esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Spatial and Temporal Dynamics in Forest and Grassland Ecosystems
#### 3.1.2 Altai Mountains as a Critical Region for Ecosystem  
The Altai Mountains provide a relatively stable environment for forest and grassland ecosystems. The analysis of forest and grassland frequency in the Altai Mountains National Park Candidate Area from 2000 to 2020 (Figure 5) indicates that these ecosystems remained largely stable, with 84.3% of forest and 76.12% of grassland demonstrating continuous presence over the 21-year period. Forest cover is primarily concentrated in the northern part of the study area, while grassland is distributed throughout, with high and medium frequency presence concentrated in the southern region, comprising 18%. Additionally, the spatial distribution trends of forest (Figure S2) and grassland (Figure S3) were analyzed using the standard ellipse method. Over the 21-year period, the centers of gravity of these land cover types exhibited minimal change, with latitude and longitude variations within 1 degree, cumulative shifts of less than 10 km, and consistently remaining located in the Altai Mountains at elevations ranging from 1239 to 2740 m.  
The Theil-Sen median and Mann-Kendall statistical tests were used to analyze trends in forest and grassland in the Altai Mountains National Park Candidate Area from 2000 to 2020 (Figure 6). The area of grassland degradation was 9.24 times greater than the area of grassland improvement, while the area of forest improvement was 1.49 times greater than forest degradation. Forests exhibited a highly significant increasing trend, primarily in the northern part of the study area, accounting for 98.14% of the restored forest area. A highly significant decrease, accounting for 85.64% of degraded forest, was also observed. Part of the region experiencing highly significant decreases contains high-frequency forest presence areas, found along the edges of stabilized forests (Figure 4), which may expose these forests to edge effects. In contrast, 70.98% grassland showed a decreasing trend, with highly significant decreases accounting for 98.15% of the decline. Additionally, the grassland in the southern part of the study area increased, accounting for 48.05% of the rise, which may be attributed to the local policy encouraging the return of grazing land to grassland.  
Figure 4: Forest and grassland ecosystem transfer chord diagram in the Altai Mountains National Park Candidate Area from 2000 to 2020 (**a**) and its spatial transfer distribution (**b**).
# Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
## 4 Surrogate Modeling Techniques  
### Generality  
Nowadays, meta-model approaches are widely used in different engineering field. These models are used to construct approximations of the finite element method and describe the relationship between the input parameters and the responses of the system. Thus, the calculation cost is reduced. In this study, the meta-models are used to estimate the sound pressure level of coupled acoustic-structural problems.  
To start, let us consider a \(m\)-dimensional problem. We use \(\mathbf{x}=\{x_{1},x_{2},\ldots,x_{m}\}^{\mathsf{T}}\) to denote the design variables with \(m\) dimensions, \(\mathbf{x}_{S}=\{\mathbf{x}^{(1)},\mathbf{x}^{(2)},\ldots,\mathbf{x}^{(n)}\}^{\mathsf{T}}\) to indicate  
Figure 2: Flowchart of the MCS.  
a set of samples with \(n\) design points, and \(\mathbf{y}_{\rm S}=\{f(\mathbf{x}^{(1)}),\ldots,f(\mathbf{x}^{(n)})\}^{\rm T}\) to signify the corresponding outputs responses of the sample set. The pair \((x_{S},\mathbf{y}_{S})\) indicates the sampled data set. The objective of the meta-modeling is to build a meta model that predict the output of any untrited point \(\mathbf{x}\), thus, to estimate \(y(\mathbf{x})\) based on the pair \((x_{S},\mathbf{y}_{\rm S})\). The implementation of a surrogate model is a multi-step process. As presented by [PERSON] et al. [25], in order to build a good approximation of the original model, we need several steps: Sampling plans, exact evaluations, constructing a surrogate and verifying meta-model quality. The flowchart of this process is presented in Figure 3.  
The surrogate modelling approach first applies design of experiments (DOEs). Classical DOEs (fractional factorial designs, central composite designs, etc.) tend to spread the sample points around the border and only put a few points in the interior of the design space. They are primarily used for screening purposes and to build polynomial meta-models. For this, the Latin hypercube sampling (LHS), proposed by [PERSON] et al. [45], is selected to repeat sampling in the design space and then chooses a type of meta-model to represent the sampled data. Thus, the original analysis code can be replaced by the surrogate model during the uncertainty process.
