esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Calcite Percentage Results
#### 3.3.1. Calcite Percentage to Unconfined Compressive Strength Ratio  
The highest calcite content (2.72%) was obtained in the S1 soil treated via the injection method; however, the samples subjected to both SA and SA + MICP treatments failed to maintain structural integrity after curing, making UCS testing unfeasible. This finding clearly demonstrates that the amount of calcite alone is not sufficient to enhance mechanical performance. Instead, it highlights the critical importance of parameters such as the micro-scale distribution of calcite, pore structure, and the formation of binding bridges [41]. In contrast, when the same soil type was treated using the mixing method, calcite contents ranging from 1.39% to 5.12% were obtained, indicating a positive correlation with UCS values of up to 398 kPa. Similarly, in the S2 soil, the calcite contents obtained via the injection method ranged between 1.36% to 1.58%, indicating UCS values reaching as high as 121 kPa. On the other hand, with the mixing method, calcite contents varied from 0.91% and 4.51%, indicating UCS values up to 247 kPa. For the S3 soil, both methods resulted in relatively lower calcite contents and UCS values. However, a more pronounced trend of strength improvement with increasing calcite content was observed in the mixing method. These findings indicate that the mixing method enables a more homogeneous distribution of additives within the soil matrix, thereby allowing the precipitated calcite to form more effective binding structures.
## 4 Results and Analysis
### Moisture Ratio Prediction Experimental Results  
In order to evaluate the segmentation model's performance, this study compares manually annotated images of real tangerine peel with the VGG-UNet segmentation outputs to obtain an accurate depiction of the morphological evolution. For clarity, segmentation results from six representative images are displayed in Figure 17. Overall, the VGG-UNet segmentation model exhibited a high degree of consistency between the multi-target segmentation results and the corresponding manually annotated images.  
Figure 16: KAN-BiLSTM test data comparison and error distribution map. (**a**) No regional data, (**b**) contains regional data.  
Figure 17: VGG-UNet model segmentation visualization results.  
Following semantic segmentation to extract the tangerine peel area, the moisture ratio is predicted to yield a real-time estimation of moisture content. The actual moisture ratio is then determined from the measured moisture loss at various drying stages. To assess the prediction model's performance, the predicted moisture values were compared with the measured values. Relevant data are presented in Figure 17 and Table 10.  
Analysis of Figure 18 reveals that the predicted and measured moisture ratio curves are closely fitted, with a determination coefficient (R\({}^{2}\)) of 0.998. This high R\({}^{2}\) value indicates that the model reliably captures the overall drying process of tangerine peel, providing a robust reference for moisture ratio prediction. Furthermore, as illustrated in Table 10, the error in determining the moisture ratio during the heat pump drying process is below 1%. Importantly, regardless of whether the predicted moisture ratio is slightly overestimated or underestimated, the relative error remains within 4%, thereby meeting practical production requirements.
