esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Quasi-Static Finite Cylindrical Cavity Expansion Model
### Analytical Solution for Cavity Pressure  
#### 2.2.1 Elastic-Plastic Response Stage (\(\mathrm{r_{c}<r_{c1}}\))  
* Elastic region (\(r_{\mathrm{p}<r<r_{t}}\)) With (2) and (3), (1) transforms to \[\frac{d^{2}u}{dr^{2}}+\frac{1}{r}\frac{du}{dr}-\frac{u}{r^{2}}=0.\] (5)  
Figure 1: Schematic diagram of quasi-static finite cylindrical cavity expansion model: (a) elastic–plastic stage; (b) plastic stage.  
Since \(\sigma_{r}\) at the lateral boundary of the target is 0 and Equation (4) holds at the elastic-plastic interface, integration gives \[u(r)=\frac{(1+b)(1+v)Y_{t}}{(2+b)E}\Bigg{[}\frac{r_{p}^{2}}{r}+(1-2v)\frac{r_{p}^{ 2}}{r_{t}^{2}}r\Bigg{]},\] (6) \[\sigma_{r}(r)=\frac{(1+b)Y_{t}}{(2+b)}\Bigg{(}\frac{r_{p}^{2}}{r^{2}}-\frac{r_{ p}^{2}}{r_{t}^{2}}\Bigg{)}.\] (7)
* Plastic region (\(r_{\rm c}<r<r_{\rm p}\)) Substituting Equation (4) into Equation (1) yields \[\frac{d\sigma_{r}}{dr}+\frac{2(1+b)Y_{t}}{(2+b)r}=0,\] (8) and integration gives \[\sigma_{r}(r)=\frac{(1+b)Y_{t}}{(2+b)}\Bigg{(}1-\frac{r_{p}^{2}}{r_{t}^{2}}+ \ln\frac{r_{p}^{2}}{r^{2}}\Bigg{)},\] (9)
* Interface Conditions Assuming that the plastic region is incompressible, it can be concluded that \[r_{p}^{2}-r_{c}^{2}=\big{(}r_{p}-u\big{(}r_{p}\big{)}\big{)}^{2}.\] (10) Substituting Equation (6) into Equation (10) yields \[r_{c}^{2}+D^{2}\Bigg{[}r_{p}^{2}+2(1-2v)\frac{r_{p}^{4}}{r_{t}^{2}}+(1-2v)^{2 }\frac{r_{p}^{6}}{r_{t}^{4}}\Bigg{]}=2D(1-2v)\frac{r_{p}^{4}}{r_{t}^{2}}+2 Dr_{ p}^{2},\] (11) \[D=\frac{(1+b)(1+v)Y_{t}}{(2+b)E}.\] (12) With \(u(r_{t})=r_{t}-r_{t0}\), (6) transforms to \[\frac{2(1+b)(1-v^{2})Y_{t}}{(2+b)E}r_{p}^{2}=r_{t}^{2}-r_{t0}r_{t}.\] (13) If \(r_{\rm c}=r_{\rm c1}\), then \(r_{\rm p}=r_{\rm t}\). From (10), and ignoring higher order terms in \(u(r_{\rm p})\) according to [28], \[r_{c1}^{2}=2u(r_{t1})r_{t1},\] (14) where \(r_{\rm t1}\) is the target radius if \(r_{\rm c}=r_{\rm c1}\). From (6), (13), and (14), \(r_{\rm c1}\) can be obtained \[r_{c1}=\frac{2\sqrt{(1+b)(2+b)(1-v^{2})EY_{t}}}{(2+b)E-2(1+b)(1-v^{2})Y_{t}}r_ {t0}.\] (15)
## 5 Simulation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{2}{c}{**Gamma Prior**} & \multicolumn{2}{c}{**Quasi-Density Prior (\(m=0.5\))**} & \multicolumn{2}{c}{**Quasi-Density Prior (\(m=1\))**} \\ \cline{3-10} \(n\) & \(t\) & \(R(t)\) & \(\hat{R}_{QEL}(t)\) & \(\hat{R}_{QEL}(t)\) & \(\hat{R}_{QEL}(t)\) & \(\hat{R}_{QEL}(t)\) & \(\hat{R}_{QEL}(t)\) & \(\hat{R}_{QEL}(t)\) \\ \hline  & 0.5 & 0.89442719 & 0.88776850 & 0.88776648 & 0.89022165 & 0.88982998 & 0.89191680 & 0.89153098 \\  & 1.0 & 0.70710678 & (0.00016976) & (0.00016982) & (0.00035915) & (0.00354834) & (0.00041245) & (0.00325973) \\  & 1.0 & 0.70710678 & 0.69200795 & 0.69196205 & 0.69792246 & 0.69687672 & 0.70202359 & 0.70099008 \\  & & & (0.00099295) & (0.00099609) & (0.00226429) & (0.01860601) & (0.00260862) & (0.01719563) \\  & 1.5 & 0.55470020 & 0.53619511 & 0.53602508 & 0.54395560 & 0.54283930 & 0.54935541 & 0.54780186 \\
30 & 2.0 & 0.44721360 & 0.42836343 & 0.42802580 & 0.43679984 & 0.43485953 & 0.44268760 & 0.44075660 \\  & & & (0.00201945) & (0.00205296) & (0.00532877) & (0.03098345) & (0.00617821) & (0.02895350) \\  & 3.0 & 0.31622777 & 0.29950277 & 0.29883663 & 0.30789776 & 0.30549234 & 0.31378419 & 0.31137683 \\  & & & (0.00197875) & (0.00204472) & (0.00582987) & (0.02636404) & (0.00679263) & (0.02483017) \\  & 4.0 & 0.24253563 & 0.22835483 & 0.22743407 & 0.23620124 & 0.23354352 & 0.24172242 & 0.23905083 \\  & & & (0.00170532) & (0.00179024) & (0.00546613) & (0.02050066) & (0.00639343) & (0.019417727) \\ MISE & & & (0.00142980) & 0.00146363 & 0.00391127 & 0.02146801 & 0.00454377 & 0.2007302 \\  & 0.5 & 0.89442719 & 0.89245776 & 0.89254759 & 0.89255407 & 0.89294560 & 0.89305929 & 0.89344847 \\  & & & (0.00000287) & (0.00000287) & (0.00000571) & (0.00032292) & (0.00000354) & (0.00033805) \\  & 1.0 & 0.70710678 & 0.70256609 & 0.70256229 & 0.7028482 & 0.70378022 & 0.70405675 & 0.70500904 \\  & & & (0.00001726) & (0.00001729) & (0.00003423) & (0.00203113) & (0.00002126) & (0.00212839) \\  & 1.5 & 0.55470020 & 0.54904411 & 0.54902961 & 0.54941704 & 0.55068211 & 0.55105151 & 0.55231396 \\  & & & (0.00003059) & (0.00003075) & (0.00006056) & (0.00377552) & (0.000306767) & (0.00396038) \\
100 & 2.0 & 0.44721360 & 0.44137270 & 0.4413419 & 0.44180982 & 0.44319053 & 0.44360155 & 0.44498085 \\  & & & (0.00003695) & (0.00003730) & (0.000073703) & (0.000745742) & (0.00004548) & (0.00494996) \\  & 3.0 & 0.31622777 & 0.31093628 & 0.31087603 & 0.31142063 & 0.31279238 & 0.31322272 & 0.31459535 \\  & & & (0.00003762) & (0.00003836) & (0.00007416) & (0.00518517) & (0.00004627) & (0.00545234) \\  & 4.0 & 0.24253563 & 0.23798035 & 0.23789508 & 0.23846706 & 0.23973927 & 0.24016162 & 0.24143623 \\  & & & (0.000033337) & (0.00003437) & (0.00006566) & (0.00484708) & (0.00004103) & (0.00510286) \\ MISE & & & 0.00002644 & 0.00002682 & 0.00005223 & 0.00348654 & 0.00003254 & 0.00366283 \\ \hline \hline \end{tabular}
\end{table}
## 2 Materials and Methods
### Study Cohort  
Included were ACHD with any form of manifest HF, history of HF, or significant risk for future HF due to abnormal ventricular function or anatomy.  
