esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Aggregate Size on Recycled Aggregate Concrete under Equivalent Mortar Volume Mix Design
## 2 Materials and Methods
### Particle Size Determination  
For the determination of particle size distribution, the representative samples of NA and RCA were selected and sieved as per KS F2502 [41].  
In order to determine the particle size distribution of OA in RCA, the residual mortar in RCA was removed using a hydrochloric acid solution. The RCA used for particle size distribution analysis was immersed in a hydrochloric acid solution with a concentration of 1 M (mol/L) for 24 h to dissolve residual mortar from OA [27; 42]. The acid treatment is less time-consuming than the method for calculating residual mortar content proposed by [PERSON] et al. [38] and has the advantage of handling large amounts of RCA in one treatment. Removal of residual mortar by the hydrochloric acid solution can dissolve calcareous aggregates, thus, special attention is required in the selection of the method [39]. Since the OA of the RCA used in this study was granite, which is generally considered to have strong acid resistance, the hydrochloric acid dissolution method was applied. The obtained OA was washed with tap water and then sieved to determine the particle size distribution curve. The particle size distributions of RCA and OA are shown in Figure 2. The RCA-AP denotes the apparent particle size distribution of RCA itself. The distribution curve for OA is denoted as RCA-OA. As expected, the size of the OA was smaller than that of the corresponding RCA, and the particle size reduction was greater in the RCA1 with the residual mortar content of 28.6% than in the RCA2 with the residual mortar content of 12%. The maximum nominal aggregate size of both RCA1 and RCA2 was 25 mm, but that of the original aggregates (RCA1-OA and RCA2-OA) was 19 mm.  
Figure 2: Results of the sieve analysis of recycled aggregates and original aggregates.  
In order to investigate the effect of the OA size, the particle size distribution of all aggregates to be used for making concrete specimens should be the same. Therefore, the particle size distribution of NA and RCA was matched to the particle size distribution of OA. For example, the RCA1-AP was obtained by manually matching the RCA1 to the RCA1-OA particle size distribution shown in Figure 2. When RAC is produced tailored to the RCA1-AP curve using RCA1 in the size range of 4.75-25 mm, the RAC actually has the particle size distribution of RCA1-OA shown in Figure 2. In this way, RCAs were prepared for the experiment and used for RAC mixing; the following is an explanation of the types and their designations:  
* RAC-AP: Concrete in which the RCA with a particle size range of 4.75-25 mm was manually fitted to the same particle size curve as OA (4.75-19 mm) (i.e., 19 mm is its apparent size).
* RAC-OA: Concrete in which the RCA with a particle size range of 4.75-25 mm was used without any modification (i.e., the aggregate contains OA of 4.75-19 mm inside).
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
Plate Boundary Observatory (PBO) network site P041 and P037 were used to validate the effectiveness of the proposed model for soil moisture retrieval, and the accuracy of the proposed model was verified by comparing and analyzing the Support Vector Machines (SVM), Radial Basis Function (RBF) Neural Networks, and Convolutional Neural Network (CNN) models.
