esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R\({}^{2}\) (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.  
remote sensing; random forest; digital soil mapping; support vector machine; boosted regression trees; soil total nitrogen +
Footnote †: journal: Remote Sensing, 2019, 11, 2934; doi:10.3390/rs11242934
## 3 Geophysical Survey  
### Seismic Reflection Transact  
Seismic reflection profiles collected in the 1980s by the Geological and Mining Institute of Spain [35] were analysed. A Mini-Sosie vibrator was used as the seismic source, and model SM-7 GR geophones with 30 Hz sensors were used to record the signals. An array of 18 geophones per trace was used, grouped in two lines parallel to the orientation of the profile, with 9 geophones each and with a separation between them of 1.85 m (the measurement was made by means of a double geophone in each position). The spacing between the traces was 15 m.  
At each firing point, 2100 hits were generated. To fix the shooting distance, tests were carried out at 30, 45, 60 and 75 m. The best reflections were obtained at distances of 45 m.  
The information was processed by the Compagnie Generale de Geophysique (CGG). The processing flow followed a standard sequence of processing, including collection of points in the \"slalom line\", deconvolution prior to stacking, static corrections from ground level at the referred point to mean ground level (time origin), continuous velocity analysis, dynamic velocity correction, automatic adjustment of residual static corrections (SATAN), gains, bandpass filtering (3-100 Hz), coherence improvement, migration and analogical output of the information [35].  
Figure 2: Details of the studied sector (black box in Figure 1a). The positions of fault 2, the boreholes (1 and 2), the different time-domain electromagnetic stations (1, 2, 3, 4, 5, 6) and the seismic profile with the positions of its traces are shown. Coordinates are in m.  
In this work, one of these seismic lines is used, namely, line 1 (see location in Figure 2). For the double-time/depth transformation, interval velocities of 1900 m/s were considered to the base of the Miocene unit, and an interval velocity of 2200 m/s were considered to the base of the Triassic unit. This velocity was determined in the processing of the line and was verified based on the known thicknesses of the different units according to mechanical probing performed in the area.
# Link-State Aware Hybrid Routing in the Terrestrial-Satellite Integrated Network +
## 5 Simulation and Analysis
### Simulation Setup  
As shown in Figure 6, the satellite simulations are conducted based on a realistic Hongyun constellation project, the parameters of satellite constellation are presented in Table 4. For the delay-sensitive communication session, an acceptable delay variation of ISL length over the time interval is about 3 ms. Meanwhile, according to [20] and Hongyun constellation parameters, the upper bound number of interplane ISLs is 12, then \(\Delta\tau\) is equal to 51 s by using (20). The real-flow in the network is self-similar and heavy-tail distributed, which shows flow's statistical consistency under different time scales, and the On-Off model can explain the generation process of self-similar flow and decompose the data traffic into multiple source-destination pair flows [42]. Thus, we consider 600 non-persistent On/Off flows. The On/Off periods of the connections are derived from a Pareto distribution with a shape equal to 1.2. The average burst time and the average idle time are set to 200 ms. The source and destination terminals are dispersed all over the Earth, divided into six continental regions, following a distribution identical to the traffic distribution used in [43], which taking into account the geographic distribution of sources and destinations. The source terminals send data at varying from 0.8 Mbps to 1.5 Mbps.  
To verify the routing performance when occurring network congestion, background flow and burst flow are set in the simulation. As shown in Figure 6, background flow refers to the original traffic on the ITL, i.e., the connected black line from \(g_{m}\) to \(g_{l}\) (\(\forall m,l\in[1,10]\), \(m\
eq l\) ). Burst flow represents the sudden increase of traffic rate on the passing ITL, in the experiment, we set the burst flows from \(g_{6}\) to \(g_{7}\), i.e., the connected red line, the transmission rate of each burst flow is 5 Mbps, obeying an exponential distribution with a mean 100 ms.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 5 Theoretical Framework and Methodology
### Hyperparameter Tuning and Statistical Validation  
To identify an optimal and statistically justifiable configuration for our autoencoder model, we performed a comprehensive hyperparameter grid search using a controlled and efficient training setup. Specifically, we reduced the number of training epochs to 50 and utilized only 25% of the full dataset for each of the training, validation, and test sets. The subsets of images for training, validation, and testing significantly reduce computation time while still enabling meaningful statistical analysis.  