In our study, the phrase \"significant risk for heart failure (HF)\" refers to ACHD who, despite not currently meeting clinical criteria for manifest HF, present with structural or functional abnormalities known to predispose them to HF. This particularly includes patients with systemic ventricular dysfunction (even if asymptomatic), significant residual lesions (e.g., valvular regurgitation or obstruction), severe pulmonary hypertension, chronically elevated central venous pressure, or a history of arrhythmias, protein-losing enteropathy, or Fontan circulation.  
While no single risk score was applied, inclusion was guided by the consensus of experienced ACHD specialists as outlined in the Pathfinder-CHD registry protocol [8].  
The registry provides comprehensive documentation of the underlying CHD, type of HF, as well as medical, surgical, and/or interventional treatments, and comorbidities.  
Inclusion criteria for the current analysis were as follows: Adults (\(\geq\)18 years) with a confirmed diagnosis of CHD--either manifest HF (according to ESC criteria), a documented history of HF, or considered at high risk due to systemic ventricular dysfunction, residual lesions, severe pulmonary hypertension, Fontan physiology, or other clinically relevant indicators of HF vulnerability.  
Exclusion criteria were the inability to provide informed consent and cognitive impairment that prevented participation in study procedures.  
Patients were consecutively enrolled during routine visits or hospitalizations at participating tertiary ACHD centers. To avoid bias, no preselection was applied beyond the criteria above. Data collection was performed using a standardized electronic case report form and included demographic information, cardiac history, medications, anthropometric parameters (BMI, metabolic body weight), functional class, surgical/interventional history, and comorbidities.  
Based on the BMI, the study population was categorized into mild (BMI 17.00-18.49), moderate (BMI 16.00-16.99), and severe underweight (BMI < 16.00). However, BMI fails to take account of the body's composition of fat, water, muscle, and bone, or where the fat is distributed. While MBW was primarily used in animal physiology, it is increasingly applied in human medicine to provide more precise insights into energy requirements, especially in critical clinical conditions. Therefore, in addition, the metabolic body weight (MBW), which describes the relationship between body weight and metabolic rate was calculated [9; 10; 11].
## 2 Experimental Section/Methods
### Synthesis of POSS-MA  
Aminopropylboostly POSS (4.373 g, 5 mmol) and triethylamine (0.557 g, 5.5 mmol) were dissolved in 50 mL of anhydrous dichloromethane. Simultaneously, methacryloyl chloride (0.575 g, 5.5 mmol) were diluted in 15 mL of anhydrous dichloromethane and transferred into a dropping funnel under atmospheric pressure. A nitrogen atmosphere protection was set up, and it was cooled to 0 \({}^{\circ}\)C in an ice-water bath. After the addition, the reaction mixture was stirred at room temperature for 12 h [31; 32]. The organic phase was washed three times with saturated sodium bicarbonate solution, and the solvent was removed by rotary evaporation to obtain a crude product (ca. 4.0 g). Subsequently, purification was carried out using a gel chromatography column (PE:DCM = 1:1) with a yield of 80%. \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)): \(\delta\) ppm: 5.66 (s, 1H), 5.31 (s, 1H), 3.31 (t, 2H), 1.96 (s, 3H), 1.90-1.79 (m, 9H), 0.95 (d, 42H), 0.60 (t, 16H).
## 4 Performance Evaluation
### Experimental Results on Real Data Sets  
Throughput as the number of continuous operations \(k\) is increased: Figure 11 shows the throughput on a real data set as \(k\) is increased from 1 to 1 million. Here, we use _1D-Geo_ as the data set. Overall trends are similar to the results on synthetic data sets as presented in Figure 9. That is, the performance of WR-Store is quite constant while the other key-value stores require much initial cost. Therefore, WR-Store is much efficient for small \(k\) while RocksDB and LevelDB outperform WR-Store for only some values of large \(k\).
