esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experimental Settings  
The results are divided into two steps. In the first step, the behavior of different regularized MoLEs is evaluated in different scenarios (different numbers of batches of data for training). The objective in this step is to understand the effects of samples in the stability/performance of each MoLE model with different penalties. In the second step, the mixture models that were trained on the full training data are compared to other predictive models by assessing the performance on the test dataset. The predictive performance is measured by the normalized root mean square error (NRMSE) and the coefficient of determination (R\({}^{2}\)). The definitions of these metrics are as follows:  
NRMSE \[=\frac{\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}}{ \text{max}(\mathbf{y})-\text{min}(\mathbf{y})},\] (23) \[\text{R}^{2} =1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_ {i}-\overline{y})^{2}},\] (24)  
where \(\hat{y}_{i}\) is the predicted output and \(\overline{y}\) is the sample mean of output \(y\): \(\overline{y}=\frac{1}{n}\sum_{i}^{n}y_{i}\). Lower values of NRMSE mean better models, while higher values of R\({}^{2}\) mean better models. R\({}^{2}\) can be interpreted as the rate of explained variance of the output to be predicted by the model, with bounds \(0\leq\text{R}^{2}\leq 1\), where 1 means perfect fit.
## 2 Theory and Method
### Comparison and Verification  
The Volgograd underground gas storage represents the only successful implementation of horizontal multi-step salt cavern gas storage to have been reported. To verify the validity and accuracy of the coupled flow model, the process of constructing the Volgograd salt cavern is simulated with the same parameters of field [27]. By using sonar detection technology, a sonar detector is deployed after the cavity formation stage ends. This process captures the propagation and reflection time of sound waves in different media, subsequently obtaining data on the cavity volume and morphology. The outlet brine concentration and cavern volume data are monitored and compared. As shown in Figures 7 and 8, the proposed model predictions are consistent with the Volgograd salt cavern monitored data with the same trend, and the deviations are within 9.4% for the outlet brine concentration and 6.3% for the cavern volume. During the process of multi-step cavity construction, the brine concentration initially experienced a rapid decline, which subsequently rose to approximately 240 g/L by day 90, and thereafter incremented slowly, stabilizing around 270 g/L.  
Figure 6: Flow chart of the simulation of the coupled model.  
For the cavern shape, due to the limitations of current sonar detection technology in mapping the shape of horizontal sections, the 3D shape of field horizontal caverns created through multi-step leaching is not documented in the existing literature. The side cavern shape of the proposed model is compared with a large-scale leaching experiment on a salt dome [46] and the M-SHLS model [38], as shown in Figure 9. The side of the cavern is crown-shaped above and cone-shaped below, as shown in both the experiment and model results.  
Due to the limited data available on horizontal salt cavern construction, a degradation verification is performed by simulating the process of multi-step vertical salt cavern construction and comparing the data with software PCL 1.0 to further confirm the model's reliability. As illustrated in Figure 10, the outlet brine concentration and cavity volume evolutions with the leaching time were analyzed and compared. The results demonstrate that the outlet brine concentration and the cavity volume data obtained from the coupled model are largely consistent with the data of the PCL 1.0. As dissolution progresses, the brine concentration initially increases rapidly, and then rises steadily  
Figure 8: Data comparison of cavern volume evolutions with leaching time.  
Figure 7: Data comparison of outlet brine concentration evolutions with leaching time.  
Figure 9: Comparison of cavern side shapes [46].  
the cavity volume initially increases slowly, and then rises steadily at a faster rate. The comparison results further verified the reliability of the coupled model.  
Above all, through comparisons of the outlet brine concentration, the cavern volume, and the side shape, the proposed coupled flow model is reliable and has a high accuracy.