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.4.4 Linear Discrimination  
LD uses features to create a linear model. LD finds a linear combination of features that characterizes or separates two or more classes of objects or events. It is unable to capture nonlinearity. Physiological responses may contain some nonlinearities that cannot be handled by a linear model. However, LD shows better performance in small data sets and with relatively small numbers of iterations for hyperparameter optimization training.
## 2 Metal Lattice-Polyurea Parallel Composite Structure and Theoretical Modeling  
### Metal Lattice-Polyurea Parallel Composite Structure  
The metal lattice-polyurea parallel composite structure consists of the metal lattice structure and the separate polyurea damping structure. The metal lattice provides significant load-bearing capacity, while the polyurea damping structure enhances the system's damping performance. Through the rational design of the polyurea damping structure's shape and size, along with precise modulation of the composite's elastic and damping properties, optimal vibration isolation and shock resistance performance can be tailored for specific application requirements.  
Polyurea materials exhibit linear elastic behavior during the initial deformation stage, resulting in relatively low damping. Therefore, preloading is essential to enhance their damping performance. Figure 1 illustrates the design of the polyurea damping structure and the metal lattice-polyurea parallel composite structure model. As shown in Figure 1a, the polyurea damping structure consists of two identical polyurea samples (#1 and #2). A pressing plate between the samples is connected to the external load via a guide rod, with an upper plate and a lower plate placed at the opposite end of each sample. The preload can be adjusted by modifying the thickness of the upper and lower plates. The guide rod is rigidly connected to the pressing plate, maintaining the polyurea samples in a state of compressive contact rather than rigid fixation. Consequently, when the vibration is transmitted to the system and the load mass applies downward pressure through the guide rod, polyurea sample (#2) primarily contributes to stiffness and damping; the stiffness and damping produced by polyurea sample (#1) can be ignored due to preload release or weakened contact. Under the opposite load condition, polyurea sample (#1) primarily provides stiffness and damping.  
As shown in Figure 1b, the upper ends of the two metal lattice structures are rigidly connected to the load mass, while the lower ends are fixed to the baseplate via support frames. The polyurea damping structures are fixedly connected to the load mass through guide rods. Together, the metal lattice-polyurea parallel composite structure and the load mass form a composite vibration isolation system.
# Attention-Based Active Learning Framework for Segmentation of Breast Cancer in Mammograms
## 4 Experimental Results
### Datasets and Measurements  
We collected breast cancer images from a cooperating hospital, which are not released due to privacy reasons. The dataset includes 1462 labeled breast cancer images, with resolution \(6781\times 3676\) pixels. Since images were acquired by different scanners, we divided all our samples into four parts based on the type of scanner, i.e., Breast-A, Breast-B, Breast-C and Breast-D.  
Various measurements were used to verify the effectiveness of the breast cancer image segmentation results, i.e., the mean Dice similarity coefficient (mDice), the mean intersection ratio (mIoU), and the mean absolute error (MAE). Where \(TP\), \(FP\) and \(FN\) indicate true positive, false positive and false negative samples, mDice can be calculated as:  
\[mDice=\frac{2 TP}{FP+2 TP+FN}. \tag{10}\]  
Note that a higher mDice implies a greater similarity between two samples.  
IoU is defined as the area of the intersection divided by the area of the union of the predicted bounding box, which can be evaluated by  
\[IoU=\frac{\text{area}\big{(}B_{p}\cap B_{gt}\big{)}}{\text{area}\big{(}B_{p} \cup B_{gt}\big{)}}, \tag{11}\]  
where \(B_{p}\) and \(B_{gt}\) are the predicted and ground-truth segmentation results, respectively.  
By calculating the Euclidean distance between the predicted and the ground-truth results, MAE can be defined as:  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|Y_{i}-\hat{Y}_{i}|, \tag{12}\]  
where \(n\) refers to the total number of samples, and \(Y_{i}\) and \(\hat{Y}_{i}\) are the predicted and ground-truth labels, respectively. Note that a lower MAE implies a better segmentation result.
