esa-sceva/satcom-chunk-collection huggingface dataset:
# Displacement Patterns and Predictive Modeling of Slopes in the Bayan Obo Open-Pit Iron Mine
## 4 Discussion
In regions with minimal rainfall fluctuations, arid climates, or complex hydrogeological conditions, this coupling may exhibit weak correlations or significant time lags, necessitating the introduction of alternative drivers, such as groundwater levels or hydraulic pressures, or structural model adaptations to accommodate regional characteristics. Third, lithological characteristics fundamentally govern slope response mechanisms to external disturbances. The rock mass in the Bayan Obo mining area primarily contains water-sensitive minerals (e.g., mica, montmorillonite) with developed joint fractures. In contrast, regions with homogeneous lithology, compact structures, or dry stable conditions often show atypical rainfall-induced displacement responses, leading to difficulties in periodic component extraction and reduced prediction model stability. Finally, the framework exhibits sensitivity to multiple critical parameters: DBSCAN's \(\epsilon\) and MinPts settings, VMD's mode number selection, and hyperparameter configurations in LSTM/GRU networks. Optimal parameter combinations may vary substantially across regions. Therefore, during implementation, cross-validation or sensitivity analysis should be conducted based on regional geological and climatic features. To assess the computational feasibility of the proposed method for near-real-time warning systems, this study evaluates the computational demands of the PSO-VMD algorithm and the training efficiency of deep neural networks. On a standard computer equipped with an Intel Core i5-13400F CPU and 16 GB RAM, the PSO-VMD algorithm completes one parameter optimization cycle (with particle swarm size set to 10 and maximum iterations to 100) in approximately 30 s on average, demonstrating low resource consumption without reliance on GPUs or high-performance servers. Under identical hardware conditions, the training processes of both LSTM and GRU models are completed within 1 min. Comprehensive tests confirm that the method achieves high prediction accuracy with low computational demands and rapid response times, meeting the essential computational performance criteria of near-real-time landslide early warning systems.
# Sliding Mode Thau Observer for Actuator Fault Diagnosis of Quadcopter UAVs
## 3 Nonlinear Observer for Fault Diagnosis  
### Standard Thau Observer for Fault Detection  
According to the state Equation (7), the two following conditions must be met by the Thau observer design:  
**C1** the pair \((C,\ A)\) is observable.  
**C2** the nonlinear term \(p(x,u)\) is continuously differentiable and assumed to be Lipschitz, with a constant \(\gamma\), i.e., \(\|p(x_{1}(t),u(t))-p(x_{2}(t),u(t))\|\leq\gamma\|x_{1}-x_{2}\|\).  
From the above conditions, the state Equation (7), based on Thau observer, can be constructed as [20]:  
\[\left\{\begin{array}{l}\dot{\hat{x}}(t)=A\hat{x}(t)+p(\hat{x},u)+Bu(t)+K( \hat{y}(t)-y(t))\\ \hat{y}=C\hat{x}(t)\end{array}\right., \tag{8}\]  
where \(K\) is the observer gain matrix which is determined by  
**Lemma 1**.: _[_11_]_ _: If the given observer gain matrix in Equation (8) satisfies_  
\[K=P_{\varepsilon}^{-1}C^{T}, \tag{9}\]  
_then matrix \(P_{\varepsilon}\) can be obtained from the Lyapunov equation_  
\[A^{T}P_{\varepsilon}+P_{\varepsilon}A-C^{T}C+\varepsilon C^{T}P_{\varepsilon} =0, \tag{10}\]  
_where \(\varepsilon\) is a positive constant such that \(P_{\varepsilon}\geq 0\), and the state space model Equation (6) is an asymptotic estimation with \(\lim\limits_{t\rightarrow\infty}\varepsilon(t)=\lim\limits_{t\rightarrow\infty }(\hat{x}(t)-x(t))=0\)._
# Eight-Port Modified E-Slot MIMO Antenna Array with Enhanced Isolation for 5G Mobile Phone
## 3 Results and Discussions
### MIMO Channel Capacity and ECC  
The channel capacity and envelope correlation coefficient are the key criteria for assessing a MIMO antenna diversity and multiplexing capability. Although zero is the ideal value for ECC, in practice the acceptable limit must be less than 0.5 (ECC < 0.5). The value of the ECC can be calculated from the S-parameter or from the far-field radiation pattern [29; 30]. This work uses a far-field radiation pattern [31] to calculate the ECC value as expressed in [32; 33] (1). From Figure 6f, this work obtained an ECC value lower than 0.01 across the desired band of interest. Figure 9 is the channel capacity plot of the design. The fabricated prototype and measurement set up is shown in Figure 10.  
