lower limits that divide the sky conditions into clear, intermediate and overcast. Several such limits have been provided by many researchers. All of them are based on solar measurements or solar modelling, e.g., [9, 10]. This is, therefore, the reason that these limits are considered empirical as they are not based on a standard methodology.  
The above gap is bridged in the present study. In other words, this work develops a mathematical methodology for determining the upper and lower limits of \(\rm{k_{d}}\). The methodology proves to be universal, but the values of the upper and lower \(\rm{k_{d}}\) limits are site-dependent. Such a work is presented for the first time in the international literature.