esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evolutionary Characteristics of Major Fractures  
### DBSACN Algorithm  
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that has the advantages of not requiring the number of clusters, the ability of handling clusters of arbitrary shapes, and effectively discovering noise points. DBSCAN algorithm only requires the determination of two parameters, \(\varepsilon\) and minPts. \(\varepsilon\) refers to the radius of the neighborhood region around a point, and minPts refers to the minimum number of points required in the neighborhood region (Figure 7). Some basic concepts of DBSCAN are defined as follows:  
1. \(\varepsilon\)-neighborhood: A region within a radius of \(\varepsilon\) around an object is called the \(\varepsilon\)-neighborhood of the object.
2. Core object: An object is considered as a core object if the number of sample points within its \(\varepsilon\)-neighborhood is greater than or equal to minPts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\sigma_{3}\)/MPa & \(\sigma_{ci}\)/MPa & \(\sigma_{cd}\)/MPa & \(\sigma_{t}\)/MPa & \(\sigma_{ci}\)/\(\sigma_{f}\) & \(\sigma_{cd}\)/\(\sigma_{f}\) & \(\sigma_{ci}\)/\(\sigma_{cd}\) \\ \hline
1-1 & 67.16 & 117.91 & 166.62 & 0.403 & 0.707 & 0.569 \\
1-2 & 80.11 & 159.78 & 208.13 & 0.385 & 0.768 & 0.501 \\
1-3 & 122.59 & 267.60 & 328.81 & 0.373 & 0.814 & 0.458 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic stress of Beishan granite under different confining pressure.  
Figure 6: Curves of AE hits, AE amplitude and stress with time: (**a**) 1-1; (**b**) 1-2; (**c**) 1-3.  
3. Directly density-reachable: For a given set \(D\), if point \(q\) is within the \(\varepsilon\)-neighborhood of \(p\), and \(p\) is a core object, then \(q\) is directly density-reachable from \(p\). 4. Density-reachable: For a given set \(D\), if there is a sequence of sample points \(p_{1}\), \(p_{2}\), \(\ldots\), \(p_{n}\), where \(p=p_{1}\) and \(q=p_{n}\), and if each object \(p_{i}\) is directly density-reachable from \(p_{i-1}\), then \(q\) is density-reachable from \(p\). 5. Density-connected: Points \(p\) and \(q\) are density-connected if \(p\) and \(q\) are directly density-reachable from \(\circ\).  
The goal of DBSCAN is to find the largest set of points that are density-connected. Based on \(\varepsilon\) and minPts, the points in the dataset can be classified into three categories:  
1. Core point: These are points whose number of samples in the \(\varepsilon\)-neighborhood is greater than or equal to minPts.
2. Border point: These are points whose number of samples in the \(\varepsilon\)-neighborhood is less than minPts, but the points can be obtained from some core points (density-reachable, or directly density-reachable).
3. Outlier: Points that are neither core points nor boundary points are referred to as outliers or noise points.
## 3 Results
### Results of Field Vane Tests  
The shear strength of the organic soils at the Antoniny, Koszyce, and Nielisz sites was measured by the field vane test (FVT). In order to evaluate the undrained shear strength \(\tau_{fu}\) of organic soils from FVT, the measured shear strength \(\tau_{fv}\) has to be corrected by using the correction factor \(\mu\) according to [PERSON] [40]:  
\[\tau_{fu}=\mu\cdot\tau_{fv} \tag{10}\]  
The correction factors for the organic soils from the Antoniny site were evaluated according to the method elaborated by the Swedish Geotechnical Institute (SGI) [41] based on the liquid limit \(w_{L}\), and also by the average correction factors determined from the following laboratory tests: triaxial compression, triaxial extension, and direct simple shear (Table 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Organic Mud** & **Mud** \\ \hline Water content \(w_{n}\) (\%) & 120–200 & 65–120 \\ Bulk density \(\rho\) (t/m\({}^{3}\)) & 1.2–1.3 & 1.3–1.5 \\ Specific density \(\rho_{5}\) (t/m\({}^{3}\)) & 2.1–2.3 & 2.3–2.5 \\ Liquid limit \(w_{L}\) (\%) & 130–220 & 70–130 \\ Organic content \(l_{om}\) (\%) & 21–35 & 8–20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Index properties of organic soils at the Nielisz site.  
