esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis
## 2 Methodology Background
### Support Vector Regression
The objective function can be changed as:  
\[Max\sum\limits_{i=1}^{l}\big{[}y_{i}\big{(}\alpha_{i}^{+}-\alpha_{i}^{-} \big{)}-\varepsilon\big{(}\alpha_{i}^{+}+\alpha_{i}^{-}\big{)}\big{]}-\frac{1 }{2}\sum\limits_{i,j=1}^{l}\big{(}\alpha_{i}^{+}-\alpha_{i}^{-}\big{)}\Big{(} \alpha_{j}^{+}-\alpha_{j}^{-}\Big{)}k\big{(}x_{i},x_{j}\big{)} \tag{9}\]  
\[subject\ to \begin{cases}\sum\limits_{i=1}^{l}\big{(}\alpha_{i}^{+}-\alpha_{i}^ {-}\big{)}=0\\ 0\leq\alpha_{i}^{+},\alpha_{i}^{-}\leq c\end{cases}  \tag{10}\]  
where \(k\big{(}x_{i},x_{j}\big{)}\) is the kernel function, so the final fitting function can be expressed as:  
\[f(x)=\sum\limits_{i,j=1}^{l}\big{(}\alpha_{i}^{+}-\alpha_{i}^{-}\big{)}k\big{(} x_{i},x_{j}\big{)}+b \tag{11}\]  
Common kernel functions include linear kernel, polynomial kernel, sigmoid kernel, and Radial Basis Function (RBF) kernel function. Among them, RBF is the most commonly used kernel function in the SVR algorithm, because it can map sample data to high-dimensional space nonlinearly, and can handle the situation that there is a nonlinear relationship between class labels and sample attributes.
## 4 Tracking
### Estimation
#### 4.2.5. PF  
All of the noted Kalman filters deal only deal with the Gaussian noise in the process model and measurement model. Another filter, the so-called PF, can handle the case of non-Gaussian noise [90]. The PF is a combination of the Monte Carlo integral sampling method and the Bayesian filtering algorithm, and its advantage is that it can be used to deal with any nonlinear and non-Gaussian estimation problem. It has been proven that the ideal estimation accuracy can be achieved as long as the number of particles is sufficient. Although the PF algorithm was proposed some time ago, it has not undergone substantive development because it has some problems, specifically the following: With increase of computational iterative PF algorithms, the PF has only one or a few particle weights close to 1, and most of its particle weights are almost 0, with the problem being the particles facing degradation. Moreover, to solve the particle degradation problem effectively, it is necessary to use the method of resampling--that is, the removal of particles of lower weight and the replicating of particles with higher weight [91; 92; 93].  
However, this method destroys the diversity of particles, and makes the particle filter again face another important problem, namely, the lack of samples, which leads to a decrease of the filter's estimation performance. Therefore, researchers have devised many solutions to these problems, including several variants of the particle filter (e.g., sampling importance resampling filter, auxiliary sampling importance resampling filter, and regularized particle filter) within a generic framework of the sequential importance sampling algorithm [94]. In Ref. [95], an enhanced particle swarm optimized particle filter was proposed together with a noninteracting multiple model, which can be efficiently applied in the modern radar maneuvering target tracking field efficiently.  
Table 2 details the performance of all the Kalman filters and the PF, and it can be seen that the classical Kalman filter can be used only for a linear system with a Gaussian process and measurement noise, and, furthermore, that EKF, UKF, and CKF can deal with nonlinear systems accompanied by Gaussian noise. Moreover, the PK can be used in any system, although with high computational complexity.
## 2 Performance Indicators of System Response
### Basic Criteria  
Indicators are based on the integration in time from 0 to \(T\) on some function of the control loop error (\(e\)), i.e., the squared or absolute error. The list of the basic criteria that are commonly used in automatic tuning of the parameters is as follows:  
* Integral-Squared Error (ISE): \[\text{ISE}=\int_{0}^{T}e^{2}(t)\,dt\] (1)
* Integral Absolute Error (IAE): \[\text{IAE}=\int_{0}^{T}|e(t)|\,dt\] (2)
* Integral Time Squared Error (ITSE): \[\text{ITSE}=\int_{0}^{T}t\cdot e^{2}(t)\,dt\] (3)
* Integral Time Absolute Error (ITAE): \[\text{ITAE}=\int_{0}^{T}t\cdot|e(t)|\,dt\] (4)* Integral-Squared Time-Squared Error (ISTSE): \[\text{ISTSE}=\int_{0}^{T}t^{2}\cdot\epsilon^{2}(t)\,dt\] (5)  
To present the impact of the selection of the above criteria, the second-order system is considered:  
\[G_{II}(s)=\frac{k\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}} \tag{6}\]  
where \(k\) is static gain, \(\zeta\) is the damping factor, and \(\omega_{n}\) is the natural frequency. In Figure 1, all of the above-mentioned indicators are presented in the damping factor domain for the following parameters: \(k=1\) and \(\omega_{n}=1\).  