Figure 10: Example of the threshold selection process. The autoencoder is configured with 16 hidden units. The jammer transmits noise with \(RJP=1\) and is located (**a**) 65 m, (**b**) 70 m, and (**c**) 75 m from the receiver.  
Figure 9: Training phase of autoencoders with known data samples, which follows \(P\) distribution. It is worth noting that only regular or “non-jammed” samples are fed to the autoencoder.  
We evaluated the influence of each hyperparameter on classification accuracy using both one-way and N-way ANOVA. The one-way ANOVA results confirmed that the decoder transfer function (\(p<0.0001\)), encoder function (\(p=0.0001\)), sparsity proportion (\(p=0.0499\)), and sparsity regularization (\(p=0.0039\)) had a statistically significant impact on model performance. In contrast, hidden layer size and \(L_{2}\) weight regularization did not show statistical significance (\(p>0.1\)), suggesting robustness to those parameters.  
The N-way ANOVA revealed further interaction effects, most notably between encoder and decoder functions (\(p<0.0001\)), and between sparsity and \(L_{2}\) regularization (\(p=0.0113\)), indicating the importance of tuning certain parameter combinations jointly rather than independently.  
Table 4 summarizes the full hyperparameter search space and highlights the optimal-performing configuration. This configuration achieved a classification accuracy of 0.9867, precision of 0.9740, recall of 1.0000, and an F1-score of 0.9868 using the following parameters: a logsig encoder, satlin decoder, hidden layer size of 32, sparsity proportion of 0.1, sparsity regularization of 10.0, and \(L_{2}\) weight regularization of 0.1.  
The ANOVA-tuned model benefits from statistically validated hyperparameters, improving generalizability and reducing the risk of overfitting. This demonstrates the effectiveness of principled hyperparameter tuning, even under reduced training budgets.  
Box plots shown in Figure 11 further support the ANOVA findings, highlighting substantial accuracy variation across activation and sparsity configurations while indicating minimal sensitivity to hidden layer size and \(L_{2}\) regularization.  
Figure 11: Box plots showing classification accuracy across hyperparameter values. The variability in decoder function, encoder function, sparsity proportion, and sparsity regularization is statistically significant and directly affects model performance.
## 2 Figure of Performance for Analysis
### Uere  
In Equation (4), position and clock bias estimation error are calculated using GDOP and URE, which means pseudo-range error. In fact, the URE is composed of the Signal-In-Space User Range Error (SISURE), which includes errors related to the space and control segment (i.e., satellite orbit and clock errors and group delay error), and the User Equipment Error (UEE), which comprises the remaining contributions specific to the receiver and environment (i.e., atmospheric error, multipath, and receiver noise) [23; 25]. Note that the dominant component of URE in the low \(C/N_{0}\) environment of SSV is the receiver noise. Therefore, for simplicity, we focus on the range equivalent receiver noise in the URE analysis of this study. Later, we take into account the SISURE together in the URE.  
The range equivalent receiver noise, which is used to estimate the position error, is expressed by the standard deviation of time error, \(\Delta\tau\), of the code delay-lock-loops (DLL) assuming a zero mean white Gaussian noise such as the following:  
\[\sigma_{\Delta\tau}=\sqrt{E\{\Delta\tau^{2}\}}\approx\ \frac{T_{\rm C}\sqrt{ \mathrm{var}\{N_{E}-N_{L}\}}}{2\sqrt{C}}=cT_{c}\sqrt{\frac{d}{4 TC/N_{0}}}\ meters \tag{10}\]  
where \(T_{\rm C}\) is chip width, \(N_{E}\) and \(N_{L}\) are noises at early and late, respectively, \(T\) is average time, \(d\) is correlator spacing, and \(c\) is speed of light. \(\sigma_{\Delta\tau}\) can be expressed in meters, using speed of light, and this error corresponding to user range error. In addition, the above user range error can be modified as follows, considering the bandwidth of the loop filter:  
\[\sigma_{\Delta\tau}=cT_{c}\sqrt{\frac{dB_{\tau,1}}{2(C/N_{0})}}\ meters \tag{11}\]  
where \(B_{\tau,1}\) is loop filter bandwidth [23].
