esa-sceva/satcom-chunk-collection huggingface dataset:
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 3 Gibbs Sampling for MIMO Detection
### Gibbs Sampling Implementation  
In the GS method herein used, each iteration \(t\) consists of transitioning each coordinate \(j=1,...,n\) to a possible element \(\omega\in\mathcal{A}\) sequentially, based on a sampling rule defined by the following probability mass function (pmf) [23]:  
\[p(\mathbf{x}_{j}^{(t+1)}|\mathbf{x}^{(t)},j,\mathbf{y},g\mathbf{H})=\frac{ \exp\big{(}-\frac{1}{2\alpha^{2}}||\mathbf{y}-g\mathbf{H}\mathbf{x}_{j|\omega }||^{2}\big{)}}{\sum\limits_{\mathbf{x}_{j|\omega}\in\mathcal{A}}\exp\big{(}- \frac{1}{2\alpha^{2}}||\mathbf{y}-g\mathbf{H}\mathbf{x}_{j|\omega}||^{2} \big{)}}, \tag{6}\]  
where \(\mathbf{x}_{j|\omega}^{T}=[\mathbf{x}_{1:j-1}^{(t+1)},\omega,\mathbf{x}_{j+1: n}^{(t)}]^{T}\) and \(\alpha\) is a positive TP. Equation (6) hints that the closer lattice point \(g\mathbf{H}\mathbf{x}\) is to \(\mathbf{y}\), the higher the probability that it is is sampled. At the end of each iteration, the ML cost  
\[f(\mathbf{x}^{(t+1)})\triangleq||\mathbf{y}-g\mathbf{H}\mathbf{x}^{(t+1)}|| \tag{7}\]  
is computed and compared with the one of the best candidate thus far; then the vector \(\mathbf{x}^{(t+1)}\) is fed into the next iteration. After \(t_{\text{max}}\) iterations, the algorithm is stopped and the vector yielding the lowest ML cost is the output solution. For further implementation details and complexity reduction techniques of GS see, e.g., [22; 27]. The aforementioned process is denoted in the literature by Gibbs sampling, where one iteration involves sampling one entry at a time in the following manner [30; 31]:  
\[x_{1}^{(t+1)} \sim p(x_{1}|x_{2}^{(t)},...,x_{n}^{(t)},\mathbf{y},\overline{ \mathbf{H}}); \tag{8}\] \[x_{2}^{(t+1)} \sim p(x_{2}|x_{1}^{(t+1)},x_{3}^{(t)},...,x_{n}^{(t)},\mathbf{y}, \overline{\mathbf{H}});\] \[\vdots\] \[x_{n}^{(t+1)} \sim p(x_{n}|x_{1}^{(t+1)},x_{2}^{(t+1)},...,x_{n-1}^{(t+1)}, \mathbf{y},\overline{\mathbf{H}}).\]  
At the end of each iteration, the ML cost in (7) is computed, and the resulting vector is fed into the next iteration. The algorithm stops after a certain number of iterations and the algorithms' output is the vector with the best ML cost. The pseudocode for this type of MCMC detector is presented in Algorithm 1.  
```
Input:\(\mathbf{y}\), \(\mathbf{H}\), initial vector \(\mathbf{x}^{(0)}\), number of iterations \(t_{\max}\)  Denote the decision vector by \(\mathbf{z}\)  Denote the ML cost function as \(f(\cdot)\) for\(t=1\) to \(t_{max}\)do  Pick a position index \(j\in\{1,2,...,n\}\) from a uniform distribution  Fix the \((n-1)\) symbols of \(\mathbf{x}^{(t)}\), and transition the \(j\)th symbol of \(\mathbf{x}^{(t)}\) to \(\omega\) according to (6)  Denote the new vector by \(\mathbf{x}^{(t+1)}\) if\(f(\mathbf{x}^{(t+1)})<f(\mathbf{z})\)then  Update \(\mathbf{z}=\mathbf{x}^{(t+1)}\) endif endfor Output:\(\mathbf{z}\)
```  
**Algorithm 1** Reversible MCMC detector based on Gibbs sampling.
# Analysis and Demonstration of First Cross-Support Interferometry Tracking in China Mars Mission
## 5 Conclusions  
China Mars Mission Tianwen-1 has been in operation more than a year since it reached Mars; until now, Tianwen-1 has made major achievements while also delivering a number of surprises. Both the orbiter and rover work well, as original planned.  
