esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis
### Kruskal-Wallis Test on Apriori Values  
The Kruskal-Wallis test was chosen in this study because it is the most appropriate statistical method for comparing three or more independent groups when the assumption of normality is violated, as was the case with the Apriori algorithm's execution time data at certain support thresholds.  
The Kruskal-Wallis test is a non-parametric alternative to one-way ANOVA used to determine if three or more independent groups originate from the same distribution. It is especially useful when the assumptions of an ANOVA (such as normality or equal variances) are not met. The purpose of the test is whether the median (not the mean) values of multiple independent groups are significantly different.  
Instead of using raw data, the Kruskal-Wallis test works on ranks:  
* Combine all values from all groups and rank them from lowest to highest.
* Calculate the sum of ranks for each group.
* Compare rank sums to test if they differ significantly.  
\[H=\frac{12}{N(N+1)}\sum_{i=1}^{k}\frac{R_{i}^{2}}{n_{i}}-3(N+1)\]  
where  
* N = total number of observations
* K = number of groups
* R\({}_{i}\) = sum of ranks for group i
* n\({}_{i}\) = size of group i  
The result is compared to a chi-square distribution with k \(-\) 1 degrees of freedom.  
Given the non-normal distribution of Apriori results at higher support levels, the Kruskal-Wallis test was employed. The test showed a statistically significant difference across support groups (\(p\) < 0.001), confirming that Apriori's performance is highly sensitive to the support parameter.  
Table 2 and Figure 3 reflects the Apriori output variation under increasing support thresholds. Clear downward trends in both median and interquartile range support the Kruskal-Wallis findings.  
Figure 3: Kruskal–Wallis distribution of Apriori performance across support levels (The black symbol represents the median line inside the box of a boxplot).  
This suggests that as support increases, Apriori becomes less capable of identifying frequent itemsets, resulting in lower output values and efficiency.
## 5 Discussion  
In applications that require high positioning accuracy, if the errors of the GNSS positioning system are not promptly warned or corrected, serious consequences may occur. The purpose of studying GNSS positioning error estimation methods is to evaluate and correct errors, ensure that GNSS can still provide high-quality positioning services in complex environments, and avoid large deviations in positioning results. As can be seen from Table 3, the proposed method achieves a lower RMSE value than the traditional method. This is because the traditional GNSS positioning error estimation method has the following shortcomings:  
1. Traditional positioning error estimation methods rely on precise assumptions, and have difficulty accurately reflecting the error size in complex environments. Therefore, the estimation accuracy is usually low.
2. There are large differences in orbital errors, clock biases, and other factors between different satellite systems when multiple GNSS systems are used together. Traditional GNSS positioning error estimation methods have difficulty compensating for errors between multiple systems at the same time, and are prone to cause accumulation and cross-interference of errors between multiple systems.  
However, the GNSS positioning error estimation method based on deep learning has powerful nonlinear modeling capabilities. It can learn the trend of error changes from historical data to effectively handle complex error factors, and has the potential to further improve accuracy with the support of large-scale data. Therefore, a new multi-layer perceptron model is proposed for GNSS positioning error estimation. Multi-head attention mechanisms and gating operations are designed to improve the standard MLP model. The improved MLP model can better capture the relationship between input features by combining multi-head attention mechanism and gating mechanism. It can be seen from the two types of comparative experiments that the proposed model not only has better performance than other commonly used methods, but also has obvious advantages over the current state-of-the-art methods.  