## 2 Image Analysis Method
### Improvement of Image Analysis Method
#### 2.2.3 Result of the Improved Aggregate Segmentation  
The improved image analysis method was tested on ballast aggregates collected from Gyeongbu high-speed railways in South Korea. Table 2 presents the results of aggregate segmentation and Figure 5 shows the segmented aggregates using image analysis when the previous method and the improved method are applied. As shown in the Table 2, the previous method exhibited varying percentages of aggregate segmentation depending on the image shooting condition. The maximum percentage of aggregate segmentation was 100% for a favorable image shooting condition, but the percentage dropped to 51% for a poor image shooting condition. However, the improved method exhibited 100% aggregate segmentation for all images regardless of the image shooting condition.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sampling Point**} & \multirow{2}{*}{
\begin{tabular}{c} **No.** \\ **of Total Aggregates** \\ \end{tabular} } & \multicolumn{2}{c}{**No. of Segmented Aggregates**} \\ \cline{2-4}  & & **Previous Method** & **Improved Method** \\ \hline Location 1 & 118 & 90 (76\%) & 118 (100\%) \\ \hline Location 2 & 17 & 13 (76\%) & 17 (100\%) \\ \hline Location 3 & 70 & 36 (51\%) & 70 (100\%) \\ \hline Location 4 & 35 & 32 (91\%) & 35 (100\%) \\ \hline Location 5 & 40 & 34 (85\%) & 40 (100\%) \\ \hline Location 6 & 49 & 41 (84\%) & 49 (100\%) \\ \hline Location 7 & 42 & 37 (88\%) & 42 (100\%) \\ \hline Location 8 & 57 & 55 (96\%) & 57 (100\%) \\ \hline Location 9 & 46 & 46 (100\%) & 46 (100\%) \\ \hline Location 10 & 87 & 87 (100\%) & 87 (100\%) \\ \hline \end{tabular}
\end{table}
Table 2: Result of aggregate segmentation.  
Figure 4: Changed aggregate segmentation procedure: (**a**) original image; (**b**) HSV image; (**c**) S-channel of HSV image; (**d**) binary image; (**e**) boundaries.  
Figure 3: Aggregate segmentation procedure used in previous research [11]: (**a**) original image; (**b**) fray-scaled; (**c**) binary image; (**d**) boundaries.
## 3 Applications
### Auditory Guidance Tools for Motor Learning
By transforming kinematic variables into sounds, proprioceptive signals are translated into exteroceptive signals, thus revealing hidden characteristics of the handwriting movement to the writer. Although this study focused on adult participants, the encouraging results incited the authors to propose a similar approach for learning and rehabilitation of dysgraphic children which also appeared to be efficient [34]. In this study the difficulty of the task could be adjusted according to the degree of fluidity for each child. Results revealed that dysgraphic children increase their writing fluidity across training sessions byusing this sonification tool. Current experiments are conducted to evaluate whether this sonification method is more efficient than existing ones based on visual and proprioceptive feedbacks.
### SEVIRI Errors of Omission and Commission
#### 5.1.3 The SEVIRI and MODIS Cloud Masks  
The cloud mask used during the processing of the FRP-PIXEL product has a substantial impact on the performance of the SEVIRI FTA detection algorithm, confounding no less than 30% of SEVIRI's omission rate in any ROI. Given the weight of this finding, we compared the SEVIRI and MODIS cloud masks used during the generation of both active fire products. The MODIS cloud cover fraction, \(f_{MODIS}\), within an ROI at any instant in time_t_ was calculated as follows:  
\[f_{MODIS}(t)=\frac{N_{cloud}(t)}{N_{total}(t)} \tag{2}\]  
where \(N_{cloud}(t)\) is the number of MODIS cloud pixels observed in the ROI at time \(t\), and \(N_{total}(t)\) is the total number of MODIS pixels covering the ROI at time \(t\). Note that \(N_{total}(t)\) for MODIS varies with \(t\) depending on the MODIS viewing geometry. For comparison, an analogous version of Equation (2) was also used to calculate the SEVIRI cloud cover fraction, \(f_{SEVIRI}\), within \(\pm\)8 min of time \(t\). Note that due to SEVIRI's geostationary projection, the number of SEVIRI thermal pixels in each ROI does not change over time.  
Figure 6a,b illustrate that \(f_{SEVIRI}\) is greater than \(f_{MODIS}\) for 99% of the near-simultaneous observations. In one poignant example, the cloud mask used during the generation of the SEVIRI FRP-PIXEL product classified ROI 1 as 91% cloudy at the same time the cloud mask used during the generation of the MODIS fire product classified ROI 1 as 1% cloudy (Figure 7). This discrepancy is attributed to a high, thin layer of cirrus that was classified as clouds by SEVIRI in the FRP-PIXEL quality file, but was ignored by MODIS in the MYD14 fire mask. Neither is necessarily incorrect, butrather the definition of what constitutes a \"cloudy pixel\" differs between the detection algorithms. We re-visit this topic in more detail in Section 6.
