esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study  
### Project Background  
The shield tunneling section of a metro project in Jinan passes through silty clay, silty soil, fine sand, and medium sand layers. The shield tunneling adopted a soil pressure balance shield machine with a cutterhead diameter of 6600 mm, an outer shield diameter of 6400 mm, and a shield body length of 6000 mm. The segment width is 1200 mm, and the thickness is 300 mm. The tunnel is buried at a depth of 10-14 m. The geological conditions are presented in Figure 6 and Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Statum** \\ **No.** \\ \end{tabular} } & \multirow{2}{*}{**Stratum**} & \multirow{2}{*}{\begin{tabular}{c} **Density.** \(\rho\) \\ **(g/cm\({}^{3}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Moisture** \\ **Content, w** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cohesion, c** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Internal Friction** \\ **Angle, \(\Phi\)** \\ **(\%)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Compression** \\ **Modulus, E** \\ **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Permeability** \\ **Coefficient, k** \\ **(m/d)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{
\begin{tabular}{c} 101 \\ 12 \\ 12 \\ 12 \\ 13 \\ 14 \\ 15 \\ 16 \\ 17 \\ 18 \\ 19 \\ 19 \\ 20 \\ 21 \\ 22 \\ 23 \\ 24 \\ 25 \\ 26 \\ 27 \\ 28 \\ 29 \\ 20 \\ 23 \\ 24 \\ 25 \\ 26 \\ 27 \\ 28 \\ 29 \\ 20 \\ 24 \\ 25 \\ 26 \\ 27 \\ 28 \\ 29 \\ 20 \\ 28 \\ 29 \\ 20 \\ 20 \\ 2 Based on the geological characteristics of the tunnel section, the soil types ahead of the excavation face were classified into three categories: silty clay, silty soil, and composite strata. Geological survey data were used to label the stratum information of 60 ring samples into these three categories. Leveraging the advantages of Support Vector Machine (SVM) algorithms in classification tasks, an SVM-based stratum characterization model was trained to label stratum information for the remaining unmarked samples. The performance of the SVM stratum characterization model was evaluated through cross-validation on the training set and labeling of the test set, achieving a labeling accuracy exceeding 90%. This accuracy level ensures the enhancement of the ground settlement prediction model through stratum classification characterization. The results of stratum classification characterization indicate that approximately 87% of the samples belong to silty clay, about 10% to composite strata, and roughly 3% to silty soil.
## 2 Materials and Methods
### Data  
#### 2.2.1 GPP Datasets  
GPP data used in this paper were provided by the GLASS program [39]. Generally, the _GPP_ can be estimated using the LUE model as follows:  
\[GPP=PAR\times fPAR\times\varepsilon_{\text{max}}\times f, \tag{1}\]  
where _PAR_ is the incident photosynthetically active radiation (MJ m\({}^{-2}\)) per time period (e.g., day or month), _fPAR_ is the fraction of _PAR_ absorbed by the vegetation canopy, \(\varepsilon_{\text{max}}\) is the potential LUE (g C m\({}^{-2}\) MJ\({}^{-1}\) APAR) without environment stress, and \(f\) is a scalar varying from 0 to 1 and represents the effects of temperature, moisture, and other environmental conditions on LUE.  
GLASS-GPP products were estimated using an improved EC-LUE (Eddy Covariance Light Use Efficiency) model, which is derived by satellite data and eddy covariance measurements [16; 17]. There are four kinds of input parameters: NDVI, PAR (photosynthetically active radiation), air temperature, and the Bowen ratio of sensible-to-latent heat flux. NDVI datasets are provided by Advanced Very High Resolution Radiometer (AVHRR), which cover a long period starting from 1979. Net radiation (Rn), air temperature (T), relative humidity (Rh), and photosynthetically active radiation (PAR) from the MERRA (Modern Era Retrospective-Analysis for Research and Applications) archive [40] were used in GPP retrieval. The MERRA dataset was provided by the NASA. Its theory uses a fixed global atmospheric model and data assimilation system to analyze the historical satellite and conventional data records into a continuous global gridded dataset. The spatial resolution was 0.5\({}^{\circ}\) latitude by 0.6\({}^{\circ}\) longitude.  
