esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Results--Analysis and Discussion
### California Bearing Ratio (CBR) and Suelling (s)  
One of the most common reasons for replacing soil material in engineering practice is the low value of the **California bearing ratio (CBR)**, which is typical for fine-grained (clayey) soils. The results of this research, presented in Figure 9, show that each of the considered materials as stabilisers--conventional, as well as waste and alternative--contributed to the improvement of the CBR value of the soil 7 and 28 days after the chemical treatment. Nevertheless, the greatest difference in effects between traditional stabilisers and all other stabilisers used in this research was observed precisely with regard to this property of the soil. Namely, compared to the soil in its natural state (CBR = 2.71%), the greatest improvement was registered with the addition of traditional stabilisers. With the addition of lime, an increase of seven times (CBR = 18.97%) was registered, and with the addition of cement binder, up to 8.5 times (CBR = 22.97%) 28 days after the treatment in both cases. The smallest increase in CBR value was registered by adding rock flour (CBR = 4.22%), i.e., potassium chloride (CBR = 3.94%). Considering that in the road construction for the subgrade at roadbed, according to the technical conditions a CBR value greater than 3% is required, the obtained results indicate the fact that each of considered stabilisers according to this criterion can find their application for the stabilisation of clayey soil for the given purposes.  
Therefore, by comparing all the examined stabilisers, it can be concluded that from the aspect of CBR, conventional stabilisers resulted in the highest increase, between which cement stands out slightly. Considering the optimal stabiliser content, however, the use of lime is more rational than cement.  
According to the CBR test standard, samples are immersed in water for 96 h before testing in laboratory conditions, during which **soil swelling (s)** is measured. All analysed stabilisers contributed to a significant reduction in soil swelling (Figure 10), in particular 28 days after the soil chemical treatment. In addition, all the considered stabilisers con  
Figure 9: Change in the soil CBR value over time given the optimal content of the considered conventional, waste, and alternative materials as stabilisers.  
tributed to the reduction of swelling below 1.75% as early as 7 days after the treatment. The most significant reduction after 7 days since the treatment was registered with the addition of cement, whereas after 28 days, the greatest reduction in swelling was shown by the soil sample treated with the optimal amount of lime (reduction up to fifteen times). The minor effect on reducing swelling was achieved by adding rock flour, i.e., potassium chloride (reduction up to five times).  
In the end, it can be concluded that with regard to soil swelling, the greatest effect was achieved by conventional stabilisers, although the difference compared to the other considered stabilisers is not so pronounced, as was the case with the previously discussed geotechnical properties of the soil.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### FAPAR Definition  
FAPAR is highly temporally variable and is easily affected by illumination. Thus FAPAR has to be defined at specific time step under specific illumination conditions. The use of FAPAR in dynamic vegetation models could run at a time step smaller than a day or at daily time step, which requires instantaneous FAPAR values or daily integrated FAPAR values [3]. According to illumination conditions, FAPAR could be defined as the black-sky FAPAR (FAPAR from direct solar radiation) and the white-sky FAPAR (FAPAR from diffuse solar radiation) [3]. The instantaneous FAPAR is always a weighted sum of black-sky FAPAR and white-sky FAPAR, where the weight is related to the proportions of skylight. Depending on the inclusion of PAR absorbed by either green (live leaves) or all (including woody materials and dead leaves) part of vegetation, FAPAR could be discriminated as green FAPAR and total FAPAR [41]. For MODIS FAPAR product, FAPAR is defined as the instantaneous FAPAR at the time of satellite overpass [3] using direct and diffuse incoming radiation [41]. The more detailed overview of product definitions could be referred to [5; 41].  
_In-situ_ FAPAR values are generally measured by radiation balance monitoring [13; 42] or by measuring directional gap fraction with hemispherical photos [43]. In the latter case, the measured FAPAR is approximated by the light interception efficiency and only includes absorption of direct solar radiation.  
The study attempts to validate the MODIS FAPAR product using ground measurements by AccuPAR device (the radiation balance monitoring approach). Essentially the definition of FAPAR between the satellite product and ground measurements agrees, which lies the basis of the study.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 5 Uncertainty Analysis  
A detailed uncertainty analysis of both the reflectance-based approach and the RadCaTS has been previously documented, and only a summary is presented here [17; 110; 111]. The uncertainty analysis for the RadCaTS was completed as part of the requirements for inclusion as an official RadCalNet site, and the uncertainty of the reflectance-based approach was most recently analyzed for work supporting the launch of Landsat 8. The current non-spectral estimate of the RadCaTS uncertainty is approximately \(\pm\)5% from 400 nm to 2400 nm, and the estimate for the reflectance-based approach is approximately \(\pm\)2.6% in the middle of the visible spectral region.
