esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning Effective Feature Representation against User Privacy Protection on Social Networks
## 4 The Proposed Method
### Downstream Tasks  
Three downstream social network analysis tasks are considered in this work, including community detection, link prediction, and node classification. We choose three such downstream tasks because they are commonly used to evaluate the effectiveness of node embeddings generated by NRL methods [2; 3; 4; 8; 19; 20]. The underlying idea is that we need to examine how effective are the feature representations of nodes. The widely-considered approach is the graph-based prediction task that accepts feature vectors as the inputs, and produces discrete labels as the outputs. Classical graph-based prediction tasks include node classification, link prediction, and community detection. They all take the feature vectors of nodes as the input of a supervised or unsupervised method, and generate labeling outcomes. An effective NRL method means that its generated node embeddings can lead to better performance of these three tasks. Below elaborates how we use the learned node embeddings to perform such three tasks. The following settings are used in our experiments.  
* _Community Detection (CD)._ By regarding node embeddings as features, we apply _K-means_ clustering algorithm implemented by scikit-learn ([[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/))) to detect network communities. The ground-truth number of communities is given. Note that we choose K-means because it is the basic, simple, and widely-used clustering approach. Also since existing network representation learning methods [26; 27] utilize K-means to fairly compare different node embedding generation models in the task of community detection, we are advised to follow accordingly.
* _Link Prediction (LP)._ This task is to predict the existence of links, given the existing network structure. We need to have the feature vectors of links and non-links. We follow node2 vec [4] to employ Hadamard operator, i.e., element-wise product, to generate the feature vectors from the embeddings of two nodes. To obtain links, we remove 50% of edges chosen randomly from the privacy-protected network while ensuring that the residual network is connected. To have non-links, we randomly sample an equal number of node pairs without edges connecting them. Note that since in our experiments, we mainly target at improving node2 vec under the privacy protection setting, we follow the setting of the original node2 vec method [4] to randomly removing 50% of edges.
* _Node Classification (NC)._ Given a certain fraction of nodes and all of their labels, the goal is to classify the labels for the remaining nodes. Node embeddings are treated as features. We utilize one-vs-rest logistic regression classifier with L2 regularization as the classifier.
## 2 The Aim of the Paper  
While optimizing engineering structural elements, the important issue is to obtain well-defined solutions, in particular, topologies with smooth boundaries. The quality of the structure topology boundaries depends mainly on the resolution level of mesh discretization; usually the greater the number of elements in the mesh, the better the representation of the optimized structure (see Figure 1).  
One can observe, however, that even for a very dense mesh, the so-called grey elements can still be present at the boundaries, even if some filtering techniques have been implemented (see Figure 2). This is because these techniques mainly reduce the effect of mesh dependency and not necessarily eliminate grey elements. This usually requires additional filtering.  
Figure 1: Mesh refining as the remedy for non-smooth boundaries: (**a**) example of a coarse mesh; (**b**) example of a fine mesh.  
Figure 2: Mesh refining may not eliminate grey elements: (**a**) example of a fine mesh with the presence of grey elements; (**b**) example of a fine mesh without grey elements at boundaries.  
It can be observed that while implementing dense meshes some thin and slender parts of structural topology can appear. This effect should also be controlled by filtering techniques (see Figure 3).  
All of the abovementioned issues have to be taken into consideration while developing topology generators for engineering applications. The main goal is to achieve a regular, well-defined structural layout. It seems that the remedy for the above can be the implementation of refined mesh adaptation techniques. Adaptive mesh refinement allows for reduction of the number of design variables without losing the accuracy of results and without an excessive increase in the number of elements caused by using a fine mesh for the whole structure. It is possible to obtain high-resolution optimal topologies with a relatively low computational cost. It is not necessary to cover void regions or solid regions, the appearance and necessity of which are detected in the very first stage of optimization process with a very fine mesh. The implementation of a fine grid is expected to be used mainly for the so-called grey regions, where it is decided whether a cell becomes solid or void. The benefit of such an approach is also that the checkerboard effect and the so-called grey areas can be eliminated without using any additional filtering.  