## 3 Data
### OpenLandMap Data  
The OpenLandMap Dataset is a global gridded dataset, providing soil information with a spatial resolution of 250 m for six different soil depths (0, 10, 30, 60, 100 and 200 cm), based on 350,000 observations from United States Department of Agriculture (USDA) database in combination with ancillary remote sensing data using a machine learning approach [52]. In this study, information about the percentual sand and clay content is used for the inversion of soil moisture to dielectric constant and vice versa, using the formula developed by [PERSON] [53]. Furthermore, field capacity (volumetric %) at 33 kPa for the 0 cm soil depth is used, for serving as an initial soil moisture starting point. For a detailed overview, the soil properties for different soil depths are listed in Tables 1 and 2 for both Rur and Apulian test site. As the proposed soil moisture retrieval approach is implemented within the Google Earth engine, the OpenLandMap dataset already available within it offers a good compromise between high spatial resolution and global coverage. However, this choice does not mean that regional or local soil datasets with higher resolution or accuracy may not also be suitable.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & **Soil Depth [m]** & **Clay [\%]** & **Sand [\%]** & **SOC [g/kg]** & **Bulk Density [kg/m\({}^{3}\)]** \\ \cline{3-6}  & & & 0 & 24.5 & 18.7 & 23 & 1276.3 \\ \cline{3-6}  & & & 0.10 & 24.6 & 18.4 & 20 & 1280.0 \\ \cline{3-6}  & & & 0.30 & 26.3 & 18.8 & 10 & 1425.9 \\ \cline{3-6}  & & & 0.60 & 28.9 & 18.3 & 5 & 1490.7 \\ \cline{3-6}  & & & 0 & 22.8 & 21.1 & 15 & 1312.2 \\ \cline{3-6}  & & & 0.10 & 22.9 & 21.2 & 13 & 1323.4 \\ \cline{3-6}  & & & 0.30 & 25.6 & 21.3 & 7 & 1420.5 \\ \cline{3-6}  & & & 0.60 & 27.7 & 20.9 & 2 & 1482.2 \\ \cline{3-6}  & & & 0 & 15.9 & 23.6 & 20 & 1306.1 \\ \cline{3-6}  & & & 0.10 & 16.2 & 23.6 & 15 & 1349.3 \\ \cline{3-6}  & & & 0.30 & 17.4 & 23.4 & 5 & 1453.2 \\ \cline{3-6}  & & & 0.60 & 18.2 & 24.1 & 4 & 1494.3 \\ \cline{3-6}  & & & 0 & 17.1 & 20.2 & 11 & 1315.7 \\ \cline{3-6}  & & & 0.10 & 17.1 & 20.2 & 12 & 1321.9 \\ \cline{3-6}  & & & 0.30 & 19.2 & 20.8 & 7 & 1458.2 \\ \cline{3-6}  & & & 0.60 & 20.6 & 20.9 & 0 & 1497.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of CRNP stations.
###### Abstract  
This study aimed to examine the influence of training load, performance, sleep, and menstrual parameters on heart rate variability (HRV) and to evaluate its potential as a predictor of sports performance. A four-year longitudinal case study was conducted on a female elite kayak athlete, involving daily monitoring of HRV, sleep quality and duration, menstrual cycles, illnesses, and acute training loads. Over this period, 1394 measurements were taken each morning immediately after waking up and before getting up. The results of four competitive seasons were analyzed using a performance index and were statistically processed with a linear mixed model. The analysis revealed a statistically significant positive association between rMSSD and both sleep quality (\(p<0.001\)) and the follicular phase of the menstrual cycle (\(p=0.003\)). In contrast, the training load (\(p=0.94\)), sleep duration (\(p=0.27\)), and illness (\(p>0.05\)) showed no statistically significant effect on rMSSD. Additionally, neither rMSSD (\(p=0.82\)) nor its trend (\(p=0.70\)) were significant predictors of the performance index. Despite the lack of a statistically significant correlation between HRV and sports performance, the findings suggest that the pre-competition decrease in HRV observed in this case study may reflect anticipatory physiological changes, potentially linked to increased sympathetic activation, as suggested in the existing literature.  
heart rate variability; sports performance; longitudinal study; elite athlete; training load; sleep quality; menstrual cycle; physiological monitoring Article
# On the Rollout of Network Slicing in Carrier Networks: A Technology Radar
## 2 Network Slicing: Concept, System Architecture and Deployment  
This section provides a technical background of network slicing, outlining the main artifacts involved in their realization.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 5 Application to Wildlife Tracking  
There are many possible applications for devices that use this algorithm. As only very short snapshots of GPS data are required, a device only needs to operate for 4 ms to acquire the data necessary for a fix. This dramatically reduces the power required (from at least 30 s of operation for a traditional GPS fix), by a factor of approximately 10,000.  
