esa-sceva/satcom-chunk-collection huggingface dataset:
# ADPSCAN: Structural Graph Clustering with Adaptive Density Peak Selection and Noise Re-Clustering
## 5 Experiments
### Real World Data
#### 5.2.2 Performance of Noise Re-Clustering  
To validate the effectiveness and practical feasibility of our method,we conducted ablation studies on multiple real-world datasets, including Karate, Polbooks, and Dolphins. These datasets are not only characterized by complex structures and diverse data characteristics but are also widely used to evaluate the performance of clustering algorithms. Our study focused particularly on assessing the impact of noise re-clustering. We designated the version of our algorithm without noise re-clustering as ADPSCAN-NR. The decision not to perform ablation studies on adaptive density peaks selection was deliberate. Adaptive density peaks selection forms the foundational step of our approach, responsible for identifying potential cluster centers without requiring manual input from users. The accuracy of this step directly influences the precision of subsequent clustering. Hence, we concentrated our efforts on evaluating how noise re-clustering enhances the final clustering results, ensuring the integrity of our method.  
Figure 7: Performance of different algorithms on the LFR benchmark networks.  
Across the Karate dataset, ADPSCAN achieved perfect scores with ARI and NMI both reaching 1.000 compared to ADPSCAN-NR. On the Polbooks dataset, ARI and NMI improved to 0.668 and 0.601, respectively, while on Dolphins, these metrics rose to 0.935 and 0.889. These significant performance improvements can be attributed to our noise re-clustering method, which calculates distance-based structural similarities between noise points and clusters, thereby enhancing clustering quality and stability (Table 3).  
In conclusion, our ablation study underscores that by integrating adaptive threshold selection and noise re-clustering, our method substantially enhances clustering accuracy.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 4 Polarization Correction and Verification
### Quality Assessment of Corrected Data
Furthermore, Figure 8c implies that isolations of all data are high (better than 35 dB), which demonstrate that the GF-3 antenna is highly isolated, as described in Section 3.3. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Mode**} & **Amplitude Imbalance (dB)** & **Phase Imbalance (deg)** & **Isolation (dB)** \\  & & **VV/HH** & **VV-HH** & **HV/HH or VH/HH** \\ \hline
1 & Q9 & \(-\)0.03 & 5.2 & 38.7 \\
2 & Q15 & 0.04 & 2.1 & 37.1 \\
3 & Q17 & 0.13 & 3.6 & 42.0 \\
4 & Q25 & \(-\)0.05 & 1.7 & 39.9 \\
5 & Q25 & \(-\)0.12 & \(-\)0.43 & 37.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Quality assessment results estimated by corner reflector. In summary, the quality assessments show that the isolation is better than 35 dB, and the channel imbalance is within 0.5 dB in amplitude and within 10\({}^{\circ}\) in phase. This accuracy reaches the expected quality requirements of GF-3 PolSAR data. Therefore, the polarization correction method based on full-link error analysis effectively completes the polarimetric correction work of GF-3 data.