Figure 3: Profiles of the corrected readings \(p_{o}\) and \(p_{1}\) and index parameters \(I_{D}\), \(K_{D}\), and \(E_{D}\) from dilatometer tests obtained for organic subsoil at the Nielisz site; (\(oc\)): overconsolidated subsoil, (\(nc\)): normally consolidated subsoil.  
For organic soils from the Koszyce site, the correction factors for peat, gyttja (calcareous-organic), and gyttja (calcareous), evaluated according to the SGI method based on the liquid limit \(w_{L}\), were \(\mu(w_{L})=0.5\), \(\mu(w_{L})=0.65\), and \(\mu(w_{L})=0.75\), respectively. For organic mud and mud from the Nielisz site, the correction factors evaluated according to the SGI method based on the liquid limit \(w_{L}\) were: \(\mu(w_{L})=0.6\) and \(\mu(w_{L})=0.65\), respectively.  
Shown in Table 6 are the values of the undrained shear strength \(\tau^{\prime}_{fw}\) of organic soils obtained from FVT using the corrected value of measured shear strength \(\tau^{\prime}_{fw}\).
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Interpolation
#### 3.3.2 Interpolating MODIS FAPAR to ASTER Dates  
The logic behind the 8-day compositing of MODIS FAPAR product is to select the maximum FAPAR across the 8 days for a given pixel [48]. For simplicity, it is assumed that the maximum FAPAR value in the 8-day interval corresponds to the end day of the composite period, rather than the start day. In the study, the acquisition dates of ASTER data are very close to the end day of the 8-day intervals of MODIS FAPAR product. Thus, a linear interpolation approach was regarded as acceptable to interpolate the coarse-resolution FAPAR products to the dates of ASTER data, which is suggested by [PERSON] _et al._[47] when the intervals were short.
# Numerical Simulation of Rock-Breaking Mechanism by Spherical Tooth Impact in Granite Formation
## 2 Model and Methodology
### Simulation Settings
#### 2.2.2 Grid and Independence Verification  
The numerical simulation investigates the rock fragmentation mechanism of large-diameter pneumatic down-the-hole hammers using two tungsten carbide button configurations with diameters of 16 mm and 22 mm. A three-dimensional finite element model was developed with particular attention to dimensional accuracy. The rock specimen dimensions were systematically determined based on button diameter scaling principles: length and width dimensions were set at 16 times the button diameter, while height was maintained at seven times. This scaling yielded a representative volume element measuring 300 mm (length) \(\times\) 300 mm (width) \(\times\) 130 mm (height), as illustrated in Figure 2.  
To realistically simulate fracture propagation dynamics, a cohesive zone modeling approach was implemented through zero-thickness cohesive elements inserted within the rock matrix meshing. This methodology enables spontaneous crack initiation and propagation under impact loading, demonstrating good congruence with experimental observations. Contact algorithms were carefully configured using a universal contact formulation to precisely characterize both button-rock interface interactions and inter-element rock contact behaviors. Furthermore, a dual-attribute constitutive model was established to simultaneously account for damage evolution at contact interfaces and viscous dissipation effects during dynamic fracturing processes.  
Mesh independence verification was conducted, as depicted in the figure, using rock models with five different mesh quantities. As shown in Figure 3, as the number of meshes increases, both the fragmentation range and the stress distribution become stable. When the mesh quantity reaches around 200,000, the curves begin to stabilize, and the difference between 220,000 and 200,000 meshes is less than 5%. Therefore, to ensure computational accuracy, a mesh quantity of 220,000 is selected.  
Figure 2: Spherical tooth impact model and meshing.  