## Appendix A.1. Security Models  
**Definition A1** (Group Mutual Authentication).: _An attacker interacts with a simulator in the following game (Figure A1)._  
_Setup. The KGC publishes the system parameters to the attacker._  
_Training Phase. The attacker is allowed to query the following oracles:_  
* _Registration oracle: The outputs of this oracle are the public key_ \(A\) _and the secret key_ \(T_{A}\)_._
* _Group-joining oracle: The input of this oracle is_ \(B_{i}\) _and the output is_ \(w_{B_{i}}\)_._
* _Authentication oracle: The inputs of the oracle are a group manager name, the group name, and the user of the group. The outputs of the oracle are_ \(t_{i},\ a_{i}\)_, and_ \(b_{i}\)_._  
_Challenge. There are two parts of this phase, depending on the entity that the attacker impersonates._  
_Part I: (The Authenticity of the Manager)_  
_The attacker sends_ \(t_{i}\) _to the simulator. After receiving_ \(t_{i}\)_, the simulator computes_ \(d_{B_{i}}\)_._  
_The simulator chooses a number_ \(r_{i}\) _randomly, computes_ \(k_{i},\ a_{i}\)_, and sends_ \(a_{i}\) _to the attacker._  
_The attacker sends_ \(b\) _to the simulator. If the attacker is authenticated, the attacker wins the game._  
_Part II: (The Authenticity of the User)_  
_The attacker sends_ \(a_{i}\) _to the simulator. After receiving_ \(a_{i}\)_, the simulator computes_ \(d_{B_{i}}\) _and_ \(F_{B_{i}}\)_._  
_The simulator checks if_ \(F_{B_{i}}d_{B_{i}}=t_{i}\ mod\ q\)_. If it is true, the attacker is authenticated and wins the game._  
_A scheme is said to achieve secure group mutual authentication  
* _Registration oracle: The outputs of this oracle are the public key_ \(A\) _and the secret key_ \(T_{A}\)_._
* _Group jointing oracle: The input of this oracle is_ \(B_{i}\) _and the output is_ \(w_{B_{i}}\)_._
* _Authentication oracle: The inputs of the oracle are a group manager name, the group name, and the user of the group. The outputs of the oracle are_ \(t_{i},\ a_{i}\) _and_ \(b_{i}\)_._  
_Challenge._ First, the simulator runs the authentication protocol and generates a valid session key \(sk\), and chooses another random session key \(sk^{\prime}\). Then, the simulator chooses a bit \(\beta\) and sets \(sk_{\beta}=sk,\ sk_{1-\beta}=sk^{\prime}\). Next, the simulator sends \(sk_{0},\ sk_{1}\) to the attacker. After receiving \(sk_{0},\ sk_{1}\), the attacker outputs a bit \(\beta^{\prime}\), and wins the game if \(\beta^{\prime}=\beta\).  
A scheme is said to achieve a secure session key if there is no polynomial-time adversary winning the above game with non-negligible advantage.
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 5 Experiments
### Comparison Experiments  
In this paper, we investigate the effects of three different distributional feature datasets on model performance, distributing four types of data for personnel safety state warning experiments using human feature data, environmental feature data, human-environment data features and human-environment-location information features. The linear kernel function and RBF kernel function were selected for comparison experiments. During the experiments, the human body feature data consisted of body temperature and heart rate, the environmental feature data consisted of internal tunnel ambient temperature and ambient humidity, all of which were two-dimensional data, while the human-environment feature data and human-environment-location feature data were multidimensional data. The ratio of training set to test set is 8:2. The number of support vectors of the models trained by each type of data is counted, and the prediction accuracy of the test set is calculated. The experimental results are shown in Table 9.  
After comparing the experimental results of the four types of data with different statistical distribution characteristics in different collection sources, we can find that the performance of the OCSVM model is good on each dataset, and the model accuracy is near 95%. In terms of kernel function selection, the model prediction accuracy of the RBF kernel function is slightly higher than that of the linear kernel function. However, the number of support vectors generated by the RBF kernel function in the models of each dataset is more than that of the linear kernel function, which indicates that the computational complexity of selecting this kernel function is higher. Thus, when the dataset is large, the linear kernel function can be selected for pre-processing and preliminary classification, and then the final state alarm can be determined using the RBF method.