Cross-support DeltaDOR measurements by intra-agency cooperation could benefit the measurement of the angular position of the spacecraft, as more baselines and a longer baseline could be arranged in the observation. In the Tianwen-1 mission, CLTC and ESOC successfully conducted cross-support interferometric tracking under commercial contract, which aims to make sure the accuracy of orbit determination of CLTC is good enough for the critical stages of the mission, such as insertion into Mars orbit and TCM. Cross-support data processing is a new challenge for CLTC; the correlation of raw data of cross baselines without proper clock information is inevitable, and DOR delay without ambiguity needs additional processing steps designed within the CLTC.  
In this paper, we discuss and analyze the algorithm and method developed especially for cross-support interferometry tracking. Results analysis testifies the effectiveness of the cross support, through which the predicted orbit accuracy is good enough especially for the critical maneuver in the mission.  
Through this work, the performance of cross-support DeltaDOR measurements by CLTC is tested and analyzed preliminary. In order to evaluate the measurement capability of CLTC for interoperability with other agencies, more research and experiments still need to be carried out in the future. However, cross-support measurement itself is undoubtedly a good way to track, and it is a very good idea which could be focused on in future studies.  
Conceptualization, S.H. and J.P.; methodology, S.H., M.W. and L.C.; software, H.M., Z.Z., and J.C.; validation, W.G.; formal analysis, H.M. and J.C.; investigation, Z.Z.; resources, M.W.; data curation, M.W.; writing--original draft preparation, S.H.; writing--review and editing, J.P.; visualization, M.W.; supervision, J.P.; project administration, W.G.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant number 61401014 and China Scholarship Council Fellowship, grant number 201503170203.  
The data presented in this study are available on request from the corresponding author.  
We thank [PERSON] of ESOC for data processing discussion during preparation cross support in China Mars mission. We also thank [PERSON] of ESOC for providing schedule file and suggestive discussion, and [PERSON] for supplying TDM. We also thank [PERSON] and [PERSON] of Beijing Institute of Tracking and Telecommunications Technology for organizing cross support.  
The authors declare no conflict of interest.
## 2 Cu-Based Catalyst-Activated PMS Processes  
In the past ten years, Cu-species catalysts have gained much attention to activate PMS owing to their cost-effectiveness and high catalytic potential, leading to the efficient elimination of organic pollutants and to eco-friendliness. Cu can also be a versatile catalyst that is easily combined with other materials such as iron to enhance their catalytic performance.  
The use of Cu-based catalysts provides an attractive and practical solution to remove organic pollutants, offering improved efficiency. Heterogeneous catalysts are frequently employed in conjunction with AOPs, such as PMS activation. This combination enhances the efficiency and effectiveness of contaminant degradation [43; 44; 45; 46; 47]. Several strategies have been proposed to mitigate Cu leaching; among them, the use of Cu-based catalysts in heterogeneous forms, including natural or designed, is considered one of the most effective strategies. By anchoring Cu onto solid supports, the risk of metal ion release is significantly minimized. These heterogeneous catalysts are also easier to separate from the treated water, reducing the likelihood of secondary pollution. Other strategies are pH control, operating within a neutral to slightly alkaline pH range that reduces the solubility of Cu species, thus limiting their release into the water.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 3 System Expected Capabilities
### Maximum Simultaneous Number of Trackable Targets  
Since the antenna has a narrow beamwidth (see Table 2), the amount of sky visible at a specific moment in time is limited and provides an idea on the number of objects illuminated by the radar simultaneously. Figure 4 shows the maximum number of simultaneous trackable targets over the course of the day for a beampark configuration. Figures 3 and 4 share the same shape of the distribution because, if we assume that the number of objects at each bin in Figure 3 is uniformly distributed over the visible sky, the number of simultaneous objects is a fraction of that number, thus the shape is retained.  
Figure 3: Number of expected observable debris objects during a 24 h timespan (regardless of RCS).
# Explainable Liver Segmentation and Volume Assessment Using Parallel Cropping
## 3 Methodology  
Our hypothesis proposes a voxel-based approach to compute all cropping boundaries in a single pass across all dimensions. This should minimize memory transfers and redundant kernel launches, increasing performance without sacrificing accuracy. We propose a GPU-optimized parallel cropping method that replaces traditional multi-pass, slice-based approaches with a single, volume-level processing step to achieve this. The technique leverages persistent threads and grid-stride loops to efficiently localize and extract the RoI, improving speed and memory utilization.  