A disadvantage of tracking with this technique is that any tracking device has to be recovered in order for the position to be estimated by post-processing the stored data. This technique is suitable for tracking species that return to a known location so that the data can be recovered. Here, we describe how this was done on nesting albatross  
Figure 6: Histogram of horizontal fix errors (in meters). The median fix error is 27.7 m. The vertical axis represents the number of fixes that fall within the corresponding range of positioning error.
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 2 Literature Review
### Monte Carlo Simulation  
Monte Carlo (MC) simulation relies on repeated random sampling and statistical analysis to compute results [79; 80]. It is closely related to random experiments, i.e., experiments for which the specific result is not known in advance [81]. These models typically depend on a number of input parameters, which result in one or more outputs. Mathematical models are used in natural sciences, social sciences, and engineering disciplines to describe interactions in a system of mathematical expressions [82; 83].  
In MC simulation, each problem is representative of a broad class of similar problems--for a detailed discussion, refer to [PERSON] [84]. The technique has been applied in fields such as financial analysis, reliability analysis, Six Sigma, and mathematical and statistical physics. [PERSON] et al. [85] applied MC simulation to develop a predictive model for flyrock estimation based on multiple regression analyses. In the present research, MC was used to estimate expected revenue from shippers and consignees through integration with a data mining tool to generate input data before the simulation.  
The literature review shows that each tool can deal with the specific problems of several fields and industries. However, this study aims to close these gaps by integrating DA techniques, association rules, clustering techniques, and decision tree for new business development decisions in the logistics business. Therefore, this study implements DA to develop a recommendation approach for matching suitable consignees/shippers using a case study of courier service business. The MCSRS integrates association rules, clustering techniques, and decision tree learning with RFM analysis to better understand the customer behavior. The specific sets of questions are listed in Table 1.
## 3 Results
### Correlations  
The productivity metrics SIF\({}_{\rm i}\), SIF\({}_{\rm d}\), GPP, NDVI, and EVI are all strongly positively correlated (Pearson correlations, \(r\) values between 0.84 and 1.00, \(p\)! 0.001, Table 2). Correlations between SIF\({}_{\rm i}\) and GPP (\(r\)-value = 0.86, \(p\)! 0.001, Table 2) as well as SIF\({}_{\rm d}\), and GPP (\(r\)-value = 0.86, \(p\)! 0.001, Table 2), NDVI and GPP (\(r\)-value = 0.84, \(p\)! 0.001, Table 2), and the EVI-GPP correlation (\(r\)-value = 0.88, \(p\)! 0.001, Table 2) were all very similar. Besides the correlation between them, SIF\({}_{\rm i}\) and SIF\({}_{\rm d}\) had the strongest correlation with EVI (\(r\)-values = 0.89, \(p\)-value! 0.001, Table 2). SIF\({}_{\rm y}\) correlations with the other productivity metrics were positive and significant, but they were the lowest correlations of the SIF metrics, and the relationship between SIF\({}_{\rm y}\) and other metrics was very low (\(r\)-values between 0.43 and 0.67, \(p\)! 0.01, Table 2). Correlations between LAI and both SIF\({}_{\rm i}\) and SIF\({}_{\rm d}\) were strongly positive (\(r\)-value = 0.76, \(p\)-value! 0.001, Table 2), and fPAR was slightly more strongly correlated to SIF\({}_{\rm i}\) and SIF\({}_{\rm d}\) (\(r\)-value = 0.82, \(p\)-value! 0.001, Table 2). LAI and fPAR were only moderately positively correlated with SIF\({}_{\rm y}\) (\(r\)-value = 0.42, \(p\)-value! 0.01, Table 2). LAI was most strongly correlated with NDVI (\(r\)-value = 0.87, \(p\)-value! 0.001, Table 2), and fPAR was most strongly correlated with EVI (\(r\)-values = 0.88, \(p\)-value! 0.001, Table 2).  