# Simulation of Electronic Quantum Devices: Failure of Semiclassical Models
## 2 Fundamentals of Semiclassical Device Modeling
### Phenomenological Dissipation and Thermalization Models
From a physical point of view, the action and role played by the IBC thermalization scheme in Equation (5) are significantly different from those of the dissipation term in Equation (4): while the latter modifies the carrier distribution in any point \(\mathbf{r}\) inside the device volume \(\Omega\), the former simply fixes the value of the incoming distribution on the boundary surface \(\sigma^{b}\) and has no effect inside the  
Figure 2: The conventional inflow or U boundary condition scheme adopted in semiclassical device modeling [20] for a one-dimensional problem. The value of the distribution function \(f(z,k)\) is specified at the boundaries \(z^{b}(k)\) of the active region, i.e., \(f(-l/2,k>0)\) and \(f(+l/2,k<0)\) are fixed by the incoming/inflowing carrier distribution function. Reprinted from [67]. device. Despite these profound differences, the two models share a common formal structure, i.e., they are both RTA-type operators of the form:  
\[\left.\frac{\partial f(\mathbf{r},\mathbf{k})}{\partial t}\right|_{\text{RTA}}=- \overline{\Gamma}(\mathbf{r},\mathbf{k})\,\left(f(\mathbf{r},\mathbf{k})- \overline{f}(\mathbf{r},\mathbf{k})\right)\, \tag{7}\]  
where, for the dissipation model in Equation (4), we have  
\[\overline{\Gamma}(\mathbf{r},\mathbf{k})=\Gamma(\mathbf{r},\mathbf{k})\ \, \overline{f}(\mathbf{r},\mathbf{k})=f^{\circ}(\mathbf{r},\mathbf{k}) \tag{8}\]  
while, for the IBC thermalization model in Equation (5), we have  
\[\overline{\Gamma}(\mathbf{r},\mathbf{k})=\int d\sigma^{b}\,\delta(\mathbf{r}- \mathbf{r}^{b})\,v_{\text{in}}(\mathbf{r}^{b},\mathbf{k})\ \, \overline{f}(\mathbf{r},\mathbf{k})=f^{b}(\mathbf{r}^{b},\mathbf{k}). \tag{9}\]  
It is worth stressing that, while for the dissipation model in Equation (8) the relaxation rate \(\overline{\Gamma}\) acts as a simple algebraic multiplication, for the IBC thermalization model in Equation (9) the latter is an integral operator acting on our phase-space coordinates \(\mathbf{r},\mathbf{k}\). As we discuss in Section 4, such formal similarity may help in understanding the common origin of diverse anomalous results obtained applying the local dissipation and thermalization models introduced thus far to quantum-transport simulations based on the Wigner-function formalism.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Classification Tests
#### 2.3.3 Evaluation Criteria  
The classifier performance was evaluated based on the accuracy, precision, recall and specificity. Accuracy can be defined as  
\[Accuracy=\ \frac{TP+TN}{TP+FP+TN+FN}, \tag{8}\]  
where \(TP\) is the True Positive count, \(TN\) is the True Negative count, \(FP\) is the False Positive count and \(FN\) is the False Negative count. Accuracy was used in this study as the criterion to be maximized in the hyperparameter optimization in the classifiers.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Hyperparameter Values** & **Matlab® Function** \\ \hline k-NN & ‘NumNeighbors’: 1–49, odd numbers only & _fitcknn_ \\ \hline LDA & ‘Gamma’: 0–1, step 0.025 & _fitcliscr_ \\  & ‘Delta’: 0 and \(1\times 10^{-6}\times 10^{5}\), where \(x=0\)–9, step 1 & _fitcliscr_ \\ \hline SVM & ‘KernelScale’: \(1\times 10^{-5}\times 10^{5}\), where \(x=0\)–10, step 0.4 & _fitcscm_ \\  & ‘BoxConstraint’: \(1\times 10^{-5}\times 10^{5}\), where \(x=0\)–10, step 0.4 & _fitcscm_ \\ \hline AdaBoost M1 & ‘LearnRate’: \(\{0.1,0.25,0.5,0.75,1\}\) & _fitcscm_ \\  & ‘MaxNumSplits’: 10–90, step 10 & _fitcensemble_ \\  & ‘NumLearningCyles’: 10–150, step 20 & _fitcscm_ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classifiers used as reference methods, tested hyperparameter values and Matlab® functions used for classifier training.  
Figure 9: “Probabilistic threshold” approach (**left**) and “two distributions” approach to classification (**right**).  