# Using Aspect-Level Sentiments for Calling App Recommendation with Hybrid Deep-Learning Models
## 4 Results
### Experimental Results for Deep-Learning Models  
This study utilized LSTM, RNN, GRU, and CNN as well asf their combinations in comparison with machine-learning models. These models were deployed with their state-of-the-art architectures as shown in Figure 4. All models took text input through an embedding layer with a vocabulary size of 5000, and the output dimension size was 200. Each model consists of dropout layers with different dropout rates to reduce the complexity of computation and dense layers with a different number of neurons. As the end layer, each model consists of three neurons and a softmax activation function. All models are compiled using the categorical_crossentropy loss function because of the multi-class data and Adam optimizer. Each model is fitted with 100 epochs and a batch size of 32. Table 16 shows the results for the deep-learning models in terms of the accuracy, precision, recall, F1 score, and G-mean. The performance of all deep-learning models was significantly better in comparison with the machine-learning models. The experimental results reveal that GRU outperformed individual models for sentiment classification. For the better feature extraction capability of CNN, we combined CNN with GRU to break the traditional CNN-LSTM streak. This combination of CNN-GRU produced significant results as CNN feature mapping criteria helped to generate more correlated features for GRU to
# Multi-Hazard WebGIS Platform for Coastal Regions
## 1 Introduction
Additionally, a baseline from historical data at regional scale sets the scene for each coastal observatory, representing distinct coastal typologies, to efficiently support the flooding preparedness and response actions for emergency management needs, considering the singularities of a territory exposed to different hazards. In order to characterize coastal elements, the concept of observatories allows the aggregation of data and the possibility to export the knowledge and tools to other coastal sites. The MOSAIC platform is the vehicle to convey the relevant information, aggregated in relevant indicators, but with the possibility to drill down to the level of individual sensor data or the model prediction maps at a specific time step and for a specific location. This platform is implemented in a complex, detailed data repository that manages both observations and model predictions. Distinct types of information, including time series, 2D results and remote sensing images from Sentinel satellites, are included, duly integrated according to their specific characteristics. Integration of sensor/data type is performed in a modular way that facilitates the integration of new sources of information. Thus, hazards can be analyzed in an integrated way (e.g., through inundation maps) or individually, through specific data streams targeted at measuring specific hazards (e.g., erosion mapping from altimetry from drone flights). This paper is divided into three sections, besides this introduction. The framework for multi-hazard analysis is described first, including both modeling and monitoring methodologies. Next, the MOSAIC platform from both coastal engineering and computer science perspectives are described. Section 3 illustrates its application to an observatory in Portugal, demonstrating its value to address multi-hazard science in coastal regions. The paper closes with concluding remarks and directions for future research.
## 6 Conclusions  
In this paper, a general NMM is developed in general equations with initial discontinuity, where the local approximation is a composition of Legendre polynomials. The hat function is used to construct the PU. We tested NMM using several examples, and there are some conclusions shown as follows:  
1. NMM can approximate the solution using different local approximation, and the local approximation can be any order.
2. The higher order NMM has bigger kernel space with providing better precision.
3. NMM can be successfully used to deal with the discontinuity capture, due to the divisible physical patches.
4. Although there are discontinuities, the \"rank problem\" (linear dependence) will not occur when the physical patches are cut. The uniqueness and existence of solution can be verified by numerical examples.
5. For dealing with discontinuities, we can cut the physical patches at discontinuities, and add more equations at discontinuities. We can also cover the discontinuity with mathematical patches by using exact solver such as Riemann solver.
6. The speed of the discontinuities should be determined by the Ranking condition when discontinuities occur.  
Conceptualization, Y.Z. and H.Z.; methodology, Y.Z.; software, Y.Z.; validation, Y.Z., H.Z. and C.L.; formal analysis, Y.Z.; investigation, Y.Z.; resources, Y.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z., H.Z. and C.L.; visualization, Y.Z.; supervision, H.Z. and C.L.; project administration, C.L.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
NMMNumerical manifold method  
FEMfinite element methods  
FDMfinite difference methods  
FVMfinite volume methods  
ENOessentially non-oscillatory behavior  
TVDTotal Variation Diminishing  
MUSCLMonotone Upstream-centered Schemes for Conservation Law  
LAslocal approximations  
GFEMgeneralized finite element methods  
MCmathematical cover  
PCphysical cover  
PPsphysical patches  
GFEMgeneralized finite element methods  
MLSMoving Least-Squares  
LDlinear dependence  
PUpartition of unity
## 4 Results
### Deep Displacement Data
#### 4.3.3. Inactive-Motion Stage  
SM stages were anticipated or followed by periods in which the monitoring data indicated that the landslide gradually ceased motion (Figure 12). This is denoted by virtually flat stretches of average cumulative daily displacement time-series for all the automatic probes (Figure 16).  