# Prediction of Solvatochromic Polarity Parameters for Aqueous Mixed-Solvent Systems
## 1 Introduction  
The use of aqueous mixtures is preferable in many chemical processes (e.g., biomass conversion [1; 2], separation and fractionation [3] and processing of active pharmaceutical ingredients [4; 5; 6]) because of the benefit of a safe solvent. Solvent polarity is an informative data being used in solvent selections [7; 8; 9; 10] for preliminary engineering design and understanding the solvent effects on the chemical processes [11]. Polarity [12] is generally referred to as a solvent's capability for solute dissolution and can be quantified with many physical properties of solvents (e.g., electronic transition energy, solubility parameters and dielectric constant). Among the physical properties of solvents, the electronic transition energy (\(E_{\mathrm{T}}\)) is commonly used to quantify an empirical solvent scale (also known as Reichardt's polarity) because the parameter requires a simple measurement using a solvatochromic technique with an indicator [13; 14].  
The \(E_{\mathrm{T}}\) values of aqueous mixtures [15] generally tend to have a negative deviation from the ideality line (Figure 0(b)) due to preferential interactions of an indicator with cosolvents. There are the correlative models (preferential solvation model [16; 17; 18] and Jouyban-Acree model [19]) for representing a preferential trend in the aqueous mixtures, while there is no predictive model for estimating \(E_{\mathrm{T}}\) values of the mixture. It is supposed that an ideal model (Equation (1), Section 2.1) is most likely applied for estimations due to a simple calculation from a mole fraction average. However, the ideal model (dashed line, Figure 0(b)) caused a large deviation from experimental data (symbol, Figure 0(b)).  
According to our previous works, the predictive models for estimating \(E_{\rm T}\) values [20] and Kamlet-Taft dipolarity/polarizability (KT-\(\pi^{\bullet}\)) [21; 22] of nonpolar-polar mixtures have been proposed and the models provided a good predictive result as shown by the example for \(E_{\rm T}\) values in Figure 1(b) (blue solid line). Since both nonpolar-polar mixtures (Figure 1a) and aqueous mixtures (Figure 1b) show a similar negative deviation from ideality, the predictive model (Equation (2)) is expected to be applicable to the aqueous mixtures.  
With preliminary assessment, the predictive model (Equation (2)) is applicable to the aqueous mixtures (blue solid line, Figure 1b). The objective of this work is to predict the polarity parameters (\(E_{\rm T}\) values) of aqueous systems using the predictive model (Equation (2)). The predictive model was validated by experimental \(E_{\rm T}\) data of Reichardt indicator for eighteen aqueous mixtures that were collected from the literature [15; 23; 24; 25].
## 3 Results
### Mycotoxin Content  
The content of selected mycotoxins (AFB1, DON, FUM B1 and B2, HT-2 toxin, OTA, T-2 toxin, and ZEN) was measured in samples of the substrate (a mixture of kitchen and garden waste) and the leachate produced during the 45 days of the composting process (Supplementary Table S1). Most of these samples did not contain any of the analysed mycotoxins at levels above our method's limit of detection (LOD, 2 ug\(\cdot\)kg\({}^{-1}\)). Only a few samples contained AFB1, HT-2, FUMs, and/or ZEN, and only trace amounts of these compounds were detected. ZEN was most frequently detected, and unlike the other mycotoxins, its content in the substrate was found to be above our method's level of quantification (\(>\)5 ug\(\cdot\)kg\({}^{-1}\); LOQ), but this occurred only once. The presence of ZEN was shown by the presence of progeny ions, i.e., 319.3 \(\rightarrow\) 301.2; 319.3 \(\rightarrow\) 283.8, and a distinct peak at the retention time of 6.81 min (Supplementary Figure S2).  
To obtain additional information about the mycotoxin contamination of commercially available compost, mycotoxin content was also determined in several samples of mature compost collected from local composting facilities or purchased from garden centres. Only trace amounts of ZEN and AFB1 (\(<\)5 ug\(\cdot\)kg\({}^{-1}\)) were detected in the mature compost obtained from the composting plants and no mycotoxins were found in the commercial compost products purchased from the garden centres.