To enhance the efficiency of the simulation analysis, an uneven meshing method is adopted. Since the rock model used is a hexahedron, the area impacted by the button is at the center of the rock's surface, while boundary conditions are applied around the rock to fix it as in experimental conditions. Thus, local mesh refinement is applied at the center where the button contacts the rock, while fewer meshes are used around the edges. According to the mesh independence verification requirements mentioned above, the granite model is divided into a total of 222,240 meshes. The meshing for the single-button impact model is shown in Figure 2.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 2 Literature Review
### Localization Estimation Algorithms  
Localization estimation algorithm is used to estimate the position information of a blind node based on partial observations of other measurements. Two of the most widely used estimation algorithms in localization are KF and PF/Monte Carlo Algorithms. Both KF and PF algorithms are based on Recursive Bayesian estimation that estimates an unknown probability density function recursively over time using incoming measurements and a mathematical process model. The KF achieves this goal by deriving analytics equations based on multivariate normal distributions and linear projections. For this reason, KF is excellent in estimating information in linear and normal distribution environments [8; 9; 10; 11; 12; 13]. Conversely, PF utilizes a set of discrete points or particles. The particles represent the distribution of likely states, with each particle representing a possible state. Therefore, PF is more efficient in non-linear and non-Gaussian environments. Furthermore, compared with the traditional trilateration localization method, PF is more robust and has less localization error in each iteration [14]. Taking into consideration the non-linear model of a number of mobile WSN, this paper concentrates on the PF localization method also known as Monte Carlo localization (MCL) [15]. The following section will discuss the details of Monte Carlo-based localization methods.
# Screen-Cam Robust Image Watermarking with Feature-Based Synchronization
## 6 Conclusions  
In this paper, a novel feature and Fourier-based screen-cam robust watermarking scheme is proposed. The distortions during the screen-cam process are analyzed. To resist possible desynchronization attacks caused by user operations and the screen-cam process, an LSFR construction method, based on the modified Harris-Laplace detector and SURF orientation descriptor, is designed to achieve watermark synchronization. In the proposed message embedding scheme, we repeatedly embed the message sequence in the DFT domain of each selected LSFR to achieve robustness against the screen-cam process. To decrease the quality degradation after embedding and improve the extraction accuracy, we employ a non-rotating embedding method and a preprocessing method to modulate the DFT magnitude coefficients. On the extraction side, we restore the captured image based on the size of the image itself to help improve the detection accuracy. The experiment shows that the proposed scheme has high robustness for common image attacks and screen-cam attacks. Compared with existing methods, the proposed scheme can further achieve robustness against screen-cam with additional common attacks.  
In future research, we aim to investigate automatic detection methods, which is a more practical application foreground. To achieve this goal, screen-cam robust invariants should be further investigated to help design novel local feature-based watermark synchronization methods or develop novel synchronization watermark message embedding and automatic detection methods.  
Conceptualization, W.C. and N.R.; methodology, W.C., N.R. and C.Z.; software, W.C. and Q.Z.; data curation, W.C., T.S. and A.K.; writing--original draft preparation, W.C. and N.R.; writing--review and editing, Q.Z., T.S. and A.K.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42071362 and 41971338, the Natural Science Foundation of Jiangsu Province, grant number BK20191373.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Sensory Evaluation  
The sensory evaluation of the color, aroma, acidity, clarity, mouthfeel and acceptability of cherry wine samples fermented with different kinds of organic acid additions is illustrated in Figure 8. The results indicated that organic acid addition had a significant effect on all of the sensory attributes (\(p<0.05\)). Specifically, the satisfaction with the clarity of the cherry wine was significantly higher than the control (\(p<0.05\)). Moreover, the increasing addition of organic acids had significant effects on aroma, acidity, mouthfeel and acceptability in each treatment group, while there was no significant difference in the effects on color and clarity, except in the MLC group (\(p<0.05\)). In each group, cherry wine obtained by adjusting the initial acid content to 4.15 and 4.80 g/kg had the highest aroma satisfaction, which means these two initial acid concentrations had a positive effect on the aroma release. However, the acidity satisfaction with cherry wine was reduced when the initial acidity content was 4.80 g/kg, as was the mouthfeel satisfaction, which resulted in lower acceptability of cherry wine.  