Relationships among environmental variables were positive, with \(p\)-values! 0.001 (Table 3). T\({}_{\rm can}\) and T\({}_{\rm air}\) have the strongest relationship (\(r\)-value = 1.00). T\({}_{\rm can}\) and T\({}_{\rm air}\) had a strong positive relationship with VPD, with an \(r\)-value of 0.88 (Table 3). Relationships between environmental variables from the two different datasets (ECWMF data and MODIS) had weaker relationships. LST\(\sim\)VPD was 0.64, and LST\(\sim\)T\({}_{\rm can}\) and T\({}_{\rm air}\) were 0.76 (Table 3).
## 2 Materials and Methods
### Sensitive Wavelengths Selection Method
#### 2.2.2 Global Sensitivity Analysis Method--SOBOL  
Sobol is a global sensitivity analysis method based on variance decomposition, which quantitatively evaluates the influence of each input parameter and the interaction between the parameters on the output variable by decomposing the variance of the output variable [22]. If \(y=f(X_{1},X_{2},\cdots,X_{m})\) represents the model structure, \(X_{1},X_{2},\cdots,X_{m}\) represent the model parameters, and m represents the number of model parameters, the variance decomposition formula can be expressed as:  
\[V(y)=\sum_{i=1}^{m}V_{i}+\sum_{i<j<m}^{m}V_{ij}+\sum_{i<j<k<m}^{m}V_{ijk} \cdots+V_{1,2,\cdots,m} \tag{5}\]  
where: \(V(y)\) is the total variance of the model output \(y\); \(V_{i}\) is the variance produced by the parameter \(X_{i}\); \(V_{ij}\) is the variance produced by the interaction of parameters \(X_{i}\) and \(X_{j}\); \(V_{ijk}\) is the variance produced by the interaction of parameters \(X_{i}\), \(X_{j}\) and \(X_{k}\); \(V_{1,\,2,\,\ldots,\,m}\) is the variance produced by the combined action of m parameters.  
For parameter \(X_{i}\), the first-order sensitivity index \(S_{i}\) can be used to express the direct contribution rate of parameter \(X_{i}\) to the total variance of the model simulation results.  
\[S_{i}=\frac{V_{i}}{V(y)} \tag{6}\]
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 2 Method
### Performance Evaluation of Displacement Prediction Model  
In order to analyze the prediction ability of displacement monitoring data model, the evaluation index is used to analyze the displacement prediction performance, including root mean square error (RMSE), the mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R\({}^{2}\)).
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 3 Optimizing the volume of the residual set
### Dynamic Execution Phase
#### 4.2.3 Fine-Grained Scheduling  
To effectively schedule contention-intensive transactions, we design a DRL-based executor to determine the access sequence on hot data items (i.e., operation sequence) and their corresponding thread assignment, as shown in Figure 4. The scheduling process satisfies the following constraints:  
1. **Minimizing cross-partition coordination:** Scheduling operations while respecting inter-transaction and intra-transaction dependency constraints to reduce synchronization overhead.
2. **Optimizing core utilization:** Ensuring proper core assignment to prevent resource underutilization and avoid excessive load concentration on specific cores.  
**Problem formulation.** Given a residual transaction set \(\mathcal{T}=\{T_{1},\ldots,T_{n}\}\) consisting of variable-length operation sequences (\(|T_{i}|\
eq|T_{j}|\)) over a data subset \(\tilde{\mathcal{D}}\subseteq\mathcal{D}\), the executor schedules these transactions across \(m\) available processing threads, \(\mathcal{C}=\{C_{1},\ldots,C_{m}\}\). We assume \(n\leq m\), as DCoS processes residual transactions concurrently within a single period across available cores. If residual transactions exceed the available cores (\(n>m\)), transactions are grouped and scheduled across multiple periods.  