The red markers are used to mark the minimal values of the plots because automatic tuning using a nature-inspired optimization algorithm minimizes one of these indicators. Comparing the additional time in the integral (ISE, ITSE, and ISTSE), it can be seen that the damping factor moves towards a higher damping factor. It should be noted that a higher power of time allows us to reduce the oscillations and settling time because of the higher importance of higher time values. Furthermore, it should be noted that the minimal values differ significantly. In such a case, the weighting factor in objective functions must be selected to the used integral indicator due to different orders of magnitude.
## 3 Preliminaries
### Medium Access Control  
In PMRC-based WSNs, a sensor node competes the media for data transmission with all the nodes within its transmission range. A simple CSMA-based MAC scheme is implemented to avoid collisions. In this MAC scheme, when a sensor node has one or more data packets to transmit, it monitors the channel until the channel is idle before initiating the transmission. After sensing an idle channel, the sensor node generates a random backoff interval for an additional deferral time before transmission. The backoff counter is decreased by slot time as long as the channel is idle. When a transmission is detected on the channel, the backoff process is terminated and the node starts to monitor the channel again and repeat the above process. The node will transmit the data packet when the backoff counter reaches zero. The backoff time is a random number with a discrete uniform distribution between \(0\) and _cw_-1, where _cw_ is the size of the contention window and _cw_ is assumed to be a constant [21].
## 4 Experiments
### Visualization  
We present the point cloud super-resolution visualization results of TSE-UNet. As shown in Figure 6, the first column is the visualization of sparse point clouds as input, the second column is the corresponding dense point clouds ground truth, and the third column is the point cloud super-resolution prediction result of TSE-UNet. As can be seen from the figure, compared with the ground truth of dense point clouds, the upsampled point cloud had some noise points, but it still generated more effective points than the original sparse point cloud. This could provide more abundant environmental information for autonomous vehicles or intelligent robots.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Components**} & \multirow{2}{*}{**PSNR**} & \multirow{2}{*}{**SSIM Loss**} \\  & **T** & **TE** & & & \\ \hline UNet & \(\times\) & \(\times\) & \(\times\) & 25.11 & 30.99 \(\times 10^{-5}\) \\ Variant 1 & ✓ & \(\times\) & \(\times\) & 26.97 & 11.89 \(\times 10^{-5}\) \\ Variant 2 & ✓ & ✓ & \(\times\) & 27.38 & 9.16 \(\times 10^{-5}\) \\ TSE-UNet & ✓ & ✓ & ✓ & **27.52** & **8.38 \(\times\)10\({}^{-5}\)** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments on key components of TSE-UNet using the CARLA32-12 dataset. “T” represents the utilization of temporal information, specifically, the 16 sequential frames used in TSE-UNet; “TE” represents the temporal information enhancement module, and “SE” signifies the spatial information enhancement module. “\(\times\)” and “\(\checkmark\)” indicate non-usage and usage of the respective modules. Higher PSNR values are preferable, while lower SSIM losses indicate improved performance. Optimal values are in bold.  
Figure 6: The visualizations of super-resolution. The top pictures are from the CARLA32-128 dataset. The bottom pictures are from the Ruby32-128 dataset. From left to right, they are the input 32-line sparse point cloud, the ground truth of the 128-line dense point cloud, and the model’s predicted super-resolution result.
## 2 Materials and Methods
### In Vitro Mineralization andentin Tubule Occlusion  
Bioactivity of the ACMFP particles, in terms of their propensity to transform/crystallize into hydroxyapatite or fluorapatite in biological conditions, was evaluated by submerging the particles in a simulated saliva solution composed as described in Table 1 (pH 7). Particles were left static in the solution for 18 h at 37 \({}^{\circ}\)C, then filtered, dried, and characterized in SEM and by XRD. Phase composition analysis of the crystallized material was performed using Rietveld refinement (Profex v4.3.5 software, an open source software, available from www.profex-xrd.org, accessed on 3 May 2023), quantifying hydroxyapatite and fluorapatite.  