Also, these kinematics stages showed similar durations for most of the monitoring period, typically six to seven months (Table 4). Moreover, these periods occurred cyclically during phases of scarce precipitations (Table 4): the highest cumulative rainfall was around 280 mm, falling approximately over seven months, with a mean daily rainfall of 1.3 mm (Figure 16 and Table 4).  
The fitting of linear trends to daily cumulative displacement data revealed that movements referring to some IM periods, within the limits of the sensor accuracies, can be considered negligible and not statistically meaningful at the 95% confidence level (Table 4). Indeed, the estimated mean rates of displacement were close to the corresponding standard errors at the 95% confidence limit. However, in some cases, some slightly negative gradients were obtained (Figure 16 and Table 4), which may be explained as a result of the effects of sensitivity of automatic probes to instrument drift. Therefore, based on both monitoring  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Probe Id**} & **Motion Stage** & **Duration (Days)** & **Data Coverage (\%)** & **Mean Coverage Rate (mm/Year)** & **Standard Error at 95\%** (mm/Year)** & **Cumulative Rainfall (mm)** & **Mean Daily Rainfall (mm)** \\ \hline \multirow{3}{*}{FON2} & IM1 & 104 & \(100\) & \(0.1\) & \(\pm 0.1\) & \(185\) & \(1.8\) \\  & IM2 & 187 & \(100\) & \(-0.8\) & \(\pm 0.1\) & \(273\) & \(1.5\) \\  & IM3 & 222 & \(100\) & \(-1.0\) & \(\pm 0.1\) & \(281\) & \(1.3\) \\ \hline \multirow{3}{*}{FON3} & IM1 & 104 & \(100\) & \(0.0\) & \(\pm 0.1\) & \(185\) & \(1.8\) \\  & IM2 & 187 & \(100\) & \(-1.4\) & \(\pm 0.1\) & \(273\) & \(1.5\) \\  & IM3 & 222 & \(100\) & \(-1.5\) & \(\pm 0.1\) & \(281\) & \(1.3\) \\ \hline \multirow{3}{*}{FON4} & IM1 & 104 & \(100\) & \(0.2\) & \(\pm 0.1\) & \(185\) & \(1.8\) \\  & IM2 & 187 & \(100\) & \(-1.1\) & \(\pm 0.1\) & \(273\) & \(1.5\) \\ \cline{1-1}  & IM3 & 222 & \(95\) & \(-1.3\) & \(\pm 0.1\) & \(281\) & \(1.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of the most important duration, mobility, and hydrological data regarding the inactive-motion (IM) stage.  
Figure 16: Average cumulative daily displacements recorded by the automatic inclinometer FON2 (**a**), FON3 (**b**), and FON4 (**c**) probes plotted together with daily rainfall during each inactive-motion (IM) stage identified. In all panels, the fitted linear trends are indicated.  
and computed motion data, during these kinematics phases the landslide motion was interpreted as substantially at rest.