\[ECC=\frac{\left|\int\  
where \(\overrightarrow{D_{i}}(\theta,\varphi)\) and \(\overrightarrow{D}(\theta,\varphi)\) represent the radiation pattern when antenna \(i\) and \(j\) are energized, while \(\Omega\) is the solid angle.  
\[DG=10(dB)\times\sqrt{1-\left|ECC\right|^{2}} \tag{2}\]  
The diversity gain plot of the antenna elements is shown in Figure 11, while Equation (2) is used to calculate the DG from the ECC value. It could be seen that for all the possible combinations, a gain of more than 9.99 dB is achieved over the operation band.  
Figure 10: (**a**) Proposed MIMO system fabricated prototype (**b**) measurement set-up (**c**) S-parameter measurement by the VNA (**d**) antenna under measurement in the chamber room.  
Figure 9: Calculated channel capacity of proposed system.  
Based on antenna efficiency and ECC, the ergodic channel capacity is obtained by averaging 100,000. Rayleigh fading channel realizations with the Kronecker channel model by considering SNR of 20 dB [34; 35]. The value varies from 40.95 to 43.50 bps\(/\)Hz. The highest peak channel capacity (PCC) is (43.5 b\(/\)s\(/\)Hz), which is satisfactory in performance with SISO (5.57 b\(/\)s\(/\)Hz) and close in value for 8 \(\times\) 8 MIMO (46 b\(/\)s\(/\)Hz). These values demonstrate unequivocally that the presented antenna system is suitable for use in 5G mobile terminal applications due to its high diversity and multiplexing capabilities. Figure 10 shows the proposed antenna fabricated prototype, far-field measurement set-up, S-parameters measurement using Vector Network Analyzer (VNA), and the antenna in the anechoic chamber room under measurement. From the obtained results, it can be easily concluded that the proposed design is highly competitive in comparison to the most recent advancements in this field, as presented in Table 2.
## 5 Experiments
### Experiment Settings  
We split the original datasets into the training set, validation set, and test set in a ratio of 6:2:2. We used the past one hour data to predict the future one hour data, which means \(P=Q=12\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Nodes** & **Days** & **Time Steps** & **Missing Rate** \\ \hline PEMS03 & 358 & 91 & 26,208 & 0.672 \% \\ PEMS04 & 307 & 59 & 16,992 & 3.182 \% \\ PEMS07 & 883 & 98 & 28,224 & 0.452 \% \\ PEMS08 & 170 & 62 & 17,856 & 0.696 \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets description [58; 59].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Spatial Global** & **Spatial Local** & **Temporal** \\ \hline FC-LSTM & - & - & RNN \\ DCRNN & - & Bidirectional random walks & RNN \\ STGCN & - & GCN & TCN \\ ASTGCN(R) & - & GCN & TCN \\ STSGCN & - & Synchronization graph \\ STFGNN & Temporal graph & - & Gated dilated convolution \\ LGTCN & Probabilistic Sparse Self-Attention & Bidirectional GCN & Multi-Channel TCN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Methodology used in the baseline model.  
We implemented the LGTCN model on the Pytorch platform, and the hyperparameters of the model were determined on the validation set. We used the same model architecture to conduct experiments on each of the four datasets. Among them, the number of multi-head of the probability sparse self-attention mechanism is 4, and d = 12. Multi-channel temporal convolutional network adopts dynamic expansion coefficients of 1, 2, 4, and 4 respectively. The hidden layer neurons of the prediction module are 128.  