Our study aims to demonstrate the potential of the presented method in dealing with real-time position error estimation problems. However, in actual applications, factors such as data transmission delay, data reliability, and application performance requirements may need to be considered. In the face of building occlusion in urban environments and signal interference in natural environments, additional measures need to be taken to deal with data uncertainty to improve the stability and reliability of position estimation.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
## 5 Results and Discussion
### Scenario 4: Time Complexity and Statistical Undetectability Rate  
In this experiment, we assessed the proposed IoT audio steganography based on the time complexity and undetectability rate. Our datasets consist of 500 training and 500 testing audio samples (speech and music) with a frequency of 44.1 kHz and quantized at 16 bits. The duration of each signal is 10 s. All covers were embedded with random messages using the proposed IoT steganography algorithm. We employed the SVM library tool [21] with radial basis function (RBF) kernels [55]. We used two successful audio steganalysis methods: the first is 2D-Mel (second-order derivative-based Mel-cepstrum) [56], which is an efficient method, and the second is EE-AS (Energy-Entropy Audio Steganalysis) [20] which proved to increase the detection rate of steganography work. The accuracy of our predictions was measured by F-measure and the receiver operating characteristic (ROC).  
In the absence of a practical implementation of the IoT audio steganography technique, we compared our method with the well-known audio steganography tools Steghide and S-tools in terms of undetectability. In Table 1, we record the overall accuracy, where higher score values are interpreted as a high detection rate. We registered 59.3% compared to 73.2% in Steghide and 81.7% in S-tools (average score between EE-AS and 2D-Mel). These records and ROC curves in Figure 11 demonstrate that we were able to achieve a higher level of undetectability scores.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hiding Methods** & **Steganalysis Method** & **F-Measure** & **ROC** \\ \hline \multirow{2}{*}{S-Tools} & 2D-Mel & 0.706 & 0.725 \\ \cline{2-4}  & EE-AS & 0.909 & 0.91 \\ \hline \multirow{2}{*}{Steghide} & 2D-Mel & 0.63 & 0.67 \\ \cline{2-4}  & EE-AS & 0.791 & 795 \\ \hline \multirow{2}{*}{Proposed} & 2D-Mel & 0.564 & 0.569 \\ \cline{2-4}  & EE-AS & 0.591 & 0.593 \\ \hline \hline \end{tabular}
\end{table}
Table 1: F-measure and ROC result.  
Figure 10: Payload capacity and SNR responses to different sampling frequencies.  
Our scheme has also low time complexity since it is based on FFT. For N samples, FFT takes a maximum time complexity of \(O(N\log N)\)[51]. In addition to the complexity time, the computational time needed is also small considering we only use audio frames of 4 ms, which require a small FFT size.
###### Abstract  
The deformation behavior of a dam can comprehensively reflect its structural state. By comparing the actual response with model predictions, dam deformation prediction models can detect anomalies for effective advance warning. Most existing dam deformation prediction models are implemented within a single-step prediction framework; the single-time-step output of these models cannot represent the variation trend in the dam deformation, which may contain important information on dam evolution during the prediction period. Compared with the single value prediction, predicting the tendency of dam deformation in the short term can better interpret the dam's structural health status. Aiming to capture the short-term variation trends of dam deformation, a multi-step displacement prediction model of concrete dams is proposed by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, the k-harmonic means (KHM) algorithm, and the error minimized extreme learning machine (EM-ELM) algorithm. The model can be divided into three stages: (1) The CEEMDAN algorithm is adopted to decompose dam displacement series into different signals according to their timing characteristics. Moreover, the sample entropy (SE) method is used to remove the noise contained in the decomposed signals. (2) The KHM clustering algorithm is employed to cluster the denoised data with similar characteristics. Furthermore, the sparrow search algorithm (SSA) is utilized to optimize the KHM algorithm to avoid the local optimal problem. (3) A multi-step prediction model to capture the short-term variation of dam displacement is established based on the clustered data. Engineering examples show that the model has good prediction performance and strong robustness, demonstrating the feasibility of applying the proposed model to the multi-step forecasting of dam displacement.  