We describe two novel cropping techniques: semiautomatic and automatic. At first, semiautomatic cropping is proper for low-quality images with unclear boundaries, requiring minimal user interaction to indicate two diagonally opposite points that surround the liver. Automatic cropping, on the other hand, is fully data-driven and relies on voxel-level thresholding to identify the RoI without manual input.  
Both cropping variants are implemented as GPU kernels that compute the RoI's bounding coordinates and then transfer only the relevant voxel data to a new memory buffer. This compact representation is then passed to the segmentation module, significantly reducing input size and enabling faster downstream processing.  
We integrate the proposed cropping step into two segmentation pipelines: traditional Chan-Vese and deep learning-based models. The Chan-Vese segmentation method is particularly well-suited for cropped images due to its robustness and mathematical transparency. It iteratively evolves a contour to fit the liver boundary and its performance benefits significantly from the reduced input size achieved via cropping.  
In parallel, we apply the cropped images to a U-Net model trained on the LiTS dataset. The reduction in input complexity accelerates the model's inference time. However, resizing steps introduced by the fixed-size input requirement may reduce raw segmentation accuracy. To address this, a post-processing filter is applied to retain only the largest connected component, effectively eliminating false positives. This combination enables high-precision segmentation with reduced computational demands.  
Both segmentation outputs--whether from Chan-Vese or post-processed U-Net--are inputs to the final volume assessment stage. We compute the total liver volume by applying a 3D-seeded region growing on the segmented volume. The accuracy and efficiency of this stage are directly enhanced by the compactness of the cropped data, which reduces voxel traversal and memory overhead.
# Privacy-Preserving Authentication Based on PUF for VANETs
###### Abstract  
The secret key is stored in an ideal tamper-proof device so that a vehicle can implement a secure authentication with the road-side units (RSUs) and other drivers. However, some adversaries can capture the secret key by physical attacks. To resist physical attacks, we propose a physical-preserving authentication based on a physical unclonable function for vehicular ad hoc networks. In the proposed scheme, a physical unclonable function is deployed on the vehicle and the RSU to provide a challenge-response mechanism. A secret key is only generated by the challenge-response mechanism when it is needed, which eliminates the need to store a long-term secret key. As a result, this prevents secret keys from being captured by adversaries, improving system security. In addition, route planning is introduced into the proposed scheme so that a vehicle can obtain the authentication key of RSUs on its route before vehicle-to-infrastructure authentication, which greatly speeds up the authentication when the vehicle enters the RSUs' coverage. Furthermore, a detailed analysis demonstrates that the proposed scheme achieves security objectives in vehicular ad hoc networks. Ultimately, when contrasted with similar schemes, the performance assessment demonstrates that our proposed scheme surpasses others in terms of computational overhead, communication overhead and packet loss rate.  
authentication; physical-preserving; physical unclonable function; route planning +
Footnote †: journal:  
00 2024  
10 May 2024  
Revised: 23 August 2024  
Accepted: 30 August 2024  
Published: 8 September 2024
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
###### Abstract  
The defence-in-depth (DiD) methodology is a defensive approach usually performed by network administrators to implement secure networks by layering and segmenting them. Typically, segmentation is implemented in the second layer using the standard virtual local area networks (VLANs) or private virtual local area networks (PVLANs). Although defence in depth is usually manageable in small networks, it is not easily scalable to larger environments. Software-defined networks (SDNs) are emerging technologies that can be very helpful when performing network segmentation in such environments. In this work, a corporate networking scenario using PVLANs is emulated in order to carry out a comparative performance analysis on defensive strategies regarding CPU and memory usage, communications delay, packet loss, and power consumption. To do so, a well-known PVLAN attack is executed using simulated attackers located within the corporate network. Then, two mitigation strategies are analysed and compared using the traditional approach involving access control lists (ACLs) and SDNs. The results show the operation of the two mitigation strategies under different network scenarios and demonstrate the better performance of the SDN approach in oversubscribed network designs.  