## 3 Results
### Binary Tree Cover  
#### 3.2.1 Visual Assessment  
The binary tree cover map achieved an overall accuracy (calculated as the total proportion of correctly classified pixels as a percentage of all pixels [67]) of 88% (\(\pm 0.51\%\) standard error), when compared to the 6648 visually attributed reference pixels (Table 1). Prior to editing and sub-tile thresholding the overall accuracy was 87%. Although the increase in accuracy due to manual editing appears small, it was not uniform across the state, as some areas required little or no editing while others required extensive revisions. For example, four of the catchment areas improved by 3%-4% in overall accuracy due to the manual editing. For the full accuracy assessment by catchment area see the supplementary material. The manual editing was particularly effective in reducing the commission of false positive tree pixels. This was measured by the user's accuracy for tree cover pixels (calculated as the proportion of correctly classified tree pixels as a percentage of the all classified tree pixels), which increased from 85% (\(\pm 0.99\%\)) to 90% (\(\pm 0.73\%\)) after editing (Table 1).  
Figure 5: A comparison of the modelled foliage projective cover (FPC) derived from the time-series of SPOT5 imagery to field measured FPC at 76 sites across New South Wales, Australia. The dashed lines and equations represent the line of best fit derived from ordinary least squares regression, which confirmed that the SPOT5 FPC had a stronger relationship (lower RMSE and higher \(r^{2}\)) to field data when the midstorey component was included.  
The producer's accuracy for tree cover pixels (also known as the true positive rate or TPR, calculated as the percentage of reference tree pixels that are correctly classified), was reduced after editing from 75% (\(\pm\)1.21%) to 73% (\(\pm\)1.21%). Although some of the manual editing targeted these omission errors, through sub-tile thresholding and digitising, it is clear that most were not corrected. This reflects the difficulty in mapping tree cover in fragmented landscapes.  
The final binary tree cover classification performed well for most of the catchment areas, with eight of the 13 catchments having overall accuracies greater than 90%. The least accurate catchments were Western (82% \(\pm\) 1.41%) and the Lower Murray-Darling (82% \(\pm\) 1.69%).
Fat stabilizes ice crystals during the freezing process, facilitates the aeration of the mixture, increases the resistance of ice cream to melting, improves the viscosity and flavor, and imparts a smooth texture. In general, the fat content of ice cream is around 8%. Most often, liquid or powdered cream is used as milk fat, and butter, butter oil, or anhydrous fat is also used. An alternative to fat is oleogels derived from vegetable oils. Oleogels are formed by stabilizing oil in a network created by a gelling medium, and thus, they have a viscous and elastic consistency. Due to oleogelation, oil molecules are trapped in a three-dimensional network, resulting in no change in their chemical composition, and the resulting structure has the properties of a solid [9,10]. In addition, organogels with high gelator concentrations can be used to prepare ice creams with quality characteristics comparable to those of the samples containing milk cream. Organogels are successfully used in artisanal ice cream to prepare low-saturated-fat products (saturated fat < 0.9 g/100 g) with added plant sterols, intended for people who wish to lower their blood cholesterol level [11]. One of the major byproducts of the tomato concentrate industry is tomato seeds. Tomato pomace consists primarily of skin and seeds. Tomato seeds can be separated from the pulp and skin using a sedimentation system, dried, and used in oil extraction. Tomato seed oil has a high content of unsaturated acids--linoleic acid contributing to more than 50% and oleic acid to more than 20%--which prevents floral vasodilation, thrombosis, increase in cholesterol content, and atherosclerosis. It also contains proteins with good functional and nutritional properties. In addition, antioxidant properties of tomato seeds have been reported [12,13,14]. This study aimed to investigate the potential use of tomato seed oil in the form of oleogels in ice cream production and the possibility of using ultrasound pasteurization as an alternative to traditional pasteurization. Thus, ice cream mixtures with different percentages of oleogels were prepared and subjected to both traditional and ultrasoundpasteurization. The physicochemical properties of the resulting ice cream mixtures were analyzed using differential scanning calorimetry, Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM).