dam deformation; multi-step prediction; concrete dams; complete ensemble empirical mode decomposition with adaptive noise; K-harmonic means; sparrow search algorithm Article
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### 3 GPP Constraints  
The CMS framework incorporates 3 GPP-specific procedural constraints within its scheduling engine, ensuring compliance with standardized network operations. From the perspective of infrastructure and architecture challenges, this capability facilitates the deployment of distributed core functions on satellites. For instance, authentication procedures, which require special considerations in discontinuous connectivity scenarios, can be effectively managed within this framework. Regarding integrated management challenges, modeling 3 GPP handover procedures within the scheduling engine enhances mobility management by improving coordination between network and satellite operations. Additionally, this approach integrates network operations into the traditionally task-oriented paradigm of satellite scheduling, enabling seamless orchestration of 3 GPP procedures as scheduled tasks. Finally, incorporating 3 GPP constraints into the schedulingframework in addressing operational dynamics challenges supports dynamic space routing. The framework enhances decision making in dynamic and rapidly changing network topology environments by leveraging handover information and related network data.
## 3 Results and Analysis  
### Volume Fraction and Degree of Crimp  
Table 1 shows the average value and the standard deviation of the fiber volume fraction of the laminates. Since the manufacturing parameters used were the same for [0\({}^{\circ}\)/90\({}^{\circ}\)]s, [90\({}^{\circ}\)]u, and [+45\({}^{\circ}\)/\(-\)45\({}^{\circ}\)]s laminates, they were grouped as one set of data to obtain the average volume fraction, and the result shares similarity with the one reported in the previous work [22]. Unidirectional [0\({}^{\circ}\)]s laminate shows a higher volume fraction due to the higher pressure applied during the manufacturing stage; however, a higher standard deviation is observed for this case. On the other hand, due to the particular woven pattern of the basket weave reinforced laminate, it displays two volume fractions, in which the warp direction shows a higher volume fraction than the weft; overall, the fiber volume fraction of basket weave laminate is around 0.615, which is also similar to the one reported by [PERSON] et al. [21].  
Furthermore, fiber crimp and maximum waviness angle were determined for the basket weave reinforced laminates (micrographs of the basket weave laminates are shown in Figure 3). Results show higher crimp along the warp direction than the weft direction, as expected, and the values are 7.16% and 3.15% for the warp and weft directions, respectively;  
Figure 2: Experimental setup for the fatigue test.  
these results are similar to the results reported by [PERSON] et al. (2018). On the other hand, the maximum angle of waviness found in this study was 19.38\({}^{\circ}\) and 12.35\({}^{\circ}\) for the warp and weft directions, respectively.
## 8 Challenges and Future Directions
### Challenges
#### 8.1.5 Standardisation  
There is a lack of a widely adopted standard for HAR data collection, communication and model representation. There are also a diverse number of devices and sensors used for HAR which use different communication protocols. This poses a challenge for integrating HAR systems with the existing infrastructure.  
Due to the absence of a standard, the HAR landscape is fragmented, and there are inconsistent practices. There is no standard to how activities can be defined, and this can lead to inconsistencies in data labelling and annotation. There is also no set protocol for data collection. This lack of standardisation needs to be addressed for interoperability and adoption of HAR systems.
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 3 Results
### SRF Gasification Trials
#### 3.1.3 Tar Composition  
The tar produced during gasification is one of the critical points of the process [47], since it can cause the formation of cracks in the pores of the filters, the production of coke that clogs the filters, and the condensation and clogging of cold spots [48]. For this reason, the composition of the tar generated must be analysed to know the extent of its impact. For the present SRF gasification process, the tar was analysed by gas chromatography (GC/MS), identifying the primary compounds shown in Table 4. It is important to emphasise that the results refer to chromatographic area percentages, i.e., these percentage data do not represent the exact composition of the samples since the area/concentration response factor is different for each compound; however, these data will be very useful for comparing tar produced in different conditions.  