## 3 The Proposed Data Integration Approach for Agricultural Open Data Platforms
### Processing and Storage Layer (PSL)  
This layer corresponds to decomposing raw sensor data streams, cleaning raw sensor data, modeling objects and storing data. PSL enables transforming raw sensor data into an appropriate format mapped to object models and storing these data in varied database options. PSL is composed of four sublayers: data decomposition engine (DDE), data cleansing engine (DCE), Object Modeling Engine (OME) and data storage engine (DSE). Next, these four sublayers are described in detail.
# Improvement on Flux Weakening Control Strategy for Electric Vehicle Applications
## 6 Experimental Setup and Performance Comparison  
A suitable test bed was implemented to test the proposed control strategies. The test bed was composed by a torquemeter, a power analyzer, the 12 slot 10 pole motor under test, a high current drive with programmable DSP, a battery pack and the active brake with a moment of inertia comparable to that of the actual vehicle. The complete setup is reported in Figure 18. The induction machine used as active brake was controlled by FOC control with appropriate position feedback with a speed close-loop. The motor under test was tolled with a current (torque) loop employing the proposed control schemes.  
An ARM architecture DSP (STM32F407 VET6) was employed to test the proposed control schemes implementing them in C code language. A set of hall-effect current transducers (LEM HC5F800-s) were used to measure the current in the control loops, a digital absolute magnetic encoder was employed to acquire the rotor position. The motor is cooled by natural air convection.  
The control setpoints and measured quantities were acquired and logged by means of by STM-Studio software.  
Figure 19 compares the experimental results of the feed-forward open loop approach. The enhanced outer voltage and the enhanced Jahns' torques and powers are reported  
Figure 17: Experimental results of the improved Jahns’ control scheme [16]: speed profile and \(i_{d}\), \(i_{q}\) currents signals.  
Figure 18: Test bench set up for experimental validation of the proposed control methods.  
versus mechanical speed and it can be clearly noticed that the enhanced Jahns' control strategy - outclasses either FF method, or outer voltage method.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 4 MSGCN Framework
### Subgraph Convolution  
This part is to get the representation vector of each node in each subgraph. Since the operation of each subgraph is the same, we can take the subgraph \(G_{i}\) as an example. We use a multi-layer graph convolution network for the subgraph \(G_{i}\), as shown in the following equation  
\[X^{(l+1)}=\sigma(\hat{A}_{i}X^{(l)}W^{(l)}), \tag{3}\]  
where \(l\) is the layer of the graph convolutional network, and the total number \(L\) is usually 2. \(X\), the content information matrix, is shared among all the subgraphs. The input of the model is \(X^{(0)}=X\). The output of the model is \(X^{(L)}=Z_{i}\). \(A_{i}\) is the adjacency matrix of the subgraph \(G_{i}\). \(\sigma()\) is the activation function. We usually select \(ReLU(x)=Max(0,x)\) in the middle layer. The activation function for the last layer is a softmax function. \(W^{(0)}\in R^{|V|\times|V|}\) and \(W^{(1)}\in R^{|V|\times|V|}\) are weight matrices that are used at each level and they are learnable parameters. Finally, the two-layer graph neural network for \(G_{i}\) is as follows:  
\[Z_{i}=softmax(\hat{A}_{i}ReLU(\hat{A}_{i}XW^{(0)}))W^{(1)} \tag{4}\]  
Figure 2: MSGCN model: We first preprocess the node content information, so that they have the same dimension. Then the original graph can be decomposed with respect to the type of edges. For each subgraph, we use graph convolution to get the node representations. Finally, an aggregation operation is applied to get the final representations.  
\(Z_{i}\in R^{|V|\times d}\) is the node representation obtained by the subgraph \(G_{i}\) through the two-layer graph convolution network, aggregating the information of neighbors which are associated with the \(i\)-th edge type.