Precision reveals the accuracy of the positive predictions. Low precision indicates that the classifier labels samples falsely as positive which may result in an increased amount of useless maintenance actions. Precision is defined by  
\[Precision=\ \frac{TP}{TP+FP}. \tag{9}\]  
Recall (or sensitivity) shows the ratio of predicted true positives to the total number of true positives. Low recall indicates that the classifier labels the samples of the positive class falsely as negative class which increases the risk that the damaged machine will be kept in operation although it should be taken to service. Recall is defined by  
\[Recall=\ \frac{TP}{TP+FN}. \tag{10}\]  
Specificity shows the ratio of predicted true negatives to the total number of true negatives. Low specificity indicates that the samples of the negative class are falsely labeled as positive class which is a harmful feature as it increases the number of false alarms. Specificity is defined by  
\[Specificity=\ \frac{TN}{TN+FP}. \tag{11}\]
## 4 Results and Discussions
### Descriptive Statistical Analysis
#### 4.1.1 Descriptive Statistical Analysis of Experiment 1  
The nonparametric statistical results for each traffic scenario in Experiment 1 are presented in Table 4. Regarding the traffic flow level, due to the insufficient sample size in the low traffic flow scenario, only the high and medium traffic flow scenarios are analyzed in this section.  
Figure 7: MCMC sampling trace plots of Experiment 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vehicle Type** & **Position** & **D/cm** & **D1/cm** & **D2/cm** \\ \hline \multirow{3}{*}{\(1\)} & 1 & 21.2 & 0.3 & 3.5 \\ \cline{2-5}  & 2 & 34.2 & 13.3 & 1.0 \\ \cline{2-5}  & 3 & 21.2 & 0.3 & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed parameters of the blind spot image for different positions of Vehicle Type 1.  
The results indicate that the mean values of time buffer values are generally smaller in city road (mean--city roads = 1.379; mean--expressway = 1.685), right lane change (mean--right lane changes = 1.410; mean--left lane changes = 1.594), and high traffic flow (mean--medium traffic flow =1.500; mean--high traffic flow = 1.347) scenarios, representative of the higher cognitive load. In the expressway (\(p\) = 0.026) and medium traffic flow (\(p\) = 0.014) scenarios, vehicle types have a significant correlation with the time buffer values, which means vehicle types have a strong impact on drivers' cognitive load in these two scenarios. In contrast, the influence of vehicle types on driver cognitive load is not evident for both lane-changing directions.  
Comparing the mean and standard deviation of the time buffer values corresponding to different vehicle types in the traffic environments with strong correlation between vehicle types and time buffer values, it is found that the time buffer values corresponding to Type 2 have the largest mean value and the smallest standard deviation, which is judged to be the blind zone design that best meets the sweeping characteristics of the driver when changing lanes and results in a lower cognitive load. Conversely, Type 1 exhibits the smallest mean time buffer value, corresponding to the highest cognitive load on drivers.
## 6 Basic use Cases of M2M Transformation Utilizing NLP Extension Capabilities
### Merging Target Elements with Synonymous Meanings
#### 6.2.4 Execution Instance of the Specific use Case Example  
Let us consider a user setting the property _mergeSynonyms_ to _true_ and \(I^{1}_{\text{SMCT}}\) to be a UML _Class_ element that is being dragged onto the SBVR business vocabulary diagram; \(I_{\text{TMCT}}=\left\{I^{i}_{\text{TMCT}}\right\}\), \(i=1\ldots N\) is a set of elements that are already present in this diagram, and \(I^{i}_{\text{TMCT}}\) is one of the elements in this set. Then, the following output is expected (note: here and further on in this paper, we assume that the underlying technology is viable to match element names precisely; however, it may differ based on the quality of tools or pretrained models that implement matching analysis or other relevant advanced NLP processing):  
* If \(I^{1}_{\text{SMCT}}\) is named as \"Employee\" and \(I^{i}_{\text{TMCT}}\) is named as \"Worker\", the user is notified about the already existing matching element and no new element is generated; and
* If \(I^{1}_{\text{SMCT}}\) is named as \"Employee\" and \(I^{i}_{\text{TMCT}}\) is named as \"Manager\", no match will be identified, therefore the general concept \"Manager\" is generated.
* Under the same conditions, let us consider a user setting the property _resolveAbbreviations_ to _true_, then the following is possible:  
Figure 8: M2M transformation specification for UML _Class_ to semantics of business vocabulary and rules (SBVR) _GeneralConcept_ transformation.  