Residual transaction processing follows the same mapping function as conflict-free transaction processing to ensure the serial execution of operations on the same data. Let \(\Phi:\mathcal{T}\rightarrow\mathcal{Q}=\{Q_{1},\ldots,Q_{p}\}\) represent the partitioning function that assigns transactions to \(p\) disjoint subsets. Additionally, a one-to-many mapping \(\Gamma:\mathcal{Q}\rightarrow\mathcal{C}\) allocates these partitions to thread groups. Consequently, the mapping of operations to threads is formally defined as follows:  
\[\mathcal{M}=\Phi(\Gamma):O_{ij}\rightarrow\tilde{\mathcal{C}}\subseteq \mathcal{C} \tag{4}\]  
Figure 3: Dependency patterns. (**a**) Direct dependency. (**b**) Transitive dependency.  
where \(O_{ij}\) represents an operation from transaction \(T_{i}\).  
The makespan refers to the maximum completion time across all threads, i.e., the time by which the last thread completes its assigned operations. The objective of the scheduler is to minimize the total makespan required to process \(\mathcal{T}\). Specifically, the overall makespan is given by:  
\[M_{\text{max}}=\text{max}\{M_{i}\mid i\in\{1,\dots,m\}\} \tag{5}\]  
where \(M_{i}\) denotes the makespan of the \(i\)-th thread. By optimizing the operations sequence and operations to threads, the scheduler aims to achieve balanced workload distribution while reducing contention and synchronization overhead.
## 3 Results
### Preliminary Screening for Inhibitory Activity by CSM and OLA  
Bacterial isolates (25 out of 81) exhibiting antibacterial activity against at least one pathogenic strain used as a target are reported in Table 2. The preliminary screening carried out by the CSM allowed individuating 21 strains, with most of them that were able to inhibit _P. dansclae_ subsp. _piscicida_ (20 isolates). Two isolates (_Pseudomonas_ sp. BS23 and _Microbacterium_ sp. C1S13A, both from Arctic sediment) were active against _V. angullarum_, while _A. hydrophila_ and _V. parahaemolyticus_ were inhibited by a single isolate each (_Salinibacterium_ sp. C3W3 and _Microbacterium_ sp. C1S13A from Arctic water and sediment, respectively).  
Overall, the OLA confirmed the results obtained by the CSM, even if it seldom revealed also an inhibitory activity that was not observed by the method above (Table 2). In detail: _Pseudomonas_ sp. C1S1 from Arctic sediment inhibited _A. hydrophilia_ and _A. salmonica_; _Pseudoalteromonas_ sp. W24, _Tenacibaculum_ spp. W14 and Mc99, _Bacillus_ sp. SP3 (all from Lake Faro) inhibited _P. danselae_ subsp. _piscicida_. As shown in Table 2, 10 strains were selected for further analyses due to their capability to produce inhibition halos of diameter \(\geq\) 1 mm in the overlay assay (data not shown).
## 5 Results  
### Measure Validation and Reliability  
This study designed two SEM models. Model 1 (Figure 3a) focuses on the three key sub-dimensions (digital infrastructure capability, digital analysis capability, and strategic support capability) and how they moderate the relationship with supply chain risk management and SCR. Model 2 (Figure 3b) investigates the overall moderating effect of digital capability on this relationship. These models aim to demonstrate how different aspects of digital capability significantly influence SCR, especially in an era marked by growing uncertainty and complexity.  