In vitro mineralization and dentin tubule occlusion testing were performed based on a previously described protocol [16]. Dentin specimens were sectioned from extracted human molars without any caries or anatomical defects using a water-cooled low-speed saw (Buehler IsoMet 2000). The sectioning was performed in the buccolingual plane, yielding specimens with approximate dimensions of 1 \(\times\) 8 \(\times\) 8 mm. Utilization of human molars in the study was performed in compliance with guidelines from the Swedish Ethical Review Authority (2016/039). After sectioning, the dentin specimens were etched in 30% phosphoric acid for 15 s to remove the smear layer and expose the tubules, followed by a thorough rinsing in deionized water. The dentin specimens were then subjected to a mineralization treatment with formulas containing glycerol and either 5% ACMFP particles or 5% ACMP particles (without fluoride) for comparison. A blank treatment formula without any mineralizing particles was also included for reference. Each treatment formula was applied to two dentin specimens twice daily for seven days, using a soft-bristled toothbrush and manual brushing with light hand pressure for approximately 30 s at each application. In between applications, the dentin specimens were stored in a simulated saliva solution (Table 1) at 37 \({}^{\circ}\)C, which was exchanged daily. Upon completion of the treatment, one specimen of each treatment formula was subjected to an acid challenge by swirling in a 2% citric acid solution (pH 2) for 30 s. The specimens were then rinsed, vacuum dried, mounted on sample stubs, and sputter coated with a thin conductive Au/Pd layer to allow SEM evaluation. One dentin specimen for each treatment cycle was evaluated, for a total of six specimens. Cross-sectional images of the dentin tubules were taken after manually breaking the specimens.
# A New Modeling Method of Angle Measurement for Intelligent Ball Joint Based on BP-RBF Algorithm
###### Abstract  
The rotation orientation and the angle of precision of an intelligent ball joint cannot be automatically obtained in passive motion. In this paper, a new method based on a Hall sensor with a permanent magnet (PM) is proposed to identify the spatial rotation orientation and angle. The basic idea is to embed a PM on a ball while the Hall sensors are arrayed into the ball socket. When the ball rotates, the Hall sensor array detects the variation of the magnetic induction intensity in space. By establishing a mathematical model between the variation of the magnetic induction intensity and the orientation and angle of rotation, the rotation angle in the space where the ball is located can be inversely solved. The establishment of the theoretical model is based on the theory of the equivalent magnetic charge method, which has a few native defects that cannot be overcome by itself. This paper presents the relationship between the magnetic induction intensity change and the rotation angle of the ball in space, which was constructed by an artificial neural network (ANN) and will simplify the mathematical model, shorten the operation time, and improve the efficiency of real-time detection. Based on the simulation analysis, the optimal matching scheme between the PM and the magnetic effect sensor was determined, and the structural parameters of the ball joint prototype were optimized. The data training and comparison test of the neural network model were completed on a self-developed calibration device. The experimental results show that for a \(\pm 20^{\circ}\) measurement range, the average errors of the uniaxial measurements are \(1^{\prime}51^{\prime\prime}\) and \(1^{\prime}55^{\prime\prime}\) on the two axes, respectively. At present, the measurement accuracy of the prototype is still relatively low; however, this idea of modeling based on ANN removes the shackles of mathematical modeling, reminding us that we can consider the design of sensors or complete geometric measurement modeling from a new perspective.  
ball joint; rotation angle in space; Hall sensor; BP artificial neural network; RBF artificial neural network +
Footnote †: journal: Applied Sciences
## 3 Analysis of TEE Usage in Edge Intelligence and IoT
### Penglai Enclave  
Penglai Enclave is a software-hardware co-designed TEE technology aimed at enhancing the security and scalability of security-critical applications in cloud environments, specifically on RISC-V platforms. Penglai introduces two key hardware primitives: the Guarded Page Table and the Mountable Merkle Tree (MMT). The Guarded Page Table enables fine-grained, page-level memory isolation, ensuring that unauthorized software cannot access secure memory. MMT provides scalable memory encryption and integrity protection, supporting large secure memory regions. The secure monitor operates at the highest privilege level (e.g., machine mode in RISC-V), managing enclave creation, enforcing memory isolation and maintaining security guarantees.  