* If \(I^{1}_{SMCT}\) is named as \"Mngr.\" and \(I^{i}_{TMCT}\) is named as \"Manager\", the user is notified about the already existing matching element and the new element is not generated; the same will happen if the element names are reversed, i.e., \(I^{1}_{SMCT}\) is named as \"Manager\" and \(I^{i}_{TMCT}\) is named as \"Mngr.\"; and
* If \(I^{1}_{SMCT}\) is named as \"Mngr.\" and \(I^{i}_{TMCT}\) is named as \"Clerk\", the general concept \"Clerk\" is generated.
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
## 2 System Model
### Smart Contract
Finally, after the published calculation results are verified, the miners who complete the mining gain reputation and incentive increments. ```
Input: The historical record, reputation entry threshold \(h^{(i)}\) and the reputation fluctuation \(f_{j}\). Output: Reputation, historical record, rewards and optimal behaviors. Initialize\(T_{j}\), \(B_{j}(a,n)\), \(\overline{K}_{j}\) Repeat while\(n_{\text{r}}\in N\) for\(\overline{R}_{j}-\frac{f_{j}}{\sqrt{\pi i_{j}(a-n)}}\geq h^{(i)},\sigma_{j}^{2}\leq \frac{h^{(i)}\sigma_{j}^{2}\leq\frac{h^{(i-1)}}{n-1}}{n-1}\) and \(f_{j}\in B_{j}(a,n)\). Miner \(j\) can join the pool \(i\) to solve PoW puzzles. Calculate \(f_{j}\) and \(h^{(i)}\) to obtain \(T_{j}\). if\(C_{j}=H\) \(T_{j}+1\)  Calculate \(u_{j}^{H}\) according to (15). else \(T_{j}\gets T_{j}-1\)  Calculate \(u_{j}^{D}\) according to (15). do until\(T_{j}>0\)  Calculate \(\tilde{t}_{j}(n)\) according to (16). Update the reputation \(R_{j}\). loop endif endfor endwhile
```  
**Algorithm 1** Honesty behavior reputation update algorithm  
Figure 2: The logic flow diagram. ```
Input:\(h^{(i)}\), \(f_{j}\), \(E\left(R_{j}^{(i)}(n)\right)\). Output:\(f_{Screen}^{(j)}(n)\), \(|\mathcal{L}^{(i)}|_{j}\), \(\sum\limits_{i\in\mathcal{M}_{ij}^{(i)}}R_{j}^{(i)}(n)\). Initialize\(D^{tar}\), \(\zeta_{\min}^{c}\), \(R_{B_{n}}^{(i)}\). Repeat while\(n_{s}\in N\) for\(\overline{R}_{j}-\frac{f_{j}}{\sqrt{n_{s}(2g-1)}}\geq h^{(i)},\sigma_{j}^{2} \leq\frac{\frac{\mu_{s}^{2}}{N_{s}^{2}}2(n-1)}{n-1}\) and \(f_{j}\in B(a,n)\). Miner \(j\) can join the pool \(i\) to solve PoW puzzles. Randomly adjust the screening threshold \(f_{Screen}^{(i)}(n+1)\). Screen the pool miners of high-reputation. if\(E\left(R_{j}^{(i)}(n)\right)\geq f_{Screen}^{(i)}(n+1)\)  Miner could be admitted joining in \((n+1)\)-th mining \(|\mathcal{L}^{(i)}|\leftarrow|\mathcal{L}^{(i)}|+1\) else  Miner \(j\) would be expelled from the first screening. end if  Calculate \(\sum\limits_{i\in\mathcal{L}^{(i)}}R_{j}^{(i)}(n)\) and compare to \(E\left(R_{j}^{(i)}(n)\right)\). do until\(\sum\limits_{i\in\mathcal{L}^{(i)}}R_{j}^{(i)}(n)<E\left(R_{j}^{(i)}(n)\right)\). Subtract \(\varepsilon\) from \(f_{Screen}^{(i)}(n+1)\) by the \(\varepsilon\)-greedy policy. Update \(\sum\limits_{i\in\mathcal{L}^{(i)}}R_{j}^{(i)}(n)\). loop endfor endwhile
```  
**Algorithm 2** A low-reputation miner screening algorithm for mining pools
## 3 Design Response Spectra for Lebanon
### Design Response Spectra According to Eurocode 8  
The NL135 standard announced the transition to Eurocode 8 as the exclusive seismic code applicable in Lebanon within the next five years. In view of this, French Universities instruct on EC8, and engineers incorporate it into their designs of buildings by adopting parameters identical to those assumed for PS92.  