## 3 The Proposed Approach
### Solution to HFLRSC  
This section will provide a brief overview of the HFLRSC solution and essential steps. The optimization problem in Equation (7) is convex and can be solved by various methods. For efficiency, we adopt in this paper the Augmented Lagrange Multiplier (ALM) [24] method. We first convert Equation (7) to the following equivalent problem:  
\[\min_{\mathbf{C},\mathbf{R},\mathbf{P}}~{}\|\mathbf{C}\|_{*}+\lambda_{1}\|\mathbf{X}\mathbf{R}-\mathbf{H} \|_{F}^{2}+\lambda_{2}\mathbf{\Omega}(\mathbf{P})~{}~{}s.t.~{}~{}\mathbf{X}=\mathbf{X}\mathbf{R}+ \mathbf{P},\mathbf{R}=\mathbf{C} \tag{10}\]  
The problem can be solved by the ALM method, and the objective function using the ALM method can be written as  
\[\begin{split}\mathbf{L}=&\|\mathbf{C}\|_{*}+\lambda_{1}\| \mathbf{X}\mathbf{R}-\mathbf{H}\|_{F}^{2}+\lambda_{2}\mathbf{\Omega}(\mathbf{P})+\\ &\frac{\mu}{2}\bigg{(}\|\mathbf{X}\mathbf{R}+\mathbf{P}-\mathbf{X}+\frac{\beta_{ 1}}{\mu}\|_{F}^{2}+\|\mathbf{C}-\mathbf{R}+\frac{\beta_{2}}{\mu}\|_{F}^{2}\bigg{)}\end{split} \tag{11}\]  
The above problem is unconstrained. Thus, it can be minimized with respect to \(\mathbf{C}\), \(\mathbf{R}\), and \(\mathbf{P}\) by fixing the other variables and then updating the Lagrange multipliers \(\beta_{1}\) and \(\beta_{2}\)where \(\mu>0\) is a penalty parameter. The inexact ALM method, also called the alternating direction method, is used in HFLRSC and is outlined in Algorithm 1.  
In Algorithm 1, the subscript \(t\) indicates the index of iteration. \(\Theta(a,b)=\mathbf{U}\mathbf{S}_{b}(\mathbf{\Sigma}_{a})\mathbf{V}^{T}\) is the singular value thresholding (SVT) operator, in which \(\mathbf{U}\mathbf{\Sigma}_{a}\mathbf{V}^{T}\) is the singular value decomposition (SVD) of a and \(\mathcal{S}_{b}(\mathbf{X})=Sign(\mathbf{X})*max(|\mathbf{X}|-q,0)\) is the soft threshold operator.  