We adopted three validation metrics for model performance evaluation, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Their formulas are presented as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}^{p}-y_{i}\right| \tag{15}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}^{p}-y_{i})^{2}} \tag{16}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y_{i}^{p}-y_{i}\right|}{y_{i}} \tag{17}\]  
where \(n\) is the number of all values, \(y_{i}\) is the ground values, and \(y_{i}^{p}\) is the predicted values at time \(i\).
## 7 Research Gaps and Future Opportunities
### Protection against Communication Attacks
#### 7.1.4 Lack of Understanding  
We know, BC-SCM solutions are employed to record communication (i.e., data exchange) among different stakeholders of a supply chain system. Due to lack of information about communication process (i.e., communication/computing protocol), BC-SCM solutions may not be able to comprehend intentions of the attacker or adversary. Therefore, they could not succeed to provide cognitive and intelligent protection against known as well as unknown attacks. In principle, any computing process/communication protocol may have different implementations, which implies that even though the implementations are producing the same output they may be vulnerable to different attacks because they have been implemented in different ways (i.e., using different libraries). Therefore, it is important to understand process/implementation level details in order to make systems self-aware as well as to identify attacker's intentions.
###### Abstract  
The new Ti-Zr (Roxolid(r)) mini-implants have not yet been fully researched. We analyzed peri-implant and posterior edentulous area microstrains during mandibular overdenture (OD) loading at different sites with different extents of forces when one-, two-, three-, or four- mini dental implants (MDIs) as single-units supported the respective ODs. The models were designed from cone beam computed tomography (CBCT) scans of an appropriate patient with narrow ridges. The mucosal thickness was 2 mm. Strain gauges were bonded on the vestibular and oral peri-implant sites, and in the distal edentulous area under the saddles. The loads were applied posteriorly bilaterally and unilaterally with 50, 100 and 150 N forces, and anteriorly with 50 and 100 N forces. Each loading was repeated 15 times. Statistical analysis included descriptive statistics, boxplots and the MANOVA. Higher forces induced higher peri-implant microstrains, as well as unilateral loadings, especially on the loaded side, in all models except the one-MDI model where anterior loads (100 N) elicited the highest peri-implant microstrain (1719.35 \(\pm\) 76.0). The highest microstrains during unilateral posterior loading (right side) with 150 N force were registered from the right MDI in the two-MDI model (1836.64 \(\pm\) 63.0). High microstrains were also recorded on the left side (1444.48 \(\pm\) 54.6). By increasing the number of implants, peri-implant microstrains and those in the edentulous area decreased. In the three- and four-MDI models, higher microstrains were found in the posterior than in the anterior MDIs under posterior loadings. None of the recorded microstrains exceeded bone preparatory mechanisms, although precaution and additional research should be provided when only one or two MDIs support ODs.  
T:-Zr mini-implants; single units; strain gauges; mandibular overdenture; different number of mini-implants; loading forces; loading position; dentistry; oral surgery +
Footnote †: journal: Applied Sciences  
Article
## 2 System Definition
### Wildfire Detection Methods
#### 2.1.3 Pervasive _In Situ_ Detection Methods
WSN detection can also be combined with UAV monitoring [50] to increase the detection accuracy and to collect the necessary data to define and coordinate the actions of the firefighters. While most WSN deployments are designed for long-term monitoring, they can be deployed in the affected areas for the short term to continuously collect data to help support firefighters' operations in the field [68]. Meeting the stringent requirements for effective long-term outdoor environmental monitoring is generally challenging for WSN technologies [69, 70, 71]. Nodes need to reliably operate for very long times without or with minimal maintenance in variable and sometimes extreme climatic conditions. Outdoor applications demanding a high number of nodes put additional emphasis on node cost and reliability. Node weight, size and ruggedness become also important for deployments in fields that are difficult to reach, e.g., when nodes are transported in backpacks [8].