The DRS in terms of horizontal absolute acceleration \(S_{e}(T)\) are given by EC8 as follows:  
\[S_{e}(T)=a_{g}\ S\left[1+\frac{T}{T_{B}}(2.5\eta-1)\right]\text{for }0<T\leq T _{B} \tag{19}\]  
\[S_{e}(T)=2.5\ a_{g}\ S\eta\text{ for }T_{B}<T\leq T_{C} \tag{20}\]  
\[S_{e}(T)=2.5\ a_{g}\ S\eta\left[\frac{T_{C}}{T}\right]\text{ for }T_{C}\leq T \leq T_{D} \tag{21}\]  
\[S_{e}(T)=2.5\ a_{g}\ S\eta\left[\frac{T_{C}T_{D}}{T^{2}}\right]\text{ for }T_{D}\leq T\leq 4\text{ s} \tag{22}\]  
Here \(a_{g}\) is the reference peak ground acceleration, taken equal to 2.5 m/s\({}^{2}\) according to Assumption 3, while \(\eta\) is a damping correction factor given by  
\[\eta=\sqrt{10/(5+\xi)}\ \geq 0.55 \tag{23}\]  
\(\xi\) being the damping ratio taken equal to 5% according to Assumption 4. The EC8 Type 1 shape for the elastic response spectrum is chosen. The use of two alternative shapes of DRS (Type 1 or Type 2) is in fact allowed by EC8. When the seismic hazard of the most contributing earthquake for the considered site has a surface-wave magnitude not greater than 5.5, Type 2 is recommended, otherwise Type 1 should be better used. Since the magnitude of Lebanese earthquakes may even greatly exceed 5.5, as can be inferred from Table 1, the Type 1 shape is here assumed for the elastic DRS, this choice being on the safe side. Five ground types are considered by EC8, as recalled by Table 5. The values of the soil factor \(S\) and of the periods \(T_{B}\), \(T_{C}\) and \(T_{D}\) for the Type 1 shape and for the five ground types are recalled in Table 6. The EC8 design spectra obtained for the five ground types and relevant to both the horizontal components are plotted in Figure 4. Similarly, to PS92 and IBC, EC8 follows the soft soil amplification rule in the third branch of the spectra.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
###### Abstract  
As the flight envelopes of turbine-based combined cycle (TBCC) engines expand, ensuring reliable ignition and flame stability under varying conditions becomes increasingly critical. Previous work has shown a significantly changed ignition performance and flow pattern in cavity-based combustors when strut structure parameters were altered, indicating a strong correlation between the ignition process, flame structure, and the strut configuration. This suggests that further investigation is required to determine the optimal strut design. Therefore, this study examines the impact of various strut configurations through numerical simulations, validated by high-speed imaging. Findings show that the tail-groove strut designs improve the flame propagation performance compared to the normal struts, with a critical depth beyond which further increases do not enhance performance. Changes in strut length have a lesser impact than depth. Flow analysis indicates that tail-groove struts create additional recirculation zones that enhance fuel atomization and flame stability. These results suggest that optimizing strut configurations is vital for achieving reliable ignition and flame stability, advancing the development of efficient engines across a wide range of operational conditions.  
a 2024 14 8979 10.3390/app14198979
## 2 System
### Adjustment of Data Obtained from Sensors
#### 2.2.3 Geomagnetic Sensor  
The values obtained from the geomagnetic sensor require offset adjustment. When the values obtained from a three-axis geomagnetic sensor are plotted in three-dimensional space, they should ideally be distributed on the surface of a sphere or an ellipsoid. The center of this point cloud should correspond to the origin at \((x,y,z)=(0,0,0)\). However, if there are magnetic objects or iron products in the surrounding environment, the center may deviate significantly from the origin. Therefore, it is necessary to calculate the center of the point cloud obtained from the geomagnetic sensor and set it as the offset. To determine the offset, the geomagnetic sensor should be rotated in all directions while collecting and storing \(N\) sets of 3D point cloud data. In the following, the calculation method is explained, with the \(n\)-th 3D point cloud data for offset calculation represented as \((x_{n},y_{n},z_{n})\).