Thus, the contributions of different organic acids (the initial acid content was 4.15 g/kg) to the sensory properties of cherry wines were compared in this study (Figure 8D). It indicated that the addition of different organic acids had significant effects on the aroma, acidity and mouthfeel of cherry wine, while the contribution to the clarity of wines was not obvious. Among the samples, MLC-4.15 scored the highest in aroma, acidity, mouthfeel and acceptability, which means it is more likely to be chosen by customers.
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.3 SPEI Data  
The SPEI dataset was originally proposed by [PERSON] et al. [36]. The SPEI is calculated based on available water (monthly precipitation) and atmospheric evaporation demand (potential evapotranspiration) and offers the advantage of providing multi-time-scale data while accounting for potential evapotranspiration in drought assessments [37]. Meanwhile, the SPEI dataset has also been widely used to assess the impacts of drought on vegetation productivity in Inner Mongolia [38; 39]. The SPEI dataset has been widely used in numerous studies to assess drought trends [23; 40]. SPEI version 2.8 is available for access at the following website: [[https://digital.csic.es/handle/10261/288226](https://digital.csic.es/handle/10261/288226)]([https://digital.csic.es/handle/10261/288226](https://digital.csic.es/handle/10261/288226)), accessed on 1 August 2023. Considering that the SPEI with a three-month time scale (SPEI-3) can better reflect the severity and duration of grassland drought [26], SPEI-3 was used to assess the effect of drought events on grassland vegetation growth in Inner Mongolia. The SPEI values and corresponding drought levels are described in Table 1[36; 41].
## 2 Literature Review
### Customer Churn Prediction Review  
Customer churn prediction (CCP) plays a crucial role in this research, since it can contribute to the impact of customer retention on business profitability and growth. Well-known papers have been studied and summarized as follows.  
Researchers [16] have investigated the understanding of nuanced behaviors leading to customer churn in the telecommunication industry. Their combined approaches, using temporal centrality metrics and data analytics, have provided insights into early churn indicators and the role of customer lifecycle stages in influencing churn tendencies. Their emphasis on leveraging advanced predictive analytics for proactive churn management underscores the importance of timely interventions and personalized customer outreach.  
[PERSON] et al. [22] focused on developing their convolution neural network (CNN) techniques in CCP and compared their approach with current practices of text data analysis via text mining. They have found that CNNs outperform the current practices and unstructured data in text data prevent them from achieving a high accuracy. Their approach can extract the most valuable features from the unstructured textual data, thus achieving more reliable prediction with higher accuracy. Additionally, they [23] have focused on business-to-business (B2B) customer retention and analyzed 6432 customers. They developed the uplift Logit Leaf model (LLM), which can achieve better performance in CCP than competing models. By integrating behavioral economics principles with their LLM model, they have paved the way for more human-centric churn prediction strategies.  
[PERSON] et al. [24] proposes a deep learning model for predicting whether retail customers will churn in the future. The results showed that the deep learning model achieved better classification and prediction results than the Logistic Regression model and an artificial neural network model. Deep learning models have been proved to be effective in a variety of domains, including agriculture [25] and stock price forecasting [26]. Researchers [27] blended deep learning and natural language processing (NLP) to analyze 25,943 customer survey data. Their blended model can identify patterns, which can lead to improved decision-making and improve accuracy in their CCP analysis. Therefore, the use of ML algorithms can be effective in prediction robustness.