software-defined networks (SDNs); private VLAN (PVLAN); security; segmentation Article
## 3 Estimation Procedure Based on StEM
### Convergence Diagnosis  
Determining convergence is a critical aspect of the StEM algorithm, yet it remains an open question in the literature. The most commonly used approach for convergence diagnostics involves visual examination of trace plots [24; 25; 26]. Recently, ref. [27] proposed a Geweke Statistics-based method. We adopt this approach in our implementation for a more rigorous assessment of convergence. Specifically, for each run, after initializing the Markov chain with the specified initial values, we determined stationarity using a batch procedure based on the Geweke statistic [28]. A Geweke statistic is computed at each increment of \(w\) iterations using a moving window with batch size \(M\). Specifically, the procedure consists of the following steps:  
1. Initialization. Set \(B=0\) and run the StEM algorithm to obtain the initial series of the estimates \(\{\mathbf{\theta}^{(wB+1)},\dots,\mathbf{\theta}^{(wB+M)}\}\).
2. Check stationarity. For each entry \(p\) in \(\mathbf{\theta}\), compute the Geweke statistic \(z_{p}\) from the Markov chain \(\{\theta_{p}^{(wB+1)},\dots,\theta_{p}^{(wB+M)}\}\). The Geweke statistic is defined as the standardized mean difference between the first \(p_{1}\) and last \(p_{2}\) portion of the chain, where \(p_{1}\) and \(p_{2}\) can be fine-tuned for a specific application. We consider stationary to be reached when all \(|z_{p}|s\) are sufficiently small, i.e., \(\sum_{p=1}^{p}z_{p}^{2}<\epsilon P\), where \(P\) is the total number of parameters and \(\epsilon\) is another tuning parameter.
3. Update. If stationarity is not reached, perform \(w\) additional runs of the chain, increase the number \(B\) by 1, and repeat step 2.
## 5 Experiments
### Performance Comparison  
#### 5.2.1 Baselines Performance  
To address RQ 1, we conducted experiments on several baselines. From Table 7, we can summarize the following points:  
1. Our proposed method, ExoFIA, outperformed the baselines according to the NDCG and MSE metrics. Additionally, our model had a higher hit rate than other models when considering HR@1P and HR@5P. It appears that our model was successful in capturing the outbreak cascades of interest. We also noticed that the simpler model measured better on HR@15P (statistics-based approaches > feature-based approaches > deep-learning-based approaches) because the distribution of this dataset is skewed. The first 15% of the cascades equate to all the cascades with propagation (i.e., the remaining 85% of cascades were not propagated, and their cascade size was 1).
2. When all metrics are taken into consideration, ExoFIA > the future-deep method > the future-linear method. The future-deep method employs multiple non-linear functions to model the relationship between the predicted values and the actual cascade size levels and performs better than the future-linear method. However, it overlooks the dynamic data implicitly stored in networks.
3. When all metrics are considered, ExoFIA > CasCN > Topo-LSTM. All of these models analyze the cascade structure. However, Topo-LSTM does not consider time information, so it performs poorly. ExoFIA performs better than CasCN because ExoFIA incorporates both endogenous factors, such as user history, as well as exogenous signals.
4. The performance of CasCN in terms of hit rate is superior to that of the future-deep method, yet it lags behind with respect to the NDCG metric. This demonstrates that while dynamic features play a crucial role in capturing burst tweets, other features are still needed to effectively accomplish the sorting task.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Types** & **Model Name** & **HR@1P** & **HR@5P** & **HR@10P** & **HR@15P** & **NDCG@1P** & **NDCG@10P** & **NDCG@15P** & **MSE** \\ \hline Statistics & Node\_num\_at\_T & 0.677 & 0.791 & **0.798** & **0.808** & 0.712 & 0.766 & 0.787 & 0.784 & 0.077 \\ \hline Feature & Future-linear & 0.645 & 0.778 & 0.791 & 0.758 & 0.711 & 0.780 & 0.802 & 0.845 & 0.069 \\ -based & Future-deep & 0.707 & 0.786 & 0.785 & 0.757 & 0.761 & 0.800 & 0.825 & 0.853 & 0.062 \\ \hline Deep-learning & Topo-LSTM & 0.699 & 0.771 & 0.712 & 0.728 & 0.740 & 0.793 & 0.818 & 0.844 & 0.067 \\ -based & CasCN & 0.720 & 0.791 & 0.797 & 0.729 & 0.757 & 0.792 & 0.818 & 0.849 & 0.065 \\ -based & ExoFIA & **0.753** & **0.799** & 0.797 & 0.754 & **0.825** & **0.853** & **0.868** & **0.882** & **0.049** \\ \hline \end{tabular}
\end{table}
Table 7: Prediction performance of ExoFIA and baselines when observation time is set to 3 h. The best result in each column is in boldface.  