The GLASS-GPP datasets provides datasets covering from 1982 to 2015 with a spatial resolution of 0.05\({}^{\circ}\) and a temporal resolution of 8 days. We downloaded 1564 scenes with 46 scenes per year, and then used the Savitzky-Golay [41] filter to correct errors and fill gaps. After a series of re-projection, extraction, we acquired the available time series of GPP of the NEC. Aiming to analyzing inter-annual variation of GPP, we calculated accumulated GPP during the growing season (April to October) of each year (denoted as GPPGS, hereafter) and, finally, acquired the 34-year GPPGS time series for further analysis.
## 2 Materials and Methods
### Statistical Model, Validation Tools, and Model-Building Strategies
On the other hand, to verify the prediction skill of the models, each balanced dataset was randomly split using 75% for calibration and the remaining 25% for validation [51]. AUC value (area under the curve) in the ROC (receiver operating characteristics) [52; 53; 54] was employed to evaluate the prediction skill of the model according to [PERSON] and Lemeshow [55]. At the same time, the Youden index optimized score cut-off [56] was obtained from the ROC plots to set confusion matrices and calculate the related validation indices (sensitivity, specificity, and accuracy). Nested applications of the Youden index cut-off were employed to define the different cut-offs of four susceptibility levels in an objective way: S1 (low), S2 (moderate), S3 (high), and S4 (very high). In Figure 4, the model-building strategy employed in this research is synthetically shown. Once the P.A.I. inventory was checked and the relative LIPs extracted, a first model named SUFRA_1 was obtained and validated, both for slide and flow landslides. Thus, each SLU was classified according to the resulting susceptibility score classes. To test the quality of the prediction images in predicting a high-resolution unknown landslide inventory, a second validation was performed in the small Sciara sub-basin (-21 km\({}^{2}\)), where a new systematic inventory for flow and slide was prepared using remote surveys. The Sciara sub-basin was selected because, in light of its geomorphological setting, it is largely representative of the landslide susceptibility in the whole Torto basin area. Then, 30% of unrecognized P.A.I. SLUs were randomly extracted for each susceptibility  
Figure 4: Synthetic scheme of the adopted model-building procedures. class and submitted to remote detection of stable/unstable status with respect to flow and slide movements. Thus, using both the checked P.A.I inventory and the 30% systematically mapped one, two new (slides/flows) SUFRA_2 models were prepared. Finally, the performance of the models was evaluated both with respect to the whole Torto basin (P.A.I. checked inventories) and the Sciara basin.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 4 Digital Control Flow Chart  
As shown in Figure 13a, there are five modules in the digital control strategy, including output voltage sampling, named V_sample; input current sampling, named I_sample; look-up table; digital controller; and digital pulse width modulation generator, named DPWM.
# A Trajectory Generation Method for Multi-Rotor UAV Based on Adaptive Adjustment Strategy
## 3 Method
### B-Spline Curve and Security Guarantee Method
#### 3.1.1 B-Spline-Based Trajectory Representation  
B-spline curve is a trajectory representation method that uses the position of control points as parameters to generate polynomial curve-fitting control points. Because the B-spline curve is the fitting of control points, the safety and smoothness of the trajectory can be adjusted by adjusting the control points. The following parameters determine the B-spline trajectory: (1) degree of B-spline \(p_{b}  
\(N_{i,k}(t)\) is the basic function of the B-spline curve, which can be calculated by Formula (5):  
\[N_{i,k}(t)=\left\{\begin{array}{ll}N_{i,0}(t)&k=0\\ \frac{t-t_{i}}{t_{i+k}-t_{i}}N_{i,k-1}(t)+\frac{t_{i+k+1}-t}{t_{i+k+1}-t_{i+1}} N_{i+1,k-1}(t)&k\geq 1\end{array}\right. \tag{5}\]  
where  
\[N_{i,0}(t)=\begin{cases}1&t_{i}<u<t_{i+1}\\ 0&else\end{cases} \tag{6}\]  
The derivative of the B-spline trajectory can provide the velocity and acceleration of the trajectory. The B-spline trajectory is still B-spline after the derivative. Formula (7) calculates the control points of the velocity and acceleration B-spline curve.  