The aim of this paper is to develop a Cellular Automata algorithm with the refined mesh adaptation technique and to present the implementation of this algorithm into topology optimization problems.
# Determining Factors Influencing Operating Speeds on Road Tangents
## 2 Methodology
### Data Analysis  
The data collected for this study were subjected to a comprehensive statistical analysis using SAS JMP 18 Pro software. The primary objective of the analysis was to examine the relationships between the operating vehicle speeds (V85) and the various geometric, environmental, and traffic-related factors along the road tangents. This analysis aimed to identify the significant associations that could provide insights into speed behavior on both single- and dual-carriageway road tangents.  
The first step in the data analysis process involved data cleaning, where anomalies and extreme outliers were identified and removed to minimize any potential distortions caused by measurement errors. Only passenger cars speed values recorded under free-flow conditions were retained to ensure that vehicle interactions did not confound the analysis.  
For continuous variables, the distribution was first assessed using the Shapiro-Wilk test to determine whether the data followed a normal distribution. For variables that were normally distributed, the Pearson correlation was applied to assess the strength and direction of the relationship between the operating speed and the respective factors. For variables that did not meet the assumption of normality, Spearman's rank correlation was used to assess the monotonic relationships, as this method does not assume normal distribution. A significance level of 0.05 was used to identify statistically significant relationships.  
For categorical variables, a one-way analysis of variance (ANOVA) or \(t\)-test was employed to test statistically significant differences in operating speeds across the different levels of each categorical variable. If a significant difference was found, the Tukey-Kramer test was then used to perform pairwise comparisons between the levels of the variable, identifying where significant differences in the operating speed occurred and quantifying the magnitude of these differences.  
The analysis was conducted separately for single- and dual-carriageway road tangents. On dual-carriageway road tangents, further distinctions were made between the right (driving) lane and the left (overtaking) lane to examine the influence of lane-specific factors, such as the share of heavy goods vehicles and traffic flow density.  
The results of this analysis provided valuable insights into the factors influencing operating speeds, highlighting the relative contributions of geometric, environmental, and traffic-related variables to variations in speed. These findings formed the basis for the interpretation and discussion of this study's results.
# An Easy Technique for Focus Characterization and Optimization of XUV and Soft X-ray Pulses
## 3 Results and Discussion
The x and y FWHM spot sizes of the projections in the focus for the Pd window were 6 \(\times\) 26 \(\upmu\)m, and the equivalent circle diameter was 4.7 \(\upmu\)m. For the Al window, the x and y FWHM spot sizes of the projections in the focus were 7 \(\times\) 22 \(\upmu\)m, and the equivalent circle diameter was 10.0 \(\upmu\)m. A total of 600 single-shot images were averaged for each figure. its integrated signal was approximately a factor of 2 lower. This fits well with the energy measurements that were carried out with the XUV photodiode for the spectral region of the Pd window. This suggests that the spectral sensitivity of the camera did not change significantly between the spectral regions of the Zr window and the Pd window. Figure 6b shows the optimized focus for the Al window. This focus was with an equivalent circle diameter of 10.3 \(\upmu\)m significantly bigger than the focus of the Zr and Pd window and did not show up as sharp features. This can be explained by the higher diffraction limit of the (\(\times\)3) lower central photon energy that is transmitted through the Al filters. The enlargement of the focus also increased the energy content above the half maximum to 25%. Furthermore, the intensity of the camera signal was much lower, and therefore, the images for the focus of the Al window had to be taken with the maximum gain of the camera (16\(\times\) amplification of the signal). The signal-to-background ratio under these conditions for a single shot was 20. To characterize the sensitivity of the CMOS camera in the Al window with respect to the Pd and Zr windows, the XUV energy on the camera with three Zr filters was compared to the XUV energy with two Al filters. The energy with the Al filters was thereby found to be 50 \(\pm\) 10% of the one with the Zr filters. After correction for the different amplification and energy, it can be concluded that the sensitivity of the CMOS camera was approximately 10 times worse for the Al window than for the Zr and Pd windows. This was probably due to the structure of the CMOS camera chip.