Based on the molecular weight of tar compounds, some researchers have divided the composition into five groups [49; 50]. In this study, a similar classification of tar compounds was employed, including: (i) light aromatics with a single ring, such as styrene; (ii) polycyclic aromatic hydrocarbon compounds with two or three rings, including indene, naphthalene, n-methyl-naphthalene, biphenyl, biphenylene, fluorene, anthracene, and phenanthrene; (iii) heterocyclic aromatics containing nitrogen, such as n-methyl-pyridine, ben  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **1** & **2** \\ \hline Moisture of the input SRF stream to & 0 & 33.7 \\ gasification (wt\(\%\)) & & \\ \hline Family of tar compounds (\(\%\) of chromatographic area) & & \\ \hline Light aromatics with a single ring & 1.5 & 1.7 \\ Polycyclic aromatics & 87.2 & 95.4 \\ Heterocyclic aromatics containing nitrogen & 7.6 & 0.8 \\ Heterocyclic aromatics containing oxygen & 1.4 & 1.5 \\ Organic compounds containing sulfur & 1.5 & 1.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gasification tar composition.  
zonitrile, n-methyl-benzonitrile, quinoline, n-methyl-quinoline, indole, n-phenyl-pyridine, n-naphthalenecarbonitrile, benzoquinoline, and 5H-indeno [1,2-b]pyridine; (iv) heterocyclic aromatics containing oxygen, including phenol and benzofuran; and (v) organic compounds containing sulfur, specifically 2-benzothiophene and propanenitrile, 3,3\({}^{\prime}\)-thiobis-).  
As seen in Table 4, the majority of compounds belong to the polycyclic aromatic group for the two experimental conditions, with 87.2% and 95.4% of chromatographic area. This difference, related to the different S/C ratio used in each experiment, is directly reflected in the group of nitrogen-containing heterocyclic aromatics, and the percentage is lower for Test #2, with 1.5%, than for Test #1, with 7.6%. The rest of the compound classification groups remain very similar for the two trials, with maximum differences of 0.2%, which are not considered significant. This composition resembles the typical one identified for tars from biomass gasification [51].
## 7 Conclusions  
Field records and numerical analyses of the ground deformation induced by the process of shield tunneling in water-rich soft stratum were presented and discussed. With the proposed computational model, a parametric study was conducted to evaluate the influence of key construction elements of shield tunneling. The conclusions were as follows:  
1. According to data collected for Tianjin Metro Line 6, the surface settlement trough result was well fitted by a Gaussian distribution curve. The surface settlement ranges from \(-14.20\) mm to \(-28.00\) mm in Tianjin's water-rich soft stratum, which is at an acceptable level of engineering.
2. A refined 3D model addressing fluid-structure interactions is developed to consider the construction process in water-rich soft stratum, including tunnel face advance, tail void grouting, lining installation, and grout hardening. The monitoring data of Tianjin Metro Line 6 are compared with the simulation results. The minimum error between the measured values and the simulated values was detected as 4% and the maximum error was detected as 20%. The simulation findings are in good agreement with the measured data, demonstrating the rationality and application of the refined numerical simulation method proposed in this paper for shield tunneling analysis in water-rich soft stratum.
3. The impact of shield driving parameters on ground deformation is drawn from the parametric study. The friction between the shield skin and the soil is the most disadvantageous parameter for the deformation control among the three parameters studied in this paper. The increase in this parameter causes four adverse results: stratum uplift in front of the cutterhead; stratum settlement behind the shield tail; increase in horizontal displacement of the stratum along both the axial and lateral directions of the tunnel; and an increase in elliptical deformation of the segment ring. The synchronous grouting quantity is the most advantageous of the three parameters for deformation control. The increase in this parameter can reduce the settlement and the horizontal displacement along the axial direction of the tunnel while significantly reducing the elliptical deformation of the segment ring. In practical engineering, timely injection of bentonite slurry reduces friction between the shield skin and the soil, and effective synchronous grouting reduces ground loss.  
Conceptualization, X.L. and D.Z.; data curation, X.L.; funding acquisition, D.Z.; investigation, Y.H.; methodology, X.L.; project administration, D.Z.; software, X.L.; supervision, D.Z.; validation, X.L.; writing--original draft, X.L.; writing--review & editing, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Chinese National Natural Science Foundation, grant number 51738002 and National Key Research and Development Programs of China, grant number 2017 YFC0805401.  