## 3 Results
### Effects of Ultrasonic Frequency on the CO\({}_{2}\) Ultimate Absorption Amount and AR
#### 3.1.2 Effects of Ultrasonic Vibration on CO\({}_{2}\) AR  
The effects of ultrasonic vibration on CO\({}_{2}\) AR are shown in Figure 3. It can be seen that under mechanical agitation, the CO\({}_{2}\) AR was 0.063, 0.061, 0.058, 0.053, 0.046, and 0.035 v(%/s) at the absorption times of 5, 10, 15, 20, 25, and 30 s, respectively. By increasing the absorption time, the cement paste gradually thickens causing the CO\({}_{2}\) AR to decrease accordingly. The cement paste changed from a fluid to pasty fluid gradually after 30 s. At 35 s, the CO\({}_{2}\) absorption of the paste reached saturation and the pastes could not absorb CO\({}_{2}\) any further.  
The CO\({}_{2}\) AR of fresh cement paste increased significantly under ultrasonic vibrating agitation compared to that under mechanical agitation. At an ultrasonic frequency of 40 kHz, the CO\({}_{2}\) AR of the cement paste was 0.08, 0.075, 0.067, and 0.055 v(%/s) at the absorption times of 5, 10, 15, and 20 s, respectively, which are 27%, 23%, 15.5%, and 3.8% higher compared to those under mechanical agitation. After 20 s, the cement paste changes from fluid to pasty fluid and the CO\({}_{2}\) absorption of paste reaches saturation at 27 s.  
At an ultrasonic frequency of 28 KHz, the CO\({}_{2}\) AR of cement paste is 0.085, 0.08, 0.072, and 0.054 v(%/s) at the absorption times of 5, 10, 15, and 20 s, respectively. The CO\({}_{2}\) AR of cement paste increases to some extent with the decrease of ultrasonic frequency. At an ultrasonic frequency of 20 kHz, the CO\({}_{2}\) AR of cement paste is 0.108, 0.1, and 0.077 v(%/s) at 5, 10, and 15 s respectively. The CO\({}_{2}\) AR of cement paste increases significantly.  
According to the results, it can be concluded that the CO\({}_{2}\) absorption time of cement paste gradually shortens, while the CO\({}_{2}\) AR and CO\({}_{2}\) AA further increase when the ultrasonic frequency gradually decreases from 40 kHz to 28 and 20 kHz. This is due to the fact that when the ultrasonic frequency increases, the ultrasonic intensity will increase accordingly. When the increased ultrasonic intensity is excessive, there are excessive bubbles produced, which conversely increase attenuation of scattering, forming barriers of the ultrasonic wave. On the other hand, increasing of ultrasonic intensity will also lead to increasing of nonlinear attenuation, which is disadvantageous for uniform agitation. Consequently, particles of the cement paste are not well distributed. Therefore, when the  
Figure 3: Effects of ultrasonic vibration on CO\({}_{2}\) AR.  
ultrasonic frequency is 20 kHz, the cavitation effect of ultrasonic agitation is best, and the corresponding CO\({}_{2}\) AR and CO\({}_{2}\) AA is highest.
# _Applied_
## 8 Discussion  
The MosAlc approach demonstrates how the combination of different classical classifiers can lead to high accuracy for environmental sound classification when compared to an equivalent DL approach such as the one proposed in Section 7. Figure 13 summarizes the achievable accuracies for both approaches for two different platforms. Notice the strong dependence on the dataset. While the MosAlc approach achieves the highest accuracy for ESC-10 dataset, the novel lightweight CNN outperforms the classical ML approach for the UrbanSound dataset. Nonetheless, both perform similarly for the BDLib dataset.  
The cost to achieve such an accuracy with classical ML is reflected in the classification time. Figure 14 depicts the classification time and the inference time for the classical ML and the DL approach respectively. The time cost of the MosAlc approach to achieve such an accuracy is significantly high, especially when considering embedded platforms, reaching up to 28 times higher than our proposed lightweight CNN. Besides such a timing difference, the overall execution time for audio recognition, the feature extraction time plus the classification or the inference time, is still dominated by the feature extraction time as shown in Figure 6. The lowest feature extraction time is several times higher than the maximum MosAlc classification time.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Datasets** & **Inference Time [ms]** & \(\mathbf{F_{1}}\) **Micro** & \(\mathbf{F_{1}}\) **Macro** & \(\mathbf{F_{1}}\) **Weighted** \\ \hline BDLib & 0.275 & 0.970 & 0.970 & 0.970 \\ ESC-10 & 0.275 & 0.909 & 0.903 & 0.909 \\ UrbanSound & 0.275 & 0.830 & 0.831 & 0.831 \\ \hline \hline \end{tabular}
\end{table}
Table 15: RPi with TFLite and post training quantization (using 8 bit).  