Additionally, Penglai employs the Shadow Enclave and Shadow Fork mechanisms, which allow for the fast creation of multiple secure instances, significantly reducing the latency of memory initialization. The system can dynamically scale to thousands of concurrent secure instances, supporting up to 512 GB of encrypted memory with only approximately 5% overhead for memory-intensive applications. This framework effectively addresses the limitations of traditional TEE systems in terms of memory protection and scalability, making it particularly suitable for large-scale, dynamic and serverless cloud computing scenarios.
## 2 Materials and Methods
### The Chemical Synthesis and Structural Characterization
#### 2.1.2 Synthesis of 9\(\alpha\)-Hydroxy-17-oxooestra-1,3,5(10)-trien-3-yl Acetate (**4**)  
The synthesis was carried out in accordance with a protocol previously described [29; 30]. Briefly, H\({}_{2}\)O (3.94 mL), NaHCO\({}_{3}\) (1.142 g), acetone (3.16 mL) and TEAC (2 mg) were added to a solution of compound **2** (156.18 mg, 0.5 mmol) in CH\({}_{2}\)Cl\({}_{2}\) (3.56 mL). Then, OxoneTM (2.36 g) was added every 15 min for 2 h followed by stirring on ice for 7 h. After, the crude was diluted in ethyl acetate (EA), washed with 10% Na\({}_{2}\)SO\({}_{3}\) and H\({}_{2}\)O, dried over anhydrous Na\({}_{2}\)SO\({}_{4}\) and filtered under suction. After solvent evaporation, purification was performed by column chromatography (eluent: EA/petroleum ether (PE) 40-60 \({}^{\circ}\)C, 1:4) to yield compound **4**[29; 30] (beige solid, 90 mg, 55%). IR (\(\
u_{\rm max}\), cm\({}^{-1}\)): 800, 902, 1013, 1196, 1372, 1448, 1606, 1723, 2832, 2938, 3060, 3351, 3564; \({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\): 0.88 (s, 3H, C18-CH\({}_{3}\)), 2.26 (s, 3H, COCH\({}_{3}\)), 6.83 (d, 1H, \(J\) = 2.4 Hz, C4-H), 6.89 (dd, 1H, \(J_{1}\) = 8.5 Hz, \(J_{2}\) = 2.4 Hz, C2-H), 7.52 (d, 1H, \(J\) = 8.5 Hz, C1-H); \({}^{13}\)C-NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\): 13.1, 20.2,21.3, 21.6, 27.8, 29.6, 32.4, 36.1, 41.3, 43.3, 47.8, 70.2, 119.8, 122.5, 126.7, 138.6, 139.3, 150.2, 169.9, 220.7.  
1.3 Synthesis of 9\(\alpha\)-Hydroxy-11\(\beta\)-nitrooxy-17-oxoestra-1,3,5(10)-trien-3-yl Acetate (**5**)  
The synthesis was carried out in accordance with a protocol previously described [22]. Briefly, a solution of CAN (2.52 g, 4.6 mmol) in 2 mL of H\({}_{2}\)O was added dropwise to a solution of compound **2** (317.6 mg, 1 mmol) in acetic acid (15 mL). The resulting orange solution was maintained under vigorous stirring for 6 h at r.t. and then was diluted two-fold with H\({}_{2}\)O and extracted with EA. The organic solution was then washed with saturated NaHCO\({}_{3}\) solution, brine, and H\({}_{2}\)O, dried using anhydrous Na\({}_{2}\)SO\({}_{4}\), and filtered under suction. After solvent evaporation, a purification with column chromatography (eluent: gradient of EA/PE 40-60 \({}^{\circ}\)C, 1:4 to 1:2) and recrystallization (MeOH) was performed to afford compound **5**[22] as a light brown solid (128.7 mg, 34%). IR (\(\
u_{\text{max}}\), cm\({}^{-1}\)): 853, 970, 1050, 1151, 1207, 1279, 1370, 1493, 1633, 1728, 1750, 2893, 2962, 3447; \({}^{1}\)H-NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\): 1.00 (s, 3H, C18-CH\({}_{3}\)), 2.26 (s, 3H, COCH\({}_{3}\)), 5.79 (t, 1H, \(J\) = 3 Hz, CHONO\({}_{2}\)), 6.89 (brs, 1H, C4-H), 6.91 (d, \(J\) = 8.3 Hz, 1H, C2-H), 7.28 (d, 1H, \(J\) = 8.3 Hz, C1-H); \({}^{13}\)C-NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\): 15.3, 20.1, 21.3, 21.3, 29.7, 31.3, 35.5, 38.2, 42.2, 46.4, 71.9, 81.4, 120.5, 123.1, 126.3, 134.9, 139.9, 150.6, 169.7, 217.9.