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### Method
#### 2.2.4 RoI Align Layer for Handling Frontal Candidate Regions  
Within the ROI Align layer, feature maps of varying sizes are transformed into abstract feature maps with consistent dimensions, which are then used by subsequent classification  
Figure 3: **(a)** The structure of FPN; **(b)** The structure of RPN; **(c)** The structure of Mask.  
and mask network modules to classify and regress each previously detected ROI, ultimately outputting the final classes and bounding boxes for front detection.  
The candidate boxes output by the ROI Align layer may deviate from the ground truth bounding boxes (IoU \(<\) 0.5), necessitating fine-tuning to bring them closer to the true target boxes and achieve more accurate localization of the candidate boxes. To better describe the bounding box regression method, we use a four-dimensional vector (\(x,y,w,h\)) to represent the window, where (\(x,y\)) denotes the center point of the window, and (w) and (h) represent its width and height, respectively. G is denoted as the ground truth bounding box. The bounding box regression is achieved through a mapping function (\(f(P_{x},P_{y},P_{w},P_{h})=(\hat{G}_{x},\)\(\hat{G}_{y},\)\(\hat{G}_{w},\)\(\hat{G}_{w},\)\(\hat{G}_{h})\)), such that (\(\hat{G}_{x},\)\(\hat{G}_{y},\)\(\hat{G}_{w},\)\(\hat{G}_{h})\)\(\approx\) (\(G_{x},\)\(G_{y},\)\(\hat{G}_{w},\)\(G_{h})\). Among them, (\(G_{x},\)\(G_{y},\)\(G_{w},\)\(G_{h}\)) represent the true values, and (\(\hat{G}_{x},\)\(\hat{G}_{y},\)\(\hat{G}_{w},\)\(\hat{G}_{h})\) represent the predicted values. Boundary box regression utilizes translation and scaling transformations to achieve mapping. The calculation formula for translation transformation is as follows:  
\[\left\{\begin{aligned} \hat{G}_{x}&=P_{w}d_{x}(P)+P_{x}\\ \hat{G}_{y}&=P_{h}d_{y}(P)+P_{y}\end{aligned}\right. \tag{3}\]  
The calculation formula for scale transformation is as follows:  
\[\left\{\begin{aligned} \hat{G}_{w}&=P_{w}exp(d_{w}(P))\\ \hat{G}_{h}&=P_{h}exp(d_{w}(P))\end{aligned}\right. \tag{4}\]  
Among them, \(d_{*}(P)\) (* represents \(x,y,w,h\)) is a set of four linear functions based on [pooling]_5. Here, we denote the feature as \(\varnothing_{5}\)(P), so \(d_{*}(P)=w_{*}^{T}\varnothing_{5}(P)\). The solution expression for \(w_{*}\) is  
\[w_{*}=arg_{w_{*}}min\sum^{N}\left(t^{i}_{*}-\hat{w}_{*}^{T}\varnothing_{5} \left(P^{i}\right)^{2}+\lambda\|\hat{w}_{*}\|^{2}\right.\]  
The translation amount ( \(t_{x},t_{t}\)) and scaling amount (\(t_{w}\),\(t_{h}\)) are represented as follows:  
\[t_{x}=\frac{G_{x}-P_{x}}{P_{w}}\, \tag{5}\]  
\[t_{y}=\frac{G_{y}-P_{y}}{P_{h}}\, \tag{6}\]  
\[t_{w}=log\left(\frac{G_{w}}{P_{w}}\ \right), \tag{7}\]  
\[t_{h}=log\left(\frac{G_{h}}{P_{h}}\ \right), \tag{8}\]