The overall execution time has a direct impact in the selection of the audio frame size when windowing. Assuming that the execution of the audio recognition is done in streaming and an overlap of 50% between each audio frame due to windowing, based on the experimental timings, the feature extraction time defines the minimum windowing. In fact, based on the timings in Figures 6 and 14, the feature extraction time for ten features on the RPi is higher than an audio frame of 500 ms, which is the condition when considering audio frames as short as 1 s. As a result, short audio frame sizes are defined by the feature extraction time and the selected platform. Nonetheless, from the embedded perspective, larger audio frame size can lead to power savings since the feature extraction time and the classification or inference time would be lower than the audio frame size, enabling the use of power saving modes present on embedded devices.  
Figure 14: Classification time of the MosAlc approach vs. Inference time of our proposed lightweight CNN based on the feature sets and the platforms.  
Figure 13: Accuracy (\(F_{1}\)_micro score_) of the MosAlc approach vs. our proposed lightweight CNN based on the feature sets and the platforms.
# Modelling and Assessment of a New Triple-Frequency IF1213 PPP with BDS/GPS
## 2 Materials and Methods  
In this section, the functional models of three IF1213 models (TF-C, TF-F, and TF-CF) are described based on the general observation model. Additionally, the TF-FP model is also presented. For convenience, the three frequencies are numbered 1, 2 and 3. The specific frequencies indicated by the numbers are shown in Table 1, and the values in parentheses are in MHz. For BDS-3, the IF combination of B1I/B1C causes the noise amplification factor to be much larger than that of B3I/B1C. Therefore, the IF13 combination utilizes the IF combination of B3I/B1C; this means that for BDS-3, the IF combination is IF1223 in the experiment, but it is still noted as IF1213 during the derivation of the model below for ease of expression.
###### Abstract  
Physical performance data is essential for planning youth training effectively; however, there is a lack of scientific information regarding performance in youth competitions. To address this gap, an innovative study was conducted with Portuguese U14 regional selections. Each player was equipped with a WimuPro(tm) inertial device. Six variables were considered: accelerations, decelerations, speed, player load, impacts, and high impacts. The objective of this study, based on data from official competitions, was to statistically analyze the distribution and intensity thresholds of six physical performance variables across five defined zones. A cluster k-means analysis was performed for a significance value of \(p<0.05\). Five zones were identified for all variables: acceleration [\(<\)0.37; 0.37 to 0.81; 0.81 to 1.54; 1.54 to 3.49; \(>\)3.49 m/s\({}^{2}\)], deceleration [\(<\)\(-\)0.26; \(-\)0.27 to \(-\)0.63; \(-\)0.63 to \(-\)1.22; \(-\)1.22 to \(-\)2.545; \(>\)\(-\)2.54 m/s\({}^{2}\)], speed [\(<\)5.42; 5.42 to 10.19; 10.20 to 14.63; 14.64 to 18.59; \(>\)18.59 km/h\({}^{2}\)], player load [\(<\)1.07; 1.07 to 1.36; 1.37 to 1.63; 1.64 to 1.95; \(>\)1.95 u.a./min], impacts [\(<\)133.45; 133.45 to 158.75; 158.76 to 181.45; 181.46 to 206.59; \(>\)206.59 cont/min], and high impacts [\(<\)1.13; 1.14 to 2.11; 2.12 to 3.13; 3.14 to 4.42; \(>\)4.42 cont/min]. These intensity zones should be taken into account to optimize training and enhance the understanding of competition in U14 basketball.  
Aticle  
Academic Editor: [PERSON]  
Received: 4 July 2025  
Revised: 24 July 2025  
Accepted: 30 July 2025  
Published: 4 August 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON]; [PERSON] Profiling External Load in U14 Basketball: Cluster Analysis of Competition  
Performance Using Inertial Devices.  