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 1 Introduction
These issues include stringent theoretical conditions and an inability to effectively represent the rank function, which can lead to suboptimal results. Currently, significant progress has been made in low-rank matrix recovery based on nonconvex regularization terms. For example, [PERSON] et al. proposed a method for minimizing matrix rank with a double nonconvex regularization term [28], and [PERSON] et al. demonstrated the effectiveness of nonconvex regularization in matrix recovery problems [29]. Since tensors are higher-dimensional extensions of matrices, tensor recovery based on nonconvex regularization has become a key focus of current research. Numerous experiments [30; 31] have shown that nonconvex approximation models can approximate the tensor rank function more accurately than tensor nuclear norms. While the aforementioned low-rank tensor completion techniques have demonstrated effectiveness in resolving several problems, there is still much space for development. Inspired by nonconvex low-rank tensor completion and nonlinear transformations, nonconvex relaxations of the tensor rank function and nonlinear transformation are simultaneously considered to further enhance completion performance in numerical and visual aspects. Therefore, we propose a low-rank approximation nonconvex nonlinear transformations (LRANNT) model for tensor completion. Specifically, a strategy of nonlinear transformation is leveraged to fully exploit the underlying low-rank structure, with the use of the double L\(\gamma\) norm as a general nonconvex surrogate for tensor rank, and the convergence of the model is demonstrated. Our current primary focus is on optimization-based models, while deep learning-based models will be discussed later. A plethora of experimental results demonstrate that the performance of our model is enhanced, especially in scenarios with extremely low sampling rates. In addition, we have conducted a sensitivity analysis on the relevant parameters. In summary, the contributions of this paper can be divided into three main parts:  
* First, we propose a low-rank approximation nonconvex nonlinear transformation model, named LRANNT, which integrates the advantages of nonconvex norms and nonlinear. This model enables more accurate approximations and improves visual performance. * Second, the proposed LRANNT model is solved by the proximal alternating minimization (PAM) algorithm, and its global convergence is experimentally validated. * Finally, extensive experiments on video, color images, and magnetic resonance imaging (MRI) data have been conducted, demonstrating the competitive performance of LRANNT in terms of both numerical accuracy and visual quality. The subsequent sections of this paper are organized as follows: Section 2 presents a review of related work. Section 3 defines crucial notations and preliminaries. This is followed by the introduction of the LRANNT model in Section 4. Section 5 evaluates the performance of LRANNT, Section 6 provides discussions, and Section 7 concludes the paper.
## 3 Results and Discussion
### Specificity
#### 3.1.2 Ouzo Matrix Effect Check  
The results from the tests are described in Section 2.4.1. Based on these, two least squares curves are constructed in the same diagram, one with the \"ouzo\" matrix and the other with the pure 40% ethyl alcohol solvent. The curves are shown in Figure 4.  
Observing the two curves in Figure 4 it appears that they are parallel. However, the documentation of the specificity should be conducted statistically by the regression analysis for the two matrices and the statistical test of the equality of the slopes of the curves. The acceptance criterion is that the slope value of one curve is included in the 95% confidence interval of the slope of the other curve [15]. From the regression analysis for the two matrices in Excel, the confidence intervals (CI) of the slopes are obtained; for Ouzo matrix, slope b = 136.77:CI 95% = (129.45; 144.09), whereas for matrix ethyl alcohol, slope b = 138.17:CI 95% = (129.75; 146.59)  
From CI it can be seen that the slope value of each curve is included in the CI of the slope of the other curve. Therefore, the slopes of the curves are statistically equal for a significance level of 95%, and it is safely concluded that the impurities of the Ouzo matrix do not affect the determination of DEHP with the specific analytical method.  
Figure 1: Chromatogram, HPLC, of a DEHP standard solution of concentration 0.7 mg/L, to check the specificity of the method.  