###### Abstract  
Landslide dams, formed by natural disasters or human activities, pose significant challenges for lifespan prediction, which is crucial for effective water conservancy management and disaster prevention. This study proposes a hybrid CNN-Transformer model optimized using the Improved Black-Winged Kite Algorithm (IBKA) aimed at improving the accuracy of landslide dam lifespan prediction by combining local feature extraction with global dependency modeling. The model integrates CNN's local feature extraction with Transformer's global modeling capabilities, effectively capturing the nonlinear dynamics of key parameters affecting landslide dam lifespan. The IBKA ensures optimal parameter tuning, which enhances the model's adaptability and generalization, especially when dealing with small-sample datasets. Experiments utilizing multi-source heterogeneous datasets compare the proposed model with traditional machine learning and deep-learning approaches, including LightGBM, MLP, SVR, CNN-Transformer, and BKA-CNN-Transformer. The results show that the IBKA-CNN-Transformer achieves R\({}^{2}\) values of 0.99 on training data and 0.98 on testing data, surpassing the baseline methods. Moreover, SHapley Additive exPlanations analysis quantifies the influence of critical features such as dam length, reservoir capacity, and upstream catchment area on lifespan prediction, improving model interpretability. This approach not only provides scientific insights for risk assessment and decision making in landslide dam management but also demonstrates the potential of deep learning and optimization algorithms in broader geological disaster management applications.  
landslide dam; CNN; transformer; black-winged kite algorithm; SHAP; disaster prediction 2025
# Simplified Calibration Method for Constant-Temperature Hot-Wire Anemometry
###### Abstract  
This study proposes a unique approach to convert a voltage signal obtained from a hot-wire anemometry to flow velocity data by making a slight modification to existing temperature-correction methods. The approach was a simplified calibration method for the constant-temperature mode of hot-wire anemometry without knowing exact wire temperature. The necessary data are the freestream temperature and a set of known velocity data which gives reference velocities in addition to the hot-wire signal. The proposed method was applied to various boundary layer velocity profiles with large temperature variations while the wire temperature was unknown. The target flow velocity was ranged between 20 and 80 m/s. By using a best-fit approach between the velocities in the boundary layer obtained by hot-wire anemometry and by the pitot-tube measurement, which provides reference data, the unknown wire temperature was sought. Results showed that the proposed simplified calibration approach was applicable to a velocity range between 20 and 80 m/s and with temperature variations up to 15 \({}^{\circ}\)C with an uncertainty level of 2.6% at most for the current datasets.  
hot-wire anemometry; temperature correction; boundary layer +
Footnote †: journal: Applied Physics Letters
# Effect of DEM Used for Terrain Correction on Forest Windthrow Detection Using COSMO SkyMed Data
## 2 Materials and Methods
### Methods  
The methods adopted in this study are summarised in Figure 2, where the same process is carried out for different DEMs, ending up with several windthrow detection results that allow us to compare the impacts of using different DEMs for the terrain correction process. In the next paragraphs, each step is detailed.
## 2 Experimental
### PANI@CNT/CNC SPME MN Extraction Procedure and GC-MS Analysis  
The PANI@CNT/CNC SPME MN was coupled with GC-MS for analysis of caffeine. In a typical procedure, the SPME MN was allowed to equilibrate in a caffeine standard for 10 min with light stirring to allow for sorption of the analyte. The caffeine standards analyzed were in the 97.6-400 mg/L concentration range and were spiked with 75 uL of 5.0 mg/mL isotopically labeled caffeine-(trimethyl-\({}^{13}\)C\({}_{3}\)) as an internal standard. The CNT/CNC SPME fiber was then directly injected into the GC-MS inlet port for 3 min for desorption to take place. A commercial SPME fiber was similarly utilized for comparison.  
For caffeine analysis using the PANI@CNT/CNC SPME MN, the GC-MS inlet port was set to a temperature of 290 \({}^{\circ}\)C with splitless injection mode. Helium was used as the carrier gas at a flow of 1.0 mL/min. The oven temperature was set to 85 \({}^{\circ}\)C initially and ramped up to 180 \({}^{\circ}\)C (30 \({}^{\circ}\)C/min) over 3.0 min, where it was then held until the end of the run (total run time = 13 min). The MS source and quad was set to 230 and 150 \({}^{\circ}\)C, respectively. The MS detector temperature was set at 200 \({}^{\circ}\)C. To evaluate the PANI@CNT/CNC SPME MN stability and reusability under the high temperature of the GC/MS conditions, a 400 mg/L caffeine standard spiked with internal standard was analyzed 15 times, about once every two days, and the SPME MN response data distribution was determined. Each caffeine standard run was analyzed in triplicate.