The authors declare no conflict of interest.
# Establishing a Geological Knowledge Base for Braided River Deltas Using Google Earth
## 3 Results and Discussion
### Assessing Threshold Significance and Data Quality for Delta Morphometry  
For the analysis of the 80% threshold of the morphometric parameter \"adjacent angle\", this study supplements statistical tests and evaluates the impact of missing data. Taking the Poyang Lake delta, Wax Lake, Po River, and Rufiji River as examples, listwise deletion was first used to handle missing values, retaining forty-seven (87%), five (23%), thirty (100%), and forty-five (87%) valid samples, respectively. The sensitivity analysis showed that the 80% threshold of the Poyang Lake delta differed by only 2.4% between listwise deletion (50.1\({}^{\circ}\)) and mean imputation (51.3\({}^{\circ}\)), indicating a negligible impact of missing data on threshold calculation. Given that the Kolmogorov-Smirnov test revealed non-normal distributions for all delta data (all \(p<0.05\)) and [PERSON]'s test confirmed heterogeneous variances (\(p=0.018\)), a Kruskal-Wallis nonparametric test was employed, revealing highly significant differences in the distribution of \"adjacent angles\" among deltas (H = 12.73, \(p=0.005\)). Further, Mann-Whitney U tests with Bonferroni correction showed that the 80% threshold of the wave-dominated Po River delta (59.2\({}^{\circ}\)) was significantly higher than that of the tide-dominated Poyang Lake delta (50.1\({}^{\circ}\), \(p=0.003\)), reflecting the regulatory effects of sedimentary dynamic environments on channel bifurcation characteristics. These analyses demonstrate that the threshold differences of \"80% of samples \(<\) X meters\" are statistically significant rather than accidental results of empirical settings, and missing data did not substantially interfere with the robustness of the conclusions.
# Protection Strategy for Edge-Weighted Graphs in Disease Spread
## 4 Simulation of Disease Spread with Protection and Analysis Results  
### Data and Methodology  
The protection tests are carried out on 4 networks, these are: Zacharys karate club network [37] (with 34 nodes and 78 edges), wild bird network [38] (with 131 nodes and 1444 edges), Sandy authors network [39] (with 86 nodes and 124 edges), and CAG-mat72 (with 72 nodes and 678 edges) where each network will have the immunization corresponding to the 10% of its highest ranked nodes according to the DIL-\(W^{\alpha}\) ranking [27], strength of the node [40], weighted betweenness centrality [41], weighted closeness centrality [42], and Laplacian centrality for undirected and edge-weighted graph [43]. For the ranking DIL-\(W^{\alpha}\), we consider 3 different values for \(\alpha\), they are 0, 0.5 and 1. In this way, we compare how the protection performs according to the value of \(\alpha\). Finally, the databases of our test networks can be found in [44].
## 3 Materials and Methods
### Target Ship Position Estimation Based on Binocular Imaging  
#### 3.2.1 Binocular Calibration and Stereoscopic Correction  
Camera calibration is a prerequisite for stereo vision, where the imaging model typically assumes that the camera has no distortion under ideal conditions and describes the imaging process using a linear model. However, in practical applications, lens errors can cause distortion, affecting the imaging position of the images. Perspective, radial, and tangential distortion are the main types of distortion, with radial and tangential distortion having a significant impact on imaging. It has been shown that camera calibration [48] is an effective way to solve the camera lens distortion problem. Through the calibration process, we can obtain the parameters of the camera such as the distortion coefficients, rotation matrix, and translation vectors, which can be used as important input data for the next stereo correction. As the camera will be affected by the optical lens and its own process, the imaging process is prone to distortion, resulting in the left and right images not achieving coplanar and line alignment and requiring stereoscopic correction through left and right camera image plane reprojection, so that it is coplanar and aligned, so as to improve the accuracy of stereoscopic vision. Figure 5 shows a comparison of the effects before and after calibration and stereo rectification of our onboard stereo camera.