```
Input: Data \(\mathbf{X}=\left\{\mathbf{x}_{i}|i=1,\dots,N\right\}\), trade-off parameters \(\lambda_{1}\) and \(\lambda_{2}\), Butterworth filter order \(n_{1}\) and cut-off frequency \(D_{0}\). Output: Self-representation matrix \(\mathbf{C}\)  Initialize filter by parameters \(n_{1}\) and \(D_{0}\) ;  Extract the high-frequency texture components of datasets by (4); whilenot convergencedo  Update \(\mathbf{C}\) by \(\mathbf{C}_{t+1}=\Theta((\mathbf{R}-\frac{\beta_{2}}{\mu})t,\frac{1}{\mu})\);  Update \(\mathbf{R}\) by \(\mathbf{R}_{t+1}^{s}=((2\lambda_{1}+\mu)\mathbf{X}^{T}\mathbf{X}+\mu*1)^{-1}(2\lambda_{1} \mathbf{X}^{T}\mathbf{H}+\mu\mathbf{X}^{T}(\mathbf{P}-\mathbf{X}-\frac{\beta_{1}}{\mu})t+\mu(\mathbf{ C}+\frac{\beta_{2}}{\mu}t))\);  Update \(\mathbf{P}\) by \(\mathbf{P}_{t+1}=\mathcal{S}_{\frac{\lambda_{1}}{\mu}}((-\mathbf{X}\mathbf{R}+\mathbf{X}-\frac {\beta_{1}}{\mu})t)\) ;  Update parameters \(\begin{cases}\beta_{1}=\beta_{1}+\mu(\mathbf{X}\mathbf{R}+\mathbf{P}-\mathbf{X})\\ \beta_{2}=\beta_{2}+\mu(\mathbf{C}-\mathbf{R})\\ \mu=\mu*\rho\end{cases}\)  end while
```  
**Algorithm 1**The algorithm of HFLRSC.
## 2 System Model
### Pre-Equalization  
To compensate the signal distortion caused by the wireless channel, we propose a channel adaptive pre-equalizer for SWIPT. This channel-adaptive pre-equalizer weighs the transmitted signal with a weight to optimize the SWIPT performance based on the perfect Channel State Information (CSI) at the transmitter, without changing the total transmit power. Therefore, we normalize the transmit power in this work. The received power, however, depends on the random channel fading. As we neglect path loss in our analysis, this means that we can instantaneously have a larger received power compared to the transmitted power, due to the diversity gain related to multipath. On average, transmitted and received power are the same. We assume \(\mathbf{w}=[w_{1};w_{2};\cdots]\) is the general pre-equalizer, where \(w_{n}\) is a complex weight multiplied with the transmitted signal at frequency bin \(f_{n}\).
## 5 Comparisons with SOTA Methods
### Experiments on Reduced-Resolution Datasets

Table 6: Quantitative results of different methods at reduced resolution for the QB dataset. Figure 12 presents the results for the WV4 dataset. Analysis of the residual images indicates that deep learning (DL) methods outperform others, highlighting the superior panchromatic sharpening capabilities of DL approaches. In Figure 10, the ground truth (GT) image reveals a greater number of scattered white roofs. As shown in the subsequent residual images, traditional methods exhibit suboptimal panchromatic sharpening effects \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **SAM \(\downarrow\)** & **ERGAS \(\downarrow\)** & **Q2N \(\uparrow\)** & **CC \(\uparrow\)** \\ \hline
**EXP** & 7.5665 & 10.4587 & 0.6677 & 0.7925 \\
**BDSD-PC** & 5.9096 & 5.8233 & 0.7138 & 0.9427 \\
**GSA** & 6.0134 & 6.2266 & 0.7017 & 0.9324 \\
**PRACS** & 6.6875 & 6.8691 & 0.7797 & 0.9282 \\
**MTF-GLP** & 5.9651 & 6.1457 & 0.7027 & 0.9347 \\
**PNN** & 3.7474 & 3.4182 & 0.7464 & 0.9539 \\
**PanNet** & 3.5791 & 3.1939 & 0.7546 & 0.9593 \\
**MSDCNN** & 3.4938 & 3.0776 & 0.7549 & 0.9612 \\
**TFNET** & 3.0540 & 2.5875 & 0.7685 & 0.9714 \\
**GGPCRN** & 2.9430 & 2.4572 & 0.7741 & 0.9743 \\
**MUCNN** & 3.2474 & 2.8515 & 0.7588 & 0.9661 \\
**PanFormer** & 3.1938 & 2.8036 & 0.7596 & 0.9666 \\
**SSIN** & 2.9114 & 2.4036 & 0.7757 & 0.9752 \\
**DCMFN (ours)** & **2.8799** & **2.3422** & **0.7866** & **0.9810** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quantitative results of different methods at reduced resolution for the WV2 dataset. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Methods** & **SAM \(\downarrow\)** & **ERGAS \(\downarrow\)** & **Q2N \(\uparrow\)** & **CC \(\uparrow\)** \\ \hline
**EXP** & 2.1106 & 1.9301 & 0.7611 & 0.9640 \\
**BDSD-PC** & 2.1545 & 1.2828 & 0.8802 & 0.9827 \\
**GSA** & 1.7325 & 1.2715 & 0.8779 & 0.9821 \\
**PRACS** & 1.9273 & 1.3257 & 0.8793 & 0.9831 \\
**MTF-GLP** & 1.7301 & 1.2954 & 0.8765 & 0.9818 \\
**PNN** & 1.9462 & 1.9320 & 0.8364 & 0.9595 \\
**PanNet** & 1.8639 & 1.8076 & 0.8483 & 0.9637 \\
**MSDCNN** & 1.8139 & 1.7549 & 0.8561 & 0.9652 \\
**TFNET** & 1.4770 & 1.2671 & 0.8981 & 0.9815 \\
**GGPCRN** & 1.5211 & 1.3458 & 0.9079 & 0.9849 \\
**MUCNN** & 1.6931 & 1.3561 & 0.8742 & 0.9697 \\
**PanFormer** & 1.6050 & 1.5103 & 0.8751 & 0.9736 \\
**SSIN** & 1.4803 & 1.3238 & 0.8911 & 0.9858 \\
**DCMFN (ours)** & **1.4240** & **1.2587** & **0.8924** & **0.9901** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative results of different methods at reduced resolution for the WV4 dataset.
## 1 Introduction
Nevertheless, the torque characteristics produced by the HE-DSDPM are still unsatisfactory compared to those produced by other stator-PM machines due to its limitation  
Figure 1: Cross-section perspective of the HE-DSDPM with: (**a**) conventional and (**b**) multi-tooth structures. issue of the configuration such as for the stator and rotor topologies, PM characteristics and slot area of the winding coil [33]. To solve this problem and further improve torque production, structural modification by using the multi-tooth technique seem a good candidate. The structural multi-tooth modification is a helpful solution in adjusting the stator/rotor combination, adapting the PM characteristics and enlarging the slot winding area in the electrical machine. This modification of structure also results in an increase in torque density and the reduction of the ripple torque [34]. Many literature reports presented the simulation design of the stator-PM machines using the structural multi-tooth modification for effective torque production, such as the multi-tooth technique utilised in FSPM for torque capability improvement, the analytical structure of an alternative multi-tooth FSPM for high torque density, the structural simulation of several FSPM types to study electromagnetic performance, and a theoretical study between multi-tooth and V-shaped FSPM to improve torque production [35; 36; 37; 38]. Furthermore, multiple research reviews related to utilising the multi-tooth technique in HE machines, such as the design of the multi-tooth HE-FSPM for EV propulsion, the innovation of multi-tooth HE motors for torque enhancement and the investigation of the influence of stator/rotor and PM combinations in a HE-FRPM on electromagnetic performance [39; 40; 41]. This paper aims to modify the structure of the HE-DSDPM using the multi-tooth technique to improve torque production. The structural modification using the multi-tooth technique of HE-DSDPM is explained in the detail. The influence of PM and stator arcs on torque production, including slot opening PM arc, \(\theta_{sp}\), stator yoke PM arc, \(\theta_{qp}\), stator tip arc, \(\theta_{st}\), and stator teeth arc, \(\theta_{t}\), is considered by optimization to obtain the optimal structure of the multi-tooth HE-DSDPM. Electromagnetic performance is examined based on magnetic flux distribution, flux linkage, back-EMF, total harmonic distortion (THD), cogging torque and electromagnetic torque with different current excitation and then compared to a conventional HE-DSDPM. All electromagnetic results are studied using the 2-D finite element analysis (FEA).