_Appl. Sci._**2025**, 1586; [[https://doi.org/10.3390/appl5158616](https://doi.org/10.3390/appl5158616)]([https://doi.org/10.3390/appl5158616](https://doi.org/10.3390/appl5158616))  
**Copyright**: 0225 by the authors.  
License MDPL, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
**Keywords:** external load; Portuguese youth basketball; inertial measurement units; cluster analysis; performance metrics
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Method Based on Model  
#### 4.3.1 Fractal Model (FM)  
The fractal model (FM) was first derived by [PERSON] [77] in 1983. Fractal dimension and porosity are the most important metrics in a fractal model. The former is a measure of complexity and irregularity, while the latter represents structural change or unevenness. In 2008, [PERSON] et al. [78] used a membership function to analyze the partial structure and fractal features of images for extracting new information. [PERSON] et al. [79] researched a multifractal-based segmentation method to locate defects, and then extracted 10 features, such as multi-dimensional fractal dimension, variance, mean value, and maximum value in the principal component vector to achieve defect detection. The method achieved accuracies of 97.9%.
## 3 Results  
### Validation of Satellite SWH Data  
The altimeter SWH data were validated by comparison with the SWH measurements from the IORS and Donghae buoy prior to the estimation of the extreme SWH value. Figure 6a,b present a comparison between the altimeter SWH data and the in situ measurements at the IORS and the Donghae buoy. When the collocation procedure between altimeter along-track SWH data and in situ measurements was performed using the criteria of 30 min and 50 km in time and space, the numbers of the matchup data produced at the IORS and Donghae buoy were 833 and 556, respectively. The SWH data from the altimeters were in good agreement with both SWH measurements from IORS and Donghae buoy. The comparison resulted in RMSE of 0.38 m at the IORS and 0.23 m at Donghae buoy, respectively. Compared with the SWH measurement from IORS, it was noted that there was a tendency for the altimeter SWH data to be slightly overestimated with a positive bias error of 0.23 m. This tendency was found to be due to a mixture of errors caused by the characteristics of the IORS platform, which observes the wave height using a microwave instrument at a height of 35 m in addition to satellites [52]. In contrast, the altimeter SWH data showed an insignificant bias of 0.01 m in comparison with the measurements from the Donghae buoy.  
The IORS and the Donghae buoy measure SWH data with a high temporal resolution of approximately 1 h at a point location, while the altimeter data are sparsely distributed with different temporal differences. Therefore, the SWH data may differ from each other depending on these observation characteristics. Considering the differences, monthly averaged values and the maximum values of the SWHs were calculated for both in situ measurements and satellite data. In the case of altimeter SWH data, the mean and maximum values were calculated using along-track data within a bin of 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) based on the location of the IORS and the Donghae buoy. Figure 6c-f indicates the comparisons of the monthly means of wave heights from the in situ observation stations and satellite observations. Overall, both the average and maximum SWHs observed from satellites and point stations were in good agreement for the entire period. Two large peaks of more than 10 m in the monthly maximum (Figure 6d) were generated by the typhoons Muifa and Bolaven, which passed over the IORS in August 2011 and August 2012, respectively. Similar values were measured for both the IORS and the altimeter. However, the maximum SWH obtained from the IORS showed a peak in August 2010 when Kompasu passed, while the maximum SWH from the altimeter did not show these characteristics. When comparing the monthly maximum SWH observed from Donghae buoy and altimeters, some peaks measured in the Donghae buoy did not appear in the altimeter observations (Figure 6f). This suggests that the altimeters failed to observe the extreme waves generated by storms that occurred suddenly in the East Sea (Sea of Japan). This is related to the fast movement of the typhoons at a relatively high speed and the fast-developed low-pressure [47; 48].
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 3 Analysis and Discussion of Experimental Results
### Experimental Results and Analysis
#### 3.3.1 Ablation Study
The comparison results are shown in Figure 10.  
In the figure, Column Model-2-A represents the feature activation map (displayed using Grad-CAM [26]) for the first corrosion detection category, \"fair,\" from Model-2, while Column Model-2-B shows the feature activation map for the second category, \"poor.\" Similarly, Columns Model-6-A and Model-6-B depict the corresponding feature activation maps for Model-6. Figure 9: Module ablation experiment visualization comparison. The corrosion rating is divided into four levels: good, fair, poor, and severe, represented by black, red, green, and yellow, respectively. Subsequent figures follow the same representation. From the feature activation maps, it is evident that after introducing the SMFE method, the model focuses more on the target category areas. The red regions are more concentrated on the target areas, while the blue regions are more spread across other categories or backgrounds. This indicates that the SMFE method effectively enhances the model's attention to important features, thereby improving detection accuracy. Figure 11 shows the comparison of the training loss for different modules of MCD-Net, where the original data has been polynomially fitted and marked with different line styles. The left panel shows the mIOU curve over the training epochs, while the right panel displays the training loss curve over the epochs. The curves compare multiple models, including Baseline, ABFF, SMFE, Transformer, and MCD-Net. Compared to individual modules, the MCD-Net model proposed in this paper demonstrates better convergence speed and stability during the training process.