\[\mathbf{V}_{i}=\frac{p_{b}(\mathbf{Q}_{i+1}-\mathbf{Q}_{i})}{t_{i+p_{b}+1}-t_{i+1}}\text{, }\mathbf{A}_{i}=\frac{(p_{b}-1)(\mathbf{V}_{i+1}-\mathbf{V}_{i})}{t_{i+p_{b}+1}-t_{i+2}} \tag{7}\]
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
###### Abstract  
Three-dimensional semantic segmentation plays a crucial role in accurately identifying terrain features and objects by effectively extracting 3D spatial information from the environment. However, the inherent sparsity of point clouds and unclear terrain boundaries in outdoor mining environments significantly complicate the recognition process. To address these challenges, we propose a novel 3D semantic segmentation network that incorporates spatiotemporal feature aggregation. Specifically, we introduced the Gated Spatiotemporal Clue Encoder, which extracts spatiotemporal context from historical multi-frame point cloud data and combines it with the current scan frame to enhance feature representation. Additionally, the Spatiotemporal Feature State Space Module is proposed to efficiently model long-term spatiotemporal features while minimizing computational and memory overhead. Experimental results show that the proposed method outperforms the baseline model, achieving a 2.1% improvement in mIoU on the self-constructed TZMD_NUC outdoor mining dataset and a 1.9% avg improvement on the public SemanticKITTI dataset. Moreover, the method simultaneously improves computational efficiency, making it more suitable for real-time applications in complex, real-world mining environments. These results validate the effectiveness of the proposed method, offering a promising solution for 3D semantic segmentation in complex, real-world mining environments, where computational efficiency and accuracy are both critical.  
3D semantic segmentation; intelligent mining; spatiotemporal feature; point cloud
## 4 Experimental Results and Discussion
### Effectiveness of the Proposed Method  
#### 4.2.1 SI and CSI  
In the proposed method, the CSI is geometrically configured by multiple SIs to enhance the surface representation. In order to demonstrate the effectiveness of the CSI, we compared the RF models that were trained using CSI and SI information, respectively. For fair comparison, except feature vectors fed to both the RF models, the identical pipeline, i.e., three-class classifier followed by the \(k\)-NN algorithm and re-labeling, was employed. Considering that the dimension of CSI is 200, i.e., \(N_{\rho}=10\) and \(N_{\gamma}=10\), the SI of 200 bins was generated using \(N_{\rho}\), \(N_{\gamma}\), \(\Delta\rho\), and \(\Delta\gamma\) as 20, 10, 0.3, and 0.5, respectively. Note that \(\Delta\rho=0.3\) in the SI of 200 bins to make the spin image coverage area the same as that of CSI in the \(\rho\)-direction. Since \(N_{\rho}\) in SI was doubled, \(\Delta\rho\) was halved.  
Table 3 shows the performance of the two RF models after applying \(k\)-NN. The RF(CSI) has a better performance than the RF(SI) in all metrics. This impressive performance is attributed to the additional features (from the subsampled SI) contained in CSI, which makes the classifier understand the differences between the class labels of the surface regions. The feature importance of the two RF models are illustrated in Figure 12. There are many bins in RF(SI) with less significance compared to RF(CSI). In addition, the important feature bins in RF(SI) are also contained in the feature bins of the RF(CSI), which implies that the original local surface information is preserved in the CSI.  
\begin{table}
\begin{tabular}{c|c c} \hline \hline
**Metrics** & **Using SI** & **Using CSI** \\ \hline Accuracy & 0.9594 & 0.9625 \\ Precision & 0.8914 & 0.8985 \\ Recall & 0.8716 & 0.8832 \\ F1 score & 0.8796 & 0.8889 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the representation performance of SI and CSI. The dimension of both SI and CSI feature vectors is 200.  
Figure 12: Comparison of feature importance of RF(CSI) and RF(SI).
## 3 Results  
### Comparison in a Controlled Environment of the T Values Monitored by the 10 Sensors  
The following are the results of the first test carried out in the climatic chamber to evaluate the behavior of the sensors and the correct difference in relation to T monitoring. Figure 4 shows the lineplot of the T values of the i-th sensor compared with the average trend. The histplot as well as the boxplot of the absolute difference between the value measured by each of the ten sensors and the average reference value are located on the right-hand side.  