# Stability and Heat Input Controllability of Two Different Modulations for Double-Pulse MIG Welding
## 4 Process Control Experiment on Reduction of Heat Input in TP-MIG Welding and DP-MIG Welding
### Reducing Heat Input by Adjusting the Frequency of Low-Energy Pulse Train to Vary the Base Time  
If the parameters of the high-energy pulse train and the peak current and peak times of the low-energy pulse train are fixed, the heat input can be reduced by increasing the duration of the base current in the low-energy pulse train because this effectively reduces the average current. This approach is known as _variation in the base time_. Two DP-MIG welding experiments were conducted at a welding speed of 0.6 m/min, and the parameters are listed in Table 4. The appearance and morphology of the welds formed in these experiments and their corresponding current waveform plots are illustrated in Figure 17.  
Two TP-MIG welding experiments were also conducted at the same welding speed (0.6 m/min), and the parameters are listed in Table 5. The superficial morphologies of the welds formed in these experiments and their corresponding current waveforms are shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(I_{ps}\) & \(t_{ps}\) & \(I_{bs}\) & \(t_{bs}\) & \(n\) & \(I_{pure}\) & \(t_{pw}\) & \(I_{bw}\) & \(t_{bw}\) & \(m\) & \begin{tabular}{c} **Average** \\ **Current** \\ **(A)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Voltage** \\ **(V)** \\ \end{tabular} &
\begin{tabular}{c} **Input** \\ **(P)** \\ \end{tabular} \\ \hline
210 & 2 & 77 & 6 & 16 & 210 & 2 & 50 & 13 & 9 & 90 & 22 & 138,600 \\
210 & 2 & 77 & 6 & 16 & 210 & 2 & 50 & 22 & 9 & 80 & 22 & 123,200 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of DP-MIG test data of low-energy pulse group at different frequencies.  
Figure 17: DP-MIGW at different frequencies: (**a**) Average current of 90 A, frequency of 3.8 Hz; (**b**) Average current of 80 A, frequency of 3 Hz.  
This approach reduces the input energy by approximately 12%. The average current and frequency decrease as the base time of the low-energy pulse train increases, and the fish scales formed at the welded joint become denser.
# Reviewing the Potential of Sentinel-2 in Assessing the Drought
## 3 Progress in Remote Sensing of Drought from a Climatological and Ecological Perspective
### Remote Sensing of Vegetation in Drought
#### 3.1.1 Leaf Area Index  
The leaf area index (LAI) is a major biophysical variable used to assess the vegetation response to drought [76]. The higher spatial and revisiting characteristics of Sentinel-2 ensure data continuity in analyzing the LAI. [PERSON] et al. [77] compared the efficiency of the Sentinel-2A red-edge band with Landsat-8 OLI data in estimating the forest canopy cover, effective canopy cover, and LAI. Lidar and field data were also assessed to derive the forest canopy cover, effective canopy cover, and LAI, and generalized additive models were applied to relate with spectral indices. The research concluded that compared to Landsat-8, Sentinel-2 red-edge band-derived results had marginally better accuracy. However, a negligible difference was registered while computing conventional vegetation indices. Similarly, Sentinel-2 and Landsat-8 were compared for estimating the LAI in the temperate, deciduous broadleaf forests. Field data were collected and linear and multi-linear regression models were applied to establish the statistical relation of the derived information. The comparative analysis concluded that except for a marginal difference, the Sentinel-2 (RMSE 0.879) is not superlative to the Landsat-8 OLI (RMSE 0.877) in estimating the LAI of the European broadleaf forest. However, the Sentinel-2 provides better data continuity compared to the Landsat system [78]. Sentinel-2 has been assessed in relation to the estimation of canopy biophysical properties by correlating the PARS model-derived bidirectional reflectance factors along with Sentinel-2 over 962 reference plots. Research highlights a higher correlation in simulated Sentinel-2 bidirectional reflectance factors and the fraction of absorbed photosynthetically active radiation, moreover, band 8 and 4 combinations are highly effective in leaf area estimation [79]. The accuracy of the HLS data was evaluated in crop biomass estimation. LAI derived from MSI and OLI sensors were coupled with a simple soil-water balance model (SAFYE) for estimating the biomass estimation. The derived LAI was calibrated with the measured field LAI. Sentinel-2 derived red-edge band-based LAI exhibits a higher level of accuracy than the near-infrared band-based Landsat data in leaf dry biomass estimation [80]. The potential of Sentinel-2 was assessed in estimating the heatwaves in vinicultural regions of Australia. The nearest neighbor algorithm was applied to downscale the world view to the Sentinel scale and calculated the NDVI. The [PERSON]'s correlation and Bland-Altman concordance tests were conducted. The red-edge and enhanced vegetation index registered a positive correlation and found reliable vegetation indices to evaluate the heatwave condition on crops [81].