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Hidden Markov Model (HMM)  
The HMM is a dual embedded random method often used to perform more complex random processes better than the traditional Markov model [19]. Based on the reviewed literature, several methods have employed the HMM technique for financial fraud detection. [PERSON] et al. [93] used an HMM-based method for detecting the card owners' behaviors through the observed incoming transaction. The author used a clustering approach to differentiate between fraudulent and non-fraudulent patterns using data conglomeration of regions of the parameter. [PERSON] et al. [19] introduced a hybrid method by integrating HMM and Genetic algorithms (GA) for identifying credit card fraudulent transactions. The approach employed the HMM to preserve previous transaction logs and GA to compute the threshold value for clustering incoming transactions into several clusters. The authors showed that the method used is more effective for credit card fraud detection. A similar approach was proposed in [86] to achieve internet banking fraud detection by disclosing the right users and monitoring their illicit behaviors.  
A method in [73] utilized HMM to address the limitation of existing fraud-detection methods when the transaction is accomplished in credit card operations. The findings of the study suggested that HMM is capable of improving fraud detection and as well minimizing false-positive rates. [PERSON] et al. [94] conducted a simulation experiment using HMM and K-means methods for improving bank fraud detection. The result demonstrated the ability of the model to effectively discover fraud in bank transactions. A similar approach proposed in [20] used an HMM-based technique, which improves the efficiency and accuracy of credit card fraud detection. To determine the clusters' closest centroids and integrate them into a single group, the authors also employed the clustering technique based on the K-means method.
## 2 Materials and Methods
### Numerical Model
#### 2.2.4 Calibration and Evaluation of the Model  
The reliability was verified and analyzed by inputting the solute transport parameters and soil hydraulic parameters through the HYDRUS-1D software, and the simulation's accuracy was evaluated by the coefficient of determination \(R^{2}\), and the root mean square error (\(RMSE\)). The closer the coefficient of determination \(R^{2}\) was to 1, the closer the root mean square error (\(RMSE\)) was to 0, which means that the model simulation results and the measured results of nitrogen had higher fitting accuracy. The calculation formula is:  
\[R^{2}=\frac{\sum_{i=1}^{n}(\frac{\wedge}{y_{i}}-\overline{y})^{2}}{\sum_{i=1}^{ n}\left(y_{i}-\overline{y}\right)^{2}} \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(S_{i}-M_{i}\right)^{2}} \tag{6}\]  
where \(S_{i}\) and \(M_{i}\) are simulated and measured values, respectively; \(N\) is the number of samples.  
According to the simulation results of the numerical model of nitrogen migration and transformation in the water level fluctuation zone, the simulated values of ammonia nitrogen, nitrate nitrogen and nitrite nitrogen are shown in Figures 4-6, and the model fitting results are shown in Table 5. The correlation coefficients are mostly above 0.8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Medium**} & \multirow{2}{*}{**Solute**} & \multirow{2}{*}{\(\rho\)**/mg:cm\({}^{-3}\)**} & _Disp/cm_ & _D/cm\({}^{2}\).d\({}^{-1}\)_ & _Kd/cm\({}^{3}\).mg\({}^{-1}\)_ \\  & & **Bulk Density** & **Longitudinal Diffusion** & **Solute Diffusion Coefficient** & **Adsortion Coefficient** \\ \hline \multirow{4}{*}{Coarse sand} & Nitrite Nitrogen & & & 1.29085 & 0.000256 \\  & Nitrate & 1600 & 1.568 & 1.69085 & 0.001584 \\  & Ammonium Nitrogen & & & 1.69085 & 0.008779 \\ \hline \multirow{4}{*}{Medium sand} & Nitrite Nitrogen & & & 1.29085 & 0.000103 \\  & Nitrate & 1800 & 1.233 & 1.69085 & 0.000413 \\  & Ammonium Nitrogen & & & 1.69085 & 0.008895 \\ \hline \multirow{4}{*}{Fine sand} & Nitrite Nitrogen & & & 1.29085 & 0.000767 \\  & Nitrate & 1800 & 1.116 & 1.69085 & 0.003094 \\ \cline{1-1}  & Ammonium Nitrogen & & & 1.69085 & 0.008976 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Solute transport parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{\(\theta r\)**} & \multirow{2}{*}{\(\theta\)s**} & \(\alpha\)/cm\({}^{-1}\) & \(n\) & _Ks/(cm\(\cdot\)d\({}^{-1}\))_ \\  & & **Saturated Volume** & **Soil Moisture** & **Soil Moisture** & **Saturated** \\  & **Water Content** & **of Water Content** & **Characteristic Parameters** & **Characteristic** & **Hydroraulic** \\ \hline Coarse sand & 0.045 & 0.43 & 0.1450 & 2.68 & 712.8 \\ Medium sand & 0.051 & 0.42 & 0.1045 & 2.08 & 550.0 \\ Fine sand & 0.057 & 0.41 & 0.1240 & 2.28 & 350.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Soil and water characteristic parameters.  