Figure 14: Cumulative distribution function (CDF) of number of posts per cascade for different  velocities.
# Breast Cancer Image Classification Using Phase Features and Deep Ensemble Models
## 2 Literature Review
### Hybrid Methods Based on CNN  
Hybrid methods that combine CNN feature extraction with machine learning classifiers have gained attention for breast cancer classification. This approach aims to keep the strengths of both deep and traditional learning techniques, improving interpretability, generalization, and performance. Table 3 summarizes recent hybrid methods applied to breast cancer detection in mammography.  
One approach integrates CNN-based feature extraction with SVM classification, using variants of AlexNet, GoogleNet, and ResNet [21]. This system achieved 97.9% accuracy on DDSM and 95.4% on MIAS using an 80/20 train-test split and 5-fold cross-validation.  
Another method applies the modified entropy whale optimization algorithm (MEWOA) to optimize features extracted from MobileNetV2 and NasNet Mobile [22]. This method reports accuracies of 99.7% on INbreast, 99.8% on MIAS, and 93.8% on DDSM with 10-fold cross-validation.  
An ensemble approach combining SVM, random forest, and sigmoid classifiers with CNN-based feature extraction reports 99.4% accuracy on MIAS and 98.5% on BCDR [23].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Literature review of recent hybrid convolutional neural network-based methods for breast cancer classification. “NR” = Not reported; other acronyms are listed in the acronym list.  
For multiclass classification, a CNN-BiLSTM hybrid model achieved 98.56% accuracy on MIAS and 92.26% on INbreast, demonstrating the benefit of combining convolutional and recurrent layers [37].  
A multiview classification model combined ResNeXt with Transformer encoders and multiplex convolutions reports 90.57% accuracy and 94.86% AUC on a private dataset [25].  
Another hybrid model used ResNet18 for feature extraction, combined with feature reduction using PSO, the dragonfly optimization algorithm (DFOA), and the crow search optimization algorithm (CSOA) and classification with weighted KNN, achieving accuracies of 84.35%, 83.19%, and 97.36% on the MIAS, INbreast, and WDBC datasets, respectively [27].  
Finally, a system combining texture features and an ensemble of classifiers with CNN refinement achieved 93.5% accuracy on MIAS and 91.45% on DDSM [28]. Table 4 provides a comparative overview of the advantages and limitations of machine learning, deep learning, and hybrid methods for mammography-based breast cancer classification.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 8 Simulation Results
### Evaluation of the Scheduling Sequence
#### 8.3.3 Maximum Time Spent per Lane of the UAVs  
To better understand and interpret the results of the scheduling, we provide more results showing the maximum time spent of the UAVs per lane and the number of UAVs that crossed the intersection per lane.  
The maximum time in system per lane when \(G\) = 80 and using mode 2 and parameters in Tables 2 and 3 is shown in Figure 21. Let lane 1 be the left-most lane, let lane 2 be the second left-most lane, let lane 3 be the middle lane, let lane 4 be the second right-most lane, and let lane 5 be the right-most lane. Referring to Figure 3, UAVs that will turn left in the intersection can take either lane 1 or lane 2 while UAVs that will move straight in the intersection can take either lane 3 or lane 4, and UAVs that will turn right must take lane 5.  
As shown in Figure 21, UAVs in lane 1 and lane 2 mostly experience longer maximum time in the system compared to the UAVs in other lanes. Aside from the reason that the trajectories of the UAVs in lane 1 and lane 2 are longer since they are left-turning UAVs,  
Figure 21: Maximum time in system of UAVs per lane using mode 2 and \(G\) = 80.  
Figure 20: Average scheduling run time of UAVs every \(k\Delta t\) using mode 2.  
they also have higher possibility of conflict in the intersection compared to the straight moving and right turning UAVs, resulting in longer waiting time before they can enter the intersection. Consequently, the UAVs in lane 1 and lane 2 mainly contribute to the increase in the average time in the system as the arrival rate increases.