## 5 Implementation and Evaluation
### Testing of Smart Broker Components
#### 5.2.1 Preparation of a Correlation Matrix  
We used real-world IoT datasets from Kaggle, for which the description can be found at [53]. This dataset is mainly prepared to predict air quality. It consists of five sensors recording different air parameters along with temperature and humidity sensors. It records the date and time of each sensor reading. The date and time of sensor readings start on 10 March 2010, at 6 p.m., and continue until 31 December 2010, spanning 10 months of recording. All records from the sensors are from the same type of machine (meaning from the same publisher). Moreover, the specified publishing interval is 1 h between readings.  
However, for testing purposes only, the speed of feeding these data to the smart contracts is increased to a 15 s interval, which becomes the \(\delta\). The original dataset does not contain any noise or drift from the 15 s. We introduced the noise and delays for experimental purposes. The correlation matrix for testing TDQAM is prepared based on the data from this dataset and stored in the ledger.
## 4 Results and Discussion
### CDF Comparison
The results of the experiment were then compared between the CNN method and the proposed method, which includes CNN and SVM. At 10,000 steps, Figure 21 shows that the AHE dataset outperforms the HE dataset for the training accuracy and their training loss. In addition, Table 6 provides the proposed defect detection scheme results on the training and testing of the HE and AHE datasets. It shows the CNN-SVM method is more accurate than the CNN method alone. As shown, the combined approach CNN-SVM outperforms the CNN method with an accuracy of 97% compared to the CNN method's accuracy of 92.96%, which improves accuracy, and the CNN-SVM is extremely effective in classification. Moreover, the confusion matrix is shown in Figure 22 for the proposed scheme using the AHE dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Testing Set**} \\ \hline \multirow{2}{*}{**Dataset**} & **Classifier** & **Percent** & **Percent** & **Percent** & **Percent** & **Percent** & **Percent** \\  & **Method** & **Sensitivity** & **Specificity** & **Accuracy** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline \multirow{2}{*}{HE} & CNN & 94.86\% & 91.23\% & 90.25 & 90.43\% & 87.16\% & 88\% \\  & CNN-SVM & 96.72\% & 93.96\% & 94.16\% & 93.83\% & 88.43\% & 90.16\% \\ \hline \multirow{2}{*}{AHE} & CNN & 99\% & 93.96\% & 92.96\% & 92.25\% & 89\% & 90\% \\  & CNN-SVM & 100\% & 94.3\% & 97\% & 95.55\% & 90.65\% & 92.96\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Training and testing validation results of the HE and AHE dataset based on sensitivity, specificity, and accuracy. Figure 21: **(a)** Training accuracy of CNN and CNN-SVM on image classification using the HE dataset; **(b)** training loss of CNN and CNN-SVM on image classification using the HE dataset; **(c)** training accuracy of CNN and CNN-SVM on image classification using the AHE dataset; **(d)** training loss of CNN and CNN-SVM on image classification using the HE dataset.
## 5 Discussion
### Implications for Autonomous Vehicle Security  
The results of our experiments demonstrate that autonomous vehicles remain vulnerable to a range of sensor-based attacks despite advances in perception technologies. The successful completion of routes under attack conditions in Episodes 7 and 26, albeit with significant trajectory deviations, suggests that current autonomous driving systems possess a degree of inherent resilience. However, the catastrophic failures observed in Episodes 5 and 27, where vehicles crashed due to sensor manipulation, highlight the critical nature of these vulnerabilities [60]. An illustrative example of such a critical failure under an RGB attack is presented in Figure A2 in Appendix B.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor Type** & **Attack Type** & **Results Without Defense** & **Results with Defense** \\ \hline RGB Camera & Salt-and-Pepper Noise (80\%) & Severe trajectory deviations; vehicle & Limited effectiveness; 30-45\% trajectory drift still weaving across lanes; crashes in some episodes & 30-45\% trajectory drift still present; filtered images remain compromised \\ \hline Dynamic Vision Sensor (DVS) & Event Flooding (60\% spurious events) & False motion perception; higher anomaly scores (0.76) & Limited effectiveness; anomaly detection detects attack phase; event filter still compromised; lower anomaly scores (0.55) \\ \hline Depth Camera & Depth Map Tampering (random patches) & Misinterpreted obstacle distances; affected perception of road boundaries & Limited effectiveness; filtered depth maps still contain significant distortions \\ \hline LiDAR & Phantom Object Injection (5 clusters) & False obstacle detection & Partial mitigation through point cloud filtering; density-based clustering removes some phantom objects \\ \hline GPS & Position Manipulation & Significant deviation between reported and actual position; incorrect routing decisions & Highly effective mitigation; route deviation and velocity consistency checks prevented course corrections based on falsified data \\ \hline Sensor Update & Denial of Service (rate limiting) & Delayed/missed sensor readings; affected perception and decision-making & Partial mitigation through sensor update monitoring and buffering mechanisms \\ \hline Control System & Steering Bias & Systematic errors in steering commands; trajectory deviation & Partial effectiveness; sophisticated attacks could still influence vehicle control \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Sensor Attacks and Defense Effectiveness.  