To assess the internal consistency and reliability of each construct, [PERSON]'s \(\alpha\), rho_A, and composite reliability were used. As shown in Table 3, all constructs had [PERSON]'s \(\alpha\) coefficients well above 0.7, and rho_A and composite reliability values were above 0.8, indicating satisfactory reliability [82]. Convergent validity was confirmed through significant factor loadings and AVE [80]. Table 4 reveals that factor loadings ranged from 0.723 to 0.901 in Model 1 and 0.634 to 0.862 in Model 2, indicating effective measurement of constructs and supporting convergent validity. Additionally, the AVE for each construct exceeded 0.5, further confirming this validity. Multicollinearity was checked using VIF values, which were all below the 3 threshold [80], confirming no multicollinearity in the study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Demographic Characteristics**} & **Frequency** & **Percentage** \\ \hline Enterprise Type & State-Owned & 100 & 40.2 \\  & Privately Owned & 149 & 59.8 \\ \hline Employees & \textless{}100 & 6 & 2.4 \\  & 100-299 & 81 & 32.5 \\  & 300-999 & 129 & 51.8 \\  & 1000-1999 & 23 & 9.2 \\  & \textgreater{}2000 & 10 & 4 \\ \hline History & \textless{}1-5 years & 13 & 5.2 \\  & 6-10 years & 168 & 67.5 \\  & 11-25 years & 59 & 23.7 \\  & 26-50 years & 8 & 3.2 \\  & \textgreater{}50 years & 1 & 0.4 \\ \hline Role in EV supply chain & Raw Material Supplier & 33 & 13.3 \\  & Component Manufacturer & 97 & 39 \\  & Vehicle Manufacturer & 19 & 7.6 \\  & Logistics Provider & 22 & 8.8 \\  & Distributor & 34 & 13.7\% \\  & Technical Service Provider & 44 & 17.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample demographic information.  
Figure 3: (**a**) Framework and testing result of Model 1; (**b**) Framework and testing result of Model 2. Note: ***\(p\) < 0.001, ***\(p\) < 0.01.  
This study evaluated discriminant validity using factor loadings, the Cornell-Larcker criterion, and HTMT values. Results showed that factor loadings were significantly higher on their respective constructs, the square root of AVE exceeded inter-construct correlations (Table 5), and all HTMT values were below 0.85. These findings confirm strong discriminant validity of the measurement model.
# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 2 Background
### Texture Features
Cluster prominence (CLP), that expresses the tailedness of the image in terms of kurtosis: \[\text{CLP}=\sum_{i=1}^{N_{g}}{(i-2\mu)^{4}}\cdot\hat{P}_{s}(i).\] (5)
3. Cluster shade (CLS), that observes the asymmetry of the image in terms of skewness: \[\text{CLS}=\sum_{i=1}^{N_{g}}{(i-2\mu)^{3}}\cdot\hat{P}_{s}(i).\] (6)
4. Contrast (CON), that corresponds to a statistical image stretching: \[\text{CON}=\sum_{j=1}^{N_{g}}{j^{2}}\cdot\hat{P}_{d}(j)^{2}.\] (7)
5. Correlation (COR), that explains the linear dependency of gray level values: \[\text{COR}=\frac{1}{2}\cdot\sum_{i=1}^{N_{g}}{(i-2\mu)^{2}}\cdot\hat{P}_{s}(i) -\frac{1}{2}\cdot\sum_{j=1}^{N_{g}}{j^{2}}\cdot\hat{P}_{d}(j).\] (8)
6. Energy (ENE), that describes the uniformity of a texture: \[\text{ENE}=\sum_{i=1}^{N_{g}}{\hat{P}_{s}(i)^{2}}\cdot\sum_{j=1}^{N_{g}}{\hat {P}_{d}(j)^{2}}.\] (9)
7. Entropy (ENT), that characterizes the degree of disorder in the image: \[\text{ENT}=-\sum_{i=1}^{N_{g}}{\hat{P}_{s}(i)}\cdot log_{2}({\hat{P}_{s}(i)}) -\sum_{j=1}^{N_{g}}{\hat{P}_{d}(j)}\cdot log_{2}({\hat{P}_{d}(j)}).\] (10)
8. Homogeneity (HOM), that represents the degree of similarity among gray tones within an image: \[\text{HOM}=\sum_{j=1}^{N_{g}}{\frac{1}{1+j^{2}}}\cdot\hat{P}_{d}(j).\] (11)
9. Variance (VAR), that defines the dispersion of shades of gray around the mean value \(\mu\): \[\text{VAR}=\frac{1}{2}\cdot\sum_{i=1}^{N_{g}}{(i-2\mu)^{2}}\cdot\hat{P}_{s}(i )+\frac{1}{2}\cdot\sum_{j=1}^{N_{g}}{j^{2}}\cdot\hat{P}_{d}(j).\] (12)