## 6 Numerical and Simulation Results
### Performance of the HSTMAC Protocol without Backoff Scheme  
As shown in Figure 10a, the average success probability of M2M traffic is always decreasing with the arrival rate of H2H packets. On the other hand, shown in Figure 10c, the average resource utilization ratio is increasing with the arrival rate of H2H packets. That is because the number of reserved RBs for M2M traffic is decreasing and the reserved resources are insufficient to satisfy the QoS requirement of M2M traffic. Under such a condition, according to the equilibrium analysis in Section 5, the resource utilization ratio is increasing with the system load.  
Figure 8: Comparison of resource utilization ratio between equilibrium analysis and iterative analytical results (\(W_{\text{BO}}=0\)): (**a**) S-ALOHA protocol; (**b**) TDMA protocol.  
Figure 9: Comparison of request number between equilibrium analysis and iterative analytical results (\(W_{\text{BO}}=0\)): (**a**) S-ALOHA protocol; (**b**) TDMA protocol.  
As the number of M2M devices is increasing from 10,000 to 50,000, there is a growth in the system load, which results in a higher resource utilization ratio. The reserved RBs are insufficient to satisfy the QoS requirement of M2M traffic. Hence, as shown in Figure 10a,b, there is a remarkable decrease in the average success probability and there is a remarkable increase in the average request number.  
Apparently, Figure 10 shows that the average success probability and resource utilization ratio of the proposed HSTMAC protocol are always higher than that of S-ALOHA and TDMA protocols while the average request number is always lower than that of S-ALOHA and TDMA protocols. The reason is that S-ALOHA is appropriate for small size packet transmission while TDMA is appropriate for large size packet transmission. In order to satisfy the QoS requirement of H2H traffic, the number of reserved RBs for H2H traffic in S-ALOHA protocol is much higher than that in TDMA protocol. As shown in Figure 10, for a high H2H traffic load in the S-ALOHA protocol, the QoS cannot be satisfied even if H2H traffic occupies all the RBs. Therefore, comparing the HSTMAC protocol to the TDMA protocol, the number of reserved RBs for M2M traffic are the same. However, comparing the  
Figure 10: Performance comparison of the proposed HSTMAC protocol with pure S-ALOHA protocol and pure TDMA protocol (Q = 10, \(W_{\text{BO}}=0\)): (**a**) Average success probability of M2M traffic; (**b**) Average request number of M2M traffic; (**c**) Resource utilization ratio.  
HSTMAC protocol to the S-ALOHA protocol, the number of reserved resource blocks for M2M traffic is much higher. As a result, the proposed HSTMAC protocol is superior to the pure TDMA protocol and pure S-ALOHA protocol especially in a high system load scenario.
## 1 Introduction
In [24], the possibility of SBAS single-frequency multiple-constellation positioning using raw GNSS data acquired with a smartphone was investigated obtaining a horizontal RMS of 1.30 m and a vertical RMS of 2.06 m. The use of smartphones in GNSS/SBAS positioning could have interesting results using DFMC (dual-frequency multi-constellation SBAS). It is envisaged that DFMC will be able to support SBAS positioning with four GNSS systems by 2025 [25; 26; 27]. The motivation for this article is the dynamic development of applications related to the use of raw GNSS data acquired with smartphones. Given the limitations on the amount and type of data that can be processed by smartphones, we decided to see how modifications to the basic GPS/EGNOS algorithm could possibly improve autonomous positioning. The aim of this paper is to investigate the feasibility of a modified satellite positioning algorithm using a smartphone and data associated with GPS and EGNOS. During the calculations, the effects of carrier-phase smoothed code observations, correction components determined using EGNOS, and modifying the observation weighting model were analysed. The remainder of the article presents the research methodology and related issues, followed by the results of the fieldwork and conclusions.