Figure 5: The process of nitrogen change in medium sand ((**a**) nitrate nitrogen; (**b**) nitrite nitrogen; (**c**) ammonium nitrogen).
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
###### Abstract  
Aiming at the change detection of water resources via remote sensing, the non-subsampling contour transformation method combining a log-vari model and the Structural Similarity of Variogram (VSSIM) model, namely log-vari and VSSIM based non-subsampled contourlet transform (L-V-NSCCT) approach, is proposed. Firstly, a differential image construction method based on non-subsampled contourlet transform (NSCT) texture analysis is designed to extract the low-frequency and high-frequency texture features of the objects in the images. Secondly, the texture features of rivers, lakes and other objects in the images are accurately classified. Finally, the change detection results of regions of interest are extracted and evaluated. In this experiment, the L-V-NSCCT approach is compared with other methods with the results showing the effectiveness of this method. The change in Dongting Lake is also analyzed, which can be used as a reference for relevant administrative departments.  
change detection; NSCT; variogram function; structure similarity; Dongting Lake 2019
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 5 SR in Wi-Fi 6
### Mechanisms for Spatial Reuse in Wi-Fi 6
#### 5.3.1 BSS Coloring  
BSS coloring [111] is an inherited feature from IEEE 802.11 ah to distinguish traffic from different BSSs. It is a 6-bit field in the novel frame preamble [112]. The access points operating on the same channel should have different BSS colors. A station uses the BSS color in the preamble to make a distinction between intra- and inter-BSS transmissions.
## 1 Introduction
However, it is, therefore, different from the purpose of analyzing the weights in this research [46]. After that, [PERSON] et al. (2019) proposed two decision-making methods based on fuzzy rough set models integrating with the fuzzy TOPSIS method (two cases) and the fuzzy aggregation operator method. The first method ranks the alternatives by the principle of fuzzy TOPSIS that the alternative with the shorter distance from the positive ideal solution (PIS) and the longer distance from the negative ideal solution (NIS) has higher priority. The second method ranks the alternatives by calculating the virtual ideal decision-making object, the lower and upper approximations, the individual ranking function, and the group ranking function using the formula derived by authors [47]. The methods are innovative and the ranking results from those three methods have a high degree of consensus (the rankings are almost the same). This paper is important and valuable but FANP is adopted by this research in consideration of the purpose of acquiring the weight of indicators and the fact of indicators' dependency. [PERSON] and [PERSON] [1] established the service quality evaluation framework for drone filming and photography and acquired the weights and rankings of indicators by ANP, but not by FANP. The purpose of this research is focused on extracting knowledge of drone filming and photography's service quality indicators and its weights from a set of experts' opinions precisely. After reviewing the current state of the research field carefully and key publications, it is clear that the data collected from the questionnaire by FANP will be more realistic than ANP and the information analyzed from these data can further include the comprehensive consensus. Therefore, in order to measure the indicators' weight for drone filming and photography service quality precisely and make an in--depth study on the experts' opinions, unlike the research of FANP with one method of integrating opinions, this research adopts two methods to integrate expert opinions at the same time and discusses it. By the FANP variant adopted in this study, the following purposes are achieved. 1. Construct the drone filming and photography service quality evaluation structure based on the SERVQUAL scale. 2. Analysis weights of the dimensions and indicators of drone filming and photography service quality from the comprehensive experts' viewpoint by FANP combined with a geometric mean. 3. Analysis weights of the dimensions and indicators of drone filming and photography service quality from the consensus of experts' viewpoint by FANP combined with SAM. 4. Compare and discuss the difference of the indicators and dimension's weight of drone filming and photography service between the comprehensive consensus of the experts' viewpoint.