## 6 Evaluation Experiments
### Experiment 1 Movement Task
#### 6.1.4 Experiment Results and Analysis  
The experimental results on the movement task are shown in Table 2, and the statistical analysis results are shown in Table 3 and Figure 9.  
Figure 8: Movement task.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Source Area**} & \multicolumn{2}{c}{**Target Area**} & \multicolumn{2}{c}{**d**} & \multicolumn{2}{c}{**t**} & \multicolumn{2}{c}{**Acc**} & \multicolumn{2}{c}{**Score**} \\  & **(row,col)** & **(row,col)** & **(row,col)** & **d** & **t** & **Acc** & **Score** \\ \hline \multirow{10}{*}{Desktop Console} & (1,4)  
In terms of interaction time, the keyboard and mouse-based desktop console is closer to the touch-screen-based Surface, while the gesture-based interaction takes longer. Although the user is more familiar with the operation of the traditional keyboard and mouse, the physical distance for the user's finger to move on the Surface touch screen is shorter, so the time consumption of the two interaction methods is relatively close. Since gesture interaction using double precision technology consumes more time in precise control mode, the total time consumption is significantly higher than other methods. We believe that users can improve interaction efficiency after practicing interactive gestures for a period of time.  
In terms of accuracy, the accuracy and stability of the desktop console method is significantly better than other methods. The results of Surface and gesture interaction are relatively close. The accuracy rates of these two interaction methods for multiple rounds of operation are quite different, and the stability is low. We believe this is due to the user's low stability of body control. After combining double-precision pointing technology, the accuracy and stability of gesture interaction have been significantly improved.  
The score is a combination of interaction time and accuracy. The desktop console and Surface scored higher due to their advantage in interaction time. Affected by the accuracy rate, the score of gesture interaction is significantly lower than other methods. While gesture interaction combined with double-precision pointing technology improved accuracy, the score decreased slightly due to the overall time-consuming increase. This shows that compared with other interaction methods, gesture interaction in movement task does not have advantages in interaction efficiency.  
In addition, we counted the time consumption of 10 groups of double-precision gesture interactions, as shown in Figure 10. It can be seen that the time consumption of the precise control mode accounts for about 32.5% of the total time consumption Due to the introduction of the precise control mode, the average time spent on interaction has increased by 54%, and the average accuracy rate has increased by 19.5%, which is close to the accuracy rate of desktop console and better than Surface touch operations. This shows that gesture interaction using double-precision technology is feasible in application scenarios where there is no limit on the interaction time.
## 3 Results
### Case Study in Slovakia
#### 3.1.3 Brief Description of the Railway Operation between Sladkovicovo and Senec  
The line from Sladkovicovo to Senec is considered to be very busy based on the analysis of a timetable fragment. Regional suburban trains play an important role because the line lies in the agglomeration of the capital. Regional trains (Os) run in a two-hour cycle from 4:00 to 22:00. During rush hour, more passenger trains and regional express (Rex) trains are engaged. During 24 h, a total of 25 trains of the Os and/or Rex category pass the Sladkovicovo-Senec section. International long-distance passenger traffic is represented by ten Eurocity (EC) trains on the Budapest-Prague (Warsaw) route operating in a two-hour cycle. National long-distance passenger transport is represented by ten fast trains (R) operating in a two-hour cycle on the Bratislava-Banska Bystrica route. Considering freight transport, the strongest representation has running freight trains (Pn) (international or national) supplemented with international express freight trains (Nex). In total, 52 freight trains pass in the Sladkovicovo-Senec section over 24 h. Local service freight trains (Mn) are less represented and occur only on one regular train path.  
The following abbreviations are used for each type of train:  
* Ex--Eurocity and Express train (passenger);
* R--Fast train (passenger);
* Os--Regional train (passenger);
* Pn--Running freight train;
* Mn--Local service freight train.  
In our calculations, we operated with the 98 trains listed in Table 1. The number of trains was determined by analyzing the graphic timetable in the selected section of the line.  
We did not consider any changes in the direction or height profiles of the line when proposing the implementation of the ETCS L3 system on the given section of the ZSR 130 line. Additionally, we did not consider any changes in the area of the railway base or superstructure. The scope of traffic also remained unchanged.