Looking at the values monitored by the ten sensors, the results show that the monitored T are almost constant, with the deviations on average tall limited to a range between \(-0.5\,^{\circ}\)C and \(0.5\,^{\circ}\)C, although the extreme values can diverge by more than \(0.5\,^{\circ}\)C, as in the case of the J_T sensor.  
We can correct for the very small difference by applying a simple linear regression over the first 50% of the data. Table 1 shows the linear regression coefficient (m) and the intercept (q) considered for all sensors.  
Figure 4: T raw data—comparison in the climatic chamber: on the left, the lineplot of the i-th sensor when compared with the average values; on the right, the histplot and the boxplot of the absolute difference between values recorded by each sensor and the average values (the white dot indicates the mean value and the black line indicates the median).  
The new comparison with the remaining 50% of data linearized (Figure 5) confirms the possibility of using these 10 sensors in the further course of the experiment without considering a discrepancy in the T measurements due to the possible intrinsic error of the instruments used.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline
**m** & 0.9988 & 1.0001 & 0.9934 & 1.0044 & 1.0004 & 0.9946 & 1.0007 & 1.001 & 0.9978 & 1.0053 \\
**q** & \(-\)0.104 & \(-\)0.07 & 0.0232 & \(-\)0.1031 & 0.0161 & 0.1032 & 0.0544 & 0.0273 & \(-\)0.1339 & 0.2521 \\ \hline \end{tabular}
\end{table}
Table 1: T values—Linear regression. Slope (m) and intercept (q) for all sensors from A to J.  
Figure 5: T data after linear regression (LR)—comparison in the climatic chamber: on the left, the lineplot of the i-th sensor when compared with the average values; on the right, the histplot and the boxplot of the absolute difference between values recorded by each sensor and the average values (the white dot indicates the mean value and the black line indicates the median).
## 2 Short-Term and Long-Term Collision Probability Calculation Models
### Short-Term Collision Probability Calculation Model
#### 2.1.1 Apogee-Perigee Filtering  
When the orbital altitude of the LEO megaconstellation is determined, the constellation satellites will operate in the constellation shell. At this time, the orbit of the space object and the shell have three states: intersection, inclusion, and separation. Space objects with orbits intersecting the shell or that are contained within the shell may collide with the constellation satellites, and space objects with orbits separated from the shell will not collide with the constellation. Therefore, the targets that may collide with the constellation shell can be preliminarily screened in accordance with the geometric relationship for the apogee and perigee of the target orbit. The relationship between the apogee-perigee and the shell of the space target is shown in Figure 2.  
Figure 1: Space targets proximity analysis process.  
Figure 2: Schematic of the relationship between the space target and the shell.  
Assuming that the height of the constellation shell is D, the nominal orbital semi-major axis of the constellation satellites is \(a\), and the apogee and perigee of the space target \(s\) are \(Apog_{s}\) and \(Perg_{s}\), respectively. Given that the eccentricity of the LEO megacons constellation satellites is approximately 0, the nominal orbital radius of the constellation can be approximately equal to \(a\). As shown in Figure 2, the apogee of the space target is compared with the upper bound of the constellation shell, and the perigee of the space target is compared with the lower bound of the constellation shell. If the perigee is greater than the radius of the upper bound of the constellation shell or the apogee is smaller than the radius of the lower bound of the constellation shell, then the target is not a threat and should be eliminated. The apogee-perigee filtering method is expressed as  
\[Perg_{s}>a+D/2||Apog_{s}<a-D/2 \tag{1}\]
## 4 Analysis of Inner Motion Loops
### Absolute Cartesian Position Inner Loop
#### 4.2.3 Implications  
The main difference in these methods is that the Jacobian inverse matrix, \(J^{-1}\), and Jacobian transpose matrix, \(J^{-T}\), appear in the term \(\overline{K}_{px}\) due to the transformation of the active joint stiffness into the cartesian space. This transformation of coordinates implies that the Cartesian stiffness depends on the actual position where the robot is, so it will not be constant during a given trajectory.  
It should be noted that the force also depends on the \(x_{d}\) input reference coming from the possible outer loops. Therefore, in Table 2, the final value of the steady-state force,\(f_{\infty}\), in both cases, is obtained by replacing \(x_{d}\) with one of the outer algorithms already exposed in Table 1.  