## Preliminaries
### System Security
C performs the algorithm Public-key-Gen to compute the public key for the ID and sends it to A\({}_{2}\). 4. Tag Query. A\({}_{2}\) adaptively chooses the tuple (ID, \(m\)) and submits it to C for querying the tag of the data block \(m\) generated by the ID. C runs Tag-Gen algorithm to generate the tag of data block \(m\) and sends it to A\({}_{2}\). **Forge:** Finally, A2 outputs a forged tag \(\sigma\) for the \(m\) with the identity \(ID\). If the forged tag \(\sigma\) is valid after the above queries, then [PERSON]\({}_{2}\) wins the game. **Definition 3**: _A CL-PGSDP scheme is secure against adaptive impersonation and forging tag attacks if any PPT adversary \(A\) (\(A_{1}\) or \(A_{2}\)) who plays the above games with the challenger \(C\) has only negligible probability \(\varepsilon\) of winning the games, that is,_  
\[\Pr(A_{win})\leq\varepsilon\]  
_in which the probability \(\varepsilon\) is taken over all coin tosses made by \(A\) and \(C\)._  
**Game 3:** This game is played by challenger [PERSON] and adversary [PERSON]\({}_{3}\) who aims to forge the data integrity proof to cheat the TPA. [PERSON]\({}_{3}\) is regarded as the untrusted CSP. From the Definition 3, we know that it is hard to forge the tag of single data block. Thus, we will focus on the issue of whether [PERSON]\({}_{3}\) can forge the integrity proof without correct data to pass the challenge. **Setup:** Challenger C runs the Setup algorithm to obtain the system parameters \(params\), the master secret key \(msk\), and partial private key for all users, and only forwards \(params\) to the adversary [PERSON]\({}_{3}\). **Tag Queries:** [PERSON]\({}_{3}\) adaptively chooses the tuple (ID, m) and sends it to [PERSON] for querying the tag of data block m. [PERSON] runs the algorithm Tag-Gen to generate the tag of m and returns it to [PERSON]\({}_{3}\). **Challenge:** [PERSON] makes a random challenge \(chal\) to [PERSON]\({}_{3}\) and requests [PERSON]\({}_{3}\) to provide the corresponding data possession proof for \(chal\). **Forge:** For the challenge \(chal\), [PERSON]\({}_{3}\) generates a proof and sends it to [PERSON]. If the proof can pass the integrity verification while [PERSON]\({}_{3}\) does not possess the correct data, [PERSON]\({}_{3}\) wins the game. **Definition 4**: _A CL-PGSDP scheme is secure against adaptive impersonation and forging proof attacks if any PPT adversary \(A\) who plays Game 3 with the challenger \(C\) has only negligible probability \(\varepsilon\) of winning the games, that is,_  
\[\Pr(A_{win})\leq\varepsilon\]  
_in which the probability \(\varepsilon\) is taken over all coin tosses made by \(A\) and \(C\)._
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 5 Evaluation and Result Analysis
### Lake Experimental Results
Since the MAI we used to improve the system was mainly reflected in the second stage, we focused on comparing the noise-reduction method with the joint treatment of the second stage. In the constellation plots, the Wiener filter has the highest communication BER, which is reflected in the worst convergence in Figure 23a. Although it seems that the convergence effect of Figure 23b,c is not very obvious, the CNN noise-reduction is improved \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Angle** & **Method** & **RMSE** & **BER** \\ \hline \multirow{5}{*}{\(\theta 1=30^{\circ}\)} & Wiener + STD & 0.5520 & 0.81 \(\times\) 10\({}^{-2}\) \\  & KBM-ANC + STD & 0.4638 & 0.60 \(\times\) 10\({}^{-2}\) \\  & CNN + STD & 0.4527 & 0.52 \(\times\) 10\({}^{-2}\) \\  & Ours1 + STD & 0.4104 & 0.32 \(\times\) 10\({}^{-2}\) \\  & Wiener + Ours2 & 0.3698 & 0.77 \(\times\) 10\({}^{-3}\) \\  & KBM-ANC + Ours2 & 0.2239 & 0.55 \(\times\) 10\({}^{-3}\) \\  & CNN + Ours2 & 0.2013 & 0.19 \(\times\) 10\({}^{-3}\) \\  & Ours1 + Ours2 & 0.1804 & 0.45 \(\times\) 10\({}^{-4}\) \\ \hline \multirow{5}{*}{\(\theta 2=60^{\circ}\)} & Wiener + STD & 0.5201 & 0.79 \(\times\) 10\({}^{-2}\) \\  & KBM-ANC + STD & 0.4723 & 0.62 \(\times\) 10\({}^{-2}\) \\  & CNN + STD & 0.4387 & 0.49 \(\times\) 10\({}^{-2}\) \\ \cline{1-1}  & Ours1 + STD & 0.3912 & 0.38 \(\times\) 10\({}^{-2}\) \\ \cline{1-1}  & Wiener + Ours2 & 0.3741 & 0.74 \(\times\) 10\({}^{-3}\) \\ \cline{1-1}  & KBM-ANC + Ours2 & 0.2187 & 0.52 \(\times\) 10\({}^{-3}\) \\ \cline{1-1}  & CNN + Ours2 & 0.1922 & 0.21 \(\times\) 10\({}^{-3}\) \\ \cline{1-1}  & Ours1 + Ours2 & 0.1732 & 0.49 \(\times\) 10\({}^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMSE and BER statistics for each joint method in the on-lake experiment. Figure 22: Statistics of angular estimates of beam formation after using the four noise-reduction methods. in terms of BER. Figure 23d shows that the proposed method has a better suppression of MAI, which is reflected by the better convergence of the constellation diagram and the lower BER. Since we used an STD structure based on a weight update strategy in the second stage, the structure uses weights to constrain each system receiver. This improved STD structure also simplifies the computational complexity because it does not need to be calculated for each receiver like the equalizer structure. Using the trained network model also improves the efficiency of computing. Using the proposed two-stage model structure can enhance the system's performance requirements for real-time.
## 4 Discussion
### Limitations and Future Directions  
Due to the challenges in precisely quantifying some human activities, such as fertilization, irrigation, and grazing exclusion practices, not all the related factors were taken into account in this study. Future research should consider more influence factors. More studies are still needed to obtain reliable soil moisture data in the MHRB and to further analyze the influence of soil moisture on vegetation. In order to further explain the driving mechanism of each factor on vegetation growth, field observations, and other technologies need to be combined in the future. In addition, the MHRB serves as an important water supply in North China, the increase in vegetation may make water shortages worse in this water-deficient area [66], and deep studies are still needed to investigate how the water resources and vegetation interact in the MHRB.
# Investigation of Reverse Swing and Magnus Effect on a Cricket Ball Using Particle Image Velocimetry
###### Abstract  
Lateral movement from the principal trajectory, or \"swing\", can be generated on a cricket ball when its seam, which sits proud of the surface, is angled to the flow. The boundary layer on the two hemispheres divided by the seam is governed by the Reynolds number and the surface roughness; the swing is fundamentally caused by the pressure differences associated with asymmetric flow separation. Skillful bowlers impart a small backspin to create gyroscopic inertia and stabilize the seam position in flight. Under certain flow conditions, the resultant pressure asymmetry can reverse across the hemispheres and \"reverse swing\" will occur. In this paper, particle image velocimetry measurements of a scaled cricket ball are presented to interrogate the flow field and the physical mechanism for reverse swing. The results show that a laminar separation bubble forms on the non-seam side (hemisphere), causing the separation angle for the boundary layer to be increased relative to that on the seam side. For the first time, it is shown that the separation bubble is present even under large rates of backspin, suggesting that this flow feature is present under match conditions. The Magnus effect on a rotating ball is also demonstrated, with the position of flow separation on the upper (retreating) side delayed due to the reduced relative speed between the surface and the freestream.  