## 2 Results and Discussion
### Drying Kinetics
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Drying Condition** & **Model** & \(a\) & \(k\) & \(n\) & \(R^{2}\) & **RMSE** & **Drying Time (min)** \\ \hline \multirow{3}{*}{CD 40 \({}^{\circ}\)C} & Lewis & – & 0.0138 & – & 0.9989 & 0.0113 & \multirow{3}{*}{360} \\  & Page (modified) & 0.9937 & 0.0126 & 1.0209 & 0.9989 & 0.0117 & \\  & Henderson–Pabis & 1.0000 & 0.0138 & – & 0.9989 & 0.0117 & \\ \hline \multirow{3}{*}{CD 50 \({}^{\circ}\)C} & Lewis & – & 0.0246 & – & 0.9962 & 0.0216 & \multirow{3}{*}{240} \\  & Page (modified) & 0.9943 & 0.0151 & 1.1293 & 0.9989 & 0.0124 & \\  & Henderson–Pabis & 1.0284 & 0.0254 & – & 0.9972 & 0.0193 & \\ \hline \multirow{3}{*}{CD 60 \({}^{\circ}\)C} & [PERSON] & – & 0.0287 & – & 0.9778 & 0.0493 & \multirow{3}{*}{180} \\  & Page (modified) & 1.0324 & 0.0371 & 0.9376 & 0.9788 & 0.0521 & \\  & Henderson–Pabis & 1.0168 & 0.0293 & – & 0.9782 & 0.0507 & \\ \hline \multirow{3}{*}{VMD 6 W/g} & [PERSON] & – & 0.1860 & – & 0.9794 & 0.0534 & \multirow{3}{*}{21} \\  & Page (modified) & 0.9983 & 0.0766 & 1.4702 & 0.9996 & 0.0086 & \\  & [PERSON]–Pabis & 1.0402 & 0.1919 & – & 0.9813 & 0.0549 & \\ \hline \multirow{3}{*}{VMD 9 W/g} & [PERSON] & – & 0.1860 & – & 0.9794 & 0.0534 & \multirow{3}{*}{21} \\  & Page (modified) & 0.9983 & 0.0766 & 1.4702 & 0.9996 & 0.0086 & \\ \cline{1-1}  & [PERSON]–Pabis & 1.0402 & 0.1919 & – & 0.9813 & 0.0549 & \\ \hline \multirow{3}{*}{VMD 12 W/g} & [PERSON] & – & 0.2769 & – & 0.9801 & 0.0523 & \multirow{3}{*}{14} \\  & Page (modified) & 0.9986 & 0.1403 & 1.4582 & 0.9996 & 0.0091 & \\ \cline{1-1}  & [PERSON]–Pabis & 0.9986 & 0.1403 & – & 0.9996 & 0.0091 & \\ \hline \multirow{3}{*}{CPD–VMFD 50 \({}^{\circ}\)C, 9} & [PERSON] & – & 0.0754 & – & 0.9811 & 0.0521 & \multirow{3}{*}{105} \\  & Page (modified) & 0.0733 & 0.7681 & 0.5011 & 0.9997 & 0.0007 & \\ \cline{1-1}  & [PERSON]–Pabis & 0.0725 & 0.3734 & – & 0.9782 & 0.0049 & \\ \hline \hline \end{tabular}  
* \(a\)—model constant; \(k\)—drying constant; \(n\)—dimensionless empirical constant; CD—convective drying; VMD—vacuum microwave drying; CPD—convective pre-drying; VMFD—vacuum microwave finish drying; \(R^{2}\)—coefficient of determination; RMSE—root mean square error.  
\end{table} 
Table 1: Statistical parameters, total drying duration, and mathematical model constants describing the drying kinetics of _P. noiflora_ leaves. The drying rate also decreased with decreased moisture ratio. This falling rate period is characterized by a continuous decrease in drying rate [17]. With VMD, a higher drying rate is typically observed at the initial stage as moisture content was initially higher in leaves, and this finding corresponded to a higher dielectric constant. Larger microwave radiation was absorbed by the sample, generating more heat, thereby increasing the drying rate. As drying progressed, the reduced amount of moisture reduced the microwave absorption, resulting in decreased drying rates.