Figure 3: Chromatogram, HPLC, of a real Ouzo sample, to check the specificity of the method.  
Figure 2: Chromatogram, HPLC, pure ethyl alcohol solvent 40%vol, to check the specificity of the method.
## 2 Results and Discussion
### Characterization of Nanomaterial
The peaks observed for the GW sample can be attributed to the presence of numerous organic compounds within the GW powder matrix. In the composite sample, peaks from both the GW and Mg NPs are evident, indicating the presence of Mg nanoparticles supported on the GW powder. However, the peaks from GW are dominant, likely due to the lower weight percentage of Mg in this composite. Based on XRD data and using the Scherrer equation, the crystallite sizes were estimated to be 21.6 nm for Mg NPs and 21.3 nm for the Mg NP/GW nanocomposite. Figure 6 presents the TGA analysis of the prepared samples. A gradual weight loss is observed for the GW sample up to 250 \({}^{\circ}\)C, attributed to the evaporation of water vapor and volatile organic compounds from the GW matrix. Between 250 \({}^{\circ}\)C and 330 \({}^{\circ}\)C, a noticeable weight loss occurs, corresponding to the decomposition of organic molecules within the GW powder [39]. This decomposition continues up to 900 \({}^{\circ}\)C, depending on the temperature required to evaporate the larger organic molecules. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **BET Surface** & **Total Volume in** & **Total Area in** & **BJH Desorption** \\  & **Area (m2/g)** & **Pores \(\leq\) (cm3/g)** & **Pores \(\geq\) (m2/g)** & **Average Pore** \\  & & & & **Width (Å)** \\ \hline GW powder & 2.7 & 0.00502 & 1.741 & 44.4 \\ Mg NPs & 42.2 & 0.165 & 69.250 & 5.1 \\ Mg NP/GW powder & 3.0 & 0.00537 & 1.885 & 44.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: BET surface area, total volume and area of pores, and average pore width for prepared samples. Figure 4: (a) N\({}_{2}\) adsorption–desorption isotherms and (b) pore size distribution of GW, Mg NPs, and Mg NP/GW powder nanocomposite. A similar trend is observed for the Mg NP/GW powder nanocomposite samples. However, the Mg sample displays a different trend, with an observable weight loss occurring up to 200 \({}^{\circ}\)C. This weight loss is likely due to the evaporation of water molecules and volatile compounds attached to the surface of the Mg nanoparticles. The transition from Mg to MgO is not observable, possibly due to the low concentration of Mg compared to the extract. Figure 5: XRD of GW, Mg NPs, and Mg NP/GW powder nanocomposite. Figure 6: TGA of GW, Mg NPs, and Mg NP/GW powder nanocomposite.
# Organizational Culture: The Key to Improving Service Management in Industry 4.0
## 4 Results
### Data Analysis
#### 4.2.1 Evaluation of Measurement Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Construct**} & \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Outer Loading**} & **Variance** & **Cronbach’s** & **Composite** & **Average** \\  & & & **Inflation** & **Factor (VIF)** & **Alpha** & **(CR)** & **Variance Extended (AVE)** \\ \hline \multirow{10}{*}{Leadership} & B02 & 0.877 & 4.386 & 0.929 & 0.943 & 0.702 \\ \cline{2-6}  & B04 & 0.869 & 4.212 & & & \\ \cline{2-6}  & B05 & 0.833 & 2.575 & & & \\ \cline{2-6}  & B07 & 0.866 & 3.304 & & & \\ \cline{2-6}  & B08 & 0.845 & 2.992 & & & \\ \cline{2-6}  & B09 & 0.819 & 2.472 & & & \\ \cline{2-6}  & B15 & 0.748 & 1.812 & & & \\ \hline \multirow{10}{*}{Organizational Culture} & C01 & 0.823 & 2.940 & 0.879 & 0.906 & 0.555 \\ \cline{2-6}  & C02 & 0.865 & 3.336 & & & \\ \cline{2-6}  & C03 & 0.828 & 2.574 & & & \\ \cline{2-6}  & C04 & 0.790 & 2.218 & & & \\ \cline{2-6}  & C05 & 0.682 & 1.998 & & & \\ \cline{2-6}  & C06 & 0.683 & 1.944 & & & \\ \cline{2-6}  & C07 & 0.796 & 2.224 & & & \\ \hline \multirow{10}{*}{Incentives} & D01 & 0.858 & 3.030 & 0.927 & 0.941 & 0.696 \\ \cline{2-6}  & D02 & 0.842 & 2.703 & & & \\ \cline{2-6}  & D04 & 0.787 & 2.334 & & & \\ \cline{2-6}  & D05 & 0.856 & 3.225 & & & \\ \cline{2-6}  & D06 & 0.850 & 2.866 & & & \\ \cline{2-6}  & D08 & 0.828 & 2.834 & & & \\ \cline{2-6}  & D09 & 0.817 & 2.971 & & & \\ \hline \multirow{10}{*}{Organizational Commitment} & G01 & 0.611 & 1.334 & 0.873 & 0.910 & 0.672 \\ \cline{2-6}  & G05 & 0.881 & 2.742 & & & \\ \cline{1-1} \cline{2-6}  & G07 & 0.897 & 3.211 & & & \\ \cline{1-1} \cline{2-6}  & G08 & 0.793 & 2.047 & & & \\ \cline{1-1} \cline{2-6}  & G09 & 0.880 & 2.890 & & & \\ \hline \multirow{10}{*}{Service Management} & F01 & 0.875 & 3.268 & 0.888 & 0.916 & 0.646 \\ \cline{1-1} \cline{2-6}  & F02 & 0.738 & 2.062 & & & \\ \cline{1-1} \cline{2-6}  & F05 & 0.873 & 3.055 & & & \\ \cline{1-1} \cline{2-6}  & F06 & 0.853 & 2.743 & & & \\ \cline{1-1} \cline{2-6}  & F07 & 0.689 & 1.605 & & & \\ \cline{1-1} \cline{2-6}  & F08 & 0.777 & 1.867 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Composite reliability (CR) and average variance extracted (AVE) of the items. by [PERSON] and [PERSON] [104], indicating that the model has good discriminant validity and convergent validity. This study analyzed the degree of correlation between the factors and variables in terms of cross-loading. In this study, the factor loadings of the individual factors between the constructs were greater than 0.50, which was statistically significant. In the structural model, the standardized path coefficients were statistically significant, and the self-loadings of each component were larger than the cross-loadings, indicating good convergent validity and discriminant validity of this measure, as shown in Table 3.
## 2 ITS Architecture and Applications
### ITS Applications
#### 2.3.1 Road Safety Applications
\begin{table}
\begin{tabular}{l l l l l l l l} \hline Applications & Use Cases & Communication Modes & Radio Coverage & TX Frequency & Critical \\  & & & & & & & Latency \\ \hline Active road & Intersection collision warning, & Broadcasting &, Cooperative & From 300m to & 10 Hz & \(\leq 100\)ms \\ safety & & Lane change assistance, etc. & messaging, etc. & 20 Km & & & \\ \hline Traffic Efficiency & Regulatory speed limit notification & Periodic / permanent message & From 300m to 5 Km & 1-10 Hz & - \\ and Management & Green light optimal speed advisory & & & & & 10 Hz & \(\leq 100\)ms \\ \hline Cooperative & Electronic toll collection & Internet vehicle and unicast full & From 0m to 1 Km & 1 Hz & \(\leq 200 ms\) \\ Navigation & & Adaptive cruise control, Vehicle & Cooperation awareness & & & 2 Hz & \(\leq 100 ms\) \\  & highway automatic system & & & & & \\ \hline Global Internet & Insurance and financial services, & Access to Internet & From 0m to full & 1 Hz & \(\leq 500 ms\) \\ Services & & Fleet management, etc. & & & range & & \\ \hline Cooperative & Point of interest notifications & Periodic / permanent message & From 0m to full & 1 Hz & \(\leq 500 ms\) \\ Local & & broadcast & range & & & \\ \cline{2-6} Services & Electronic commerce & Full duplex communications & & & & \\ \cline{2-6}  & Media downloading & Access to Internet & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Typical ITS Applications vs. Performance and System Requirements