flow visualization; cricket; boundary layers; Particle Image Velocimetry +
Footnote †: journal: Applied Sciences
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 4 Countermeasures against Covert Channel Attacks  
There are multiple developments related to countermeasures of covert channels. Major countermeasures include the following: shielding through physical means, to block the transmission of data (Section 4.1); jamming, which involves the injection of noise into the system to make the channel transmit incorrect data, and, thereby, increasing BER (Section 4.2); and runtime detection, which involves monitoring of the system for anomalous/unusual activity (Section 4.3).
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 6 Conclusions  
While most methods are aimed at the detection of temporary open water (TOW) areas, the developed method especially focuses on the detection of temporarily flooded vegetation (TFV), which allows the detection of flood areas without underestimating the flood extent. The developed method is based on Sentinel-1 C-band time series data and on additional information taking into account backscatter intensity, permanent water, and topographical and urban information, and uses a hierarchical thresholding approach to combine this information. This approach was used to map a flood event that occurred in spring 2017 in the Chobe-Sambesi flood plain in Namibia.  
Normalized time series features were derived based on VV and VH polarisation and their combinations. The time series feature using VV polarisation performed better for the derivation of TOW than the composite of the derived time series features together. The time series feature using the ratio between VV and VH performed best for the derivation of TFV. This demonstrated that both polarizations, and especially their combination, are relevant for the detection of TFV. The classification results showed that the developed SAR time series approach is well suited to map flood in vegetated areas. By supplementing the TOW with the TFV areas, the accuracy of the classification results was significantly improved, and the entire flood extent could be detected. This highlights the importance of the extraction of TFV areas besides TOW for floodplain monitoring.  
In the developed approach, the urban areas were not included; however, those will be integrated in the future as a refinement of the methodology. The developed method can be used to monitor future long-term flooding and flood dynamics. Furthermore, the classification results can serve as supplement information in the evaluation of ground observations or as inputs to hydrological models. In combination with the method developed here, the growing Sentinel-1 archive will be used in the future to analyse different TFV types aiming at the continued improvement of the derivation of the entire inundation area during flood events.  
[PERSON] designed the structure and contents of this article, acquired the data, carried out the analysis and interpretation, developed the approach, and wrote all sections of this paper. The co-authors contributed to the reflection of the ideas, the critical review of the methodology, the structure of the paper, and the review of orthographic and grammatical correctness. The sequence of authors reflects their level of contribution.  
This research was funded by the Federal ministry for Economic Affairs and Energy (BMWi) grant number 50 EE1338.  
The authors would like to thank the anonymous reviewers for their helpful comments and constructive suggestions.  
The authors declare no conflict of interest.
## 4 Experiments
### Layer Segmentation  
#### 4.2.1 Dataset  
To show that our method is applicable to OCT multi-layer segmentation, we apply our method to segment 7 retina layers on Duke publicly available data set [6]. Duke publicly available data set [6] comprises 10 DME volumes, each contains 61 SD-OCT B-scan images with a size of 496 \(\times\) 768. Only 11 images in each volume were labeled by two experts. Eight boundaries were demarcated to divide each B-scan image into nine parts. They include Region above the retina (RaR), Inner limiting membrane (ILM), Nerve fiber ending to Inner plexiform layer(NFL-IPL), Inner Nuclear layer (INL), Outer plexiform layer (OPL), Outer Nuclear layer to Inner segment myeloid (ONL-ISM), Inner segment ellipsoid (ISE), Outer segment to Retinal pigment epithelium (OS-RPE) and Region below the retina (RbR).