Particularly concerning is our observation that defense mechanisms showed limited effectiveness against sophisticated attacks on RGB and depth sensors. While our GPS spoofing defenses performed well, the continued vulnerability of visual perception systems represents a significant security gap that malicious actors could exploit [47]. This suggests that the current approach of hardening individual sensors against attacks may be insufficient, and a more holistic security architecture is needed [51].
# Investigation of Potential of GNSS-R Polarization: Theoretical Simulations
## 3 Theoretical Simulations
### Vegetation DDM Output at Various Polarizations  
In this section, we present the scattering properties of vegetation and the corresponding spaceborne DDM output using the models described in Section 2.2. Because the target geophysical parameters have much larger scattering coefficients at the specular scattering cone, we first present the coherent scattering properties at various polarizations.  
It can be seen from the left figure of Figure 5 that the scattering properties vary as the polarization changes. Scattering at RR polarization has the lowest values for lower incidence angles; the values increase as the incidence angle increases, and finally decrease at larger incidence angles. Except for RR and VR polarizations, the scattering properties of the other polarizations present trends that are similar. The scattering properties at VR polarization should be given greater attention because of an apparent decrease in the incidence angle of 70\({}^{\circ}\), a phenomenon attributable to lower scattering at the Brewster angle. The component scattering contribution to the total scattering at LR polarization is presented in the right figure of Figure 5. It can be seen that the total scattering is dominated by the trunk layer, and that the scattering from the crown layer is the lowest. The scattering information from the ground parts (i.e., the direct ground component and the specular ground component) has higher scattering values than the crown layer.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Integration Area** \\ \hline X\_range & [\(-\)40,40] \(*\) 1000 \\ \hline Y\_range & [\(-\)40,40] \(*\) 1000 \\ \hline X\_interval & 1000 \\ \hline Y\_interval & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Integration area settings.  
Figure 4: DDM variations at LR (**left**), VR (**middle**), and HR (**right**) polarizations.  
By employing the LAGRS simulator, the DDM output at different polarizations is realized (Figure 6). Figure 6 (left figure) shows the DDM at LR polarization, while the DDM at VR and HR polarization is shown in Figure 6 (middle figure) and 6 (right figure), respectively. The integration area is listed in Table 2. The vegetation parameters for the simulation in Figure 6 were set as described, and all other observation geometry parameter settings were kept constant. The final DDM output shown in Figure 6 reflects the polarization difference, i.e., the polarization provides different information for the same vegetation given the same observation geometry and integration area.
## 4 Results and Discussion  
### Simulation Result Analysis  
By using the co-simulation platform, the control process of the multi-channel concentrated liquid supply system is simulated, and the feasibility of the co-simulation platform and control scheme is verified through step response, sinusoidal tracking and anti-interference, respectively. In addition, in this simulation analysis, a traditional PID and an adaptive fuzzy PID controller are used as comparison methods to study the change in the main pipe's pressure after clustering, and explain the advantages of a variable universe fuzzy PID controller in the control of MCCS system. In order to facilitate the comparison and analysis, we use a set of PID parameters with better control effects (\(K_{p0}=4.5\), \(K_{i0}=3.4\), \(K_{d0}=10\), \(T=0.05\) s) while the rest of the basic parameters are exactly the same, and the control effect of three controllers is observed under different target signals.