## 4 Simulation Results and Discussions  
### Performance Measures  
The system performance metrics used to evaluate the proposed systems are system utilization, average waiting time, and the TBP of each slice traffic. They are defined as follows:  
* System utilization (U)  
System utilization measures the efficiency of the slice allocation system. it can be defined as the ratio of the average number of the total current cell occupied RBs (\(R^{c}\)) to the total number of RBs in one radio frame (R) [37]. Therefore, utilization can be expressed as follows:  
\[U=\frac{R^{c}}{R} \tag{18}\]
* Average waiting time (W)  
The average queuing time delay for a request \(i\), \(W_{i}\), is the average waiting time of a new arrival user (call) before being served or blocked [37]. It can be expressed as follows:  
\[W_{i}=\frac{\sum Waiting\text{ }time\text{ }of\text{ }request\ i}{Total\text{ }num.\text{ }of\text{ }arrived\text{ }requests\ i} \tag{19}\]
* Total Blocking Probabilities (TBP)  
The (\(TBP_{i}\)) of a request \(i\) is the probability that request \(i\) will be rejected due to a lack of resources [11]. This probability is the sum of the blocking probability and the time-out probability. The blocking probability (\(BP_{i}\)), of a request \(i\) is the probability that request i is rejected (i.e., blocked) when its queue is full. It can be defined as follows:  
\[BP_{i}=\frac{Bk_{i}}{Ar_{i}} \tag{20}\]  
where \(Bk_{i}\) is the total number of type \(i\) requests that are blocked, and \(Ar_{i}\) is the total number of type \(i\) requests that have arrived.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Arrival Event of Request \(i\)** & **Departure Event of Request \(i\)** \\ \hline if (\(R_{c}+\Delta R_{i}\ \leq R\)) & if (\(Q_{i}\) is full) \\ \multicolumn{2}{c}{Admit request \(i\)} & Set ASP(i) = \(\frac{R_{i}^{c}}{R_{i}^{c}}\times(MTD_{i}-\ D_{i})\) \\ else & Select \(Q_{i}\) with the lowest ASP value. \\ \multicolumn{2}{c}{if (\(Q_{i}\) not reaches its limits)} & if (\(R_{c}+\Delta R_{i}\ \leq R\)) \\ \multicolumn{2}{c}{Admit request \(i\)} \\ \multicolumn{2}{c}{else} \\ \multicolumn{2}{c}{Insert in \(Q_{i}\)} & Release the resource. \\ \multicolumn{2}{c}{end if} \\ else & else \\ \multicolumn{2}{c}{Reject request \(i\)} & Release the resource \\ \multicolumn{2}{c}{end if} \\ end if & end if \\ \hline \hline \end{tabular}
\end{table}
Table 8: The arrival and departure procedures in the DA-ASA.  
The time-out probability (\(TOP_{i}\)) is the probability that a queued request \(i\) is dropped from its queue when it exceeds its maximum waiting time before receiving service. It can be defined as follows:  
\[TOP_{i}=\frac{Dp_{i}}{\Delta r_{i}} \tag{21}\]  
where \(Dp_{i}\) is the total number of type \(i\) requests that dropped, and \(Ar_{i}\) is the total number of type \(i\) requests that have arrived. Therefore, the \(TBP_{i}\) of a request \(i\) can be written as follows:  
\[TBP_{i}=BP_{i}+TOP_{i} \tag{22}\]
## 3 Empirical Gramians
### Parameter-Space Empirical Gramians
#### 3.2.1 Empirical Sensitivity Gramian  
Controllability-based parameter identification can be realized using an approach from [46], which treats the parameters as (additional) constant inputs. The controllability Gramian for a linear system with linear parametrization (constant source or load) can be decomposed additively based on linear superposition:  
\[\dot{x}(t) =Ax(t)+Bu(t)+F\theta=Ax(t)+\begin{pmatrix}B&F\end{pmatrix}\begin{pmatrix} u(t)\\ \theta\end{pmatrix}\] \[\Rightarrow W_{C}=W_{C}(A,B)+\sum_{i=1}^{P}W_{C,i}(A,F_{si}).\]  
Similar to [12], the trace of the parameter-controllability Gramians \(W_{C,i}\) embodies a measure of (average) sensitivity, and holds approximately for systems with nonlinear parametrization [38].  
**Definition 6** (Empirical Sensitivity Gramian).: _The **empirical sensitivity Gramian** is given by a diagonal matrix with entries corresponding to the traces of the parameter-controllability Gramians,_  
\[W_{S,ii}:=\operatorname{tr}(W_{C,i}).\]  
The sum over all controllability Gramians can also be used for robust model reduction [5]. Similarly, treating the parameters as inputs, the cross Gramian's trace can be utilized as a sensitivity measure [13].