Here, \(S_{f}\) is the selection matrix of the force direction, \(R_{pf}\) is the compliance matrix of the force control, and \(I\) is the identity matrix.  
The feedforward of the reference force eliminates the error in rigid robots, which have the possibility to access directly to the motor torques. However, the elasticity of the environment and the inner loop introduce an error. For the case of Jacobian transpose, to obtain zero error, the feedforward should be  
\[f_{\infty}=f_{d}\ \Leftrightarrow F\mathbf{F}=\Big{(}S_{f}K_{e}^{-1}+K_{px}^{-1} \Big{)}f_{d}+S_{f}\kappa_{e}, \tag{45}\]  
and for the case of the Jacobian inverse,  
\[f_{\infty}=f_{d}\ \Leftrightarrow F\mathbf{F}=\Big{(}S_{f}K_{e}^{-1}+\widetilde{K}_{ px}^{-1}\Big{)}f_{d}+S_{f}\kappa_{e}, \tag{46}\]  
In Table 2, the final value for the steady-state force depends on the characteristics of the environment, as its position (\(\mathbf{x}_{e}\)) and stiffness (\(\mathbf{K}_{e}^{-1}\)). Thus, there will be a force error in steady state, unless those values are known with exacttitude to be compensated.  
It is crucial to notice that, in outer loops with feedforward action, it is possible to obtain a zero-force error. To achieve this, the reference force must have the value given by \(F\mathbf{F}\) term. In the case of Jacobian transpose, the feedforward action depends on the environment and the proportional gain of the cartesian position control. In the other case (Jacobian inverse), the control action depends on the environment, and the proportional gain of the joint control transformed into cartesian space. Therefore, it is necessary to know the Jacobian matrix to calculate the stiffness in each sampling period. In summary, in all the versions of the absolute cartesian position control, the characteristics of the environment (stiffness and position) appear. To achieve good tracking, these magnitudes must be known. Moreover, for the inner loop with Jacobian transpose, the force control is uniform in all the working range. For the Jacobian inverse, it is not.
# Malware Analysis in IoT & Android Systems with Defensive Mechanism
## 5 Android Vulnerability Detection
### Android Vulnerability Detection Using System Call Logs  
The model proposed in [29] is used to detect malicious behavior at runtime by executing the apps in a controlled environment for a static time and recording the system call occurring during this time. The most common systems used for the classification of malware using learning methods include the Naive Bayes algorithm, the Random Forest algorithm, and the stochastic descent gradient algorithm. This work showcased a malware detection rate of over 95% with a misclassification rate as low as 8%. The work used a particular Chi-square model to cater to the analysis of malware, which may not serve the accurate traceability aspect of every recorded system call.  
Previous researchers [30] relied upon system calls to perform malware detection. Their method draws upon the fact that malware tends to evolve through an iterative process of borrowing and modifying code from other malware. As a result, malware samples are likely to share common behavioral features. The authors performed an extensive experimental evaluation using a real device on which they executed more than 1000 apps for a total of nearly 20 K runs, which led to deriving an accuracy of over 95%. However, a malware classification technique alone could not judge the validity of the call being traced. There is a need to integrate the approach with malware elimination techniques.  
Social media plays a prominent role as a data generator source that may increase the fear of safe data falling into unsafe hands. Furthermore, the use of low-cost servers as a deployment environment is significantly preferred over high-end servers to support scalability to a greater extent. This phenomenon can be applied to the data emerging via call logs associated with smartphones, irrespective of the nature of the operating system. A similar idea underpins the static methods of previous authors [31] who looked for code segments in multiple apps. A dynamic approach benefits from the fact that the same behavior can be encoded in a number of different, but semantically identical, ways. An analysis of over 7000 Smartphone application binaries showed that the developed schemes detected all instances of plagiarism from a set of real-world malware incidents, with low false-positives and on a scale of millions of applications, using only commodity servers. The meta-information of nearly 158,000 applications from the Android Market was analyzed, and it was found that nearly 29% of applications are likely to be plagiarized. However, the approach is only proven to be efficient for one class of systems, uncovering the malware issues identified with a generic set of classes. Furthermore, the assumption of random methods to perform the analysis is insignificant when applied to real-world data samples in the long run.