esa-sceva/satcom-chunk-collection huggingface dataset:
# Software-Defined Heterogeneous Vehicular Networking: The Architectural Design and Open Challenges
## 1 Introduction
Nevertheless, despite of several advantages that SDN brings to a networking infrastructure, it is also vulnerable to a number of security attacks since malicious entities may launch attack on either the data plane via targeting the network elements from within the network itself and via the southbound application programming interface (API), by directly attacking the control plane as it acts as the centralized point of intelligence for the entire underlying network or on the applications plane by targeting certain specialized applications and via the northbound API. However, ensuring security in a SDN-based network remains beyond the scope of this article. Although a number of architectures have been recently proposed for guaranteeing an enhanced network resource management in VANETs (kindly refer to Section 3 for details), most of them have not accounted for the unique VANET-associated features and characteristics in their designs, i.e., frequent changes in network topology owing to the highly dynamic behaviour of vehicles in the data plane, extremely large and distributed network, stringent delay constraints, the need for efficient and smooth handovers, etc. Moreover, a number of these architectures primarily rely on accumulating the centralized intelligence in a _single_ centralized SDN controller, which on the one hand, provides a global view of the entire underlying network, but on the other hand, may become a _single point of network failure_ in case of any unforeseen event. Thus, a re-design of the existing vehicular networking architectures is highly indispensable. Accordingly, this article is one of the first few research studies to bring forth the notion of a highly reconfigurable software-defined heterogeneous vehicular networking (SDHVNet) architecture to facilitate rapid network innovation for meeting the stringent performance requirements of diverse safety-critical vehicular cooperative applications and services. SDHVNet is a robust and performant next-generation heterogeneous networking architecture for designing intelligent transportation systems (ITS). In contrast to the existing architectures proposed in the research literature, centralized intelligence is augmented with the localized intelligence to avoid a _single point of network failure_. The remainder of this article is organized as follows. In Section 2, we outline a brief background of vehicular networks, analyse the key radio access technological candidates for vehicular communication along with their potential and limitations and discuss the need for heterogeneous networking. Section 3 summarizes the current state-of-the-art in the context of ITS. Section 4 depicts our proposed hierarchical and logical architecture for the envisaged SDHVNet. Six key design challenges, together with their probable solutions, in the context of the deployment of safety-critical applications on such SDHVNets are also deliberated. Finally, conclusions are drawn in Section 5.
# Carbon Oxides Gases for Occupancy Counting and Emergency Control in Fog Environment
## 1 Introduction
They developed a time-series model to predict the number of people in an academic staff room and cinema theatre. [PERSON] et al. [18] proposed an indoor estimator based on real-time CO\({}_{2}\) measurements. They used a Feature Scaled Extreme Learning Machine algorithm, which is a variation of the standard Extreme Learning Machine (ELM), to identify the dynamic model. In [19], the authors presented a simple algorithm which uses indoor carbon dioxide concentrations to provide estimated occupancy profiles in office buildings. They also provided arguments to estimate the reliability and usefulness for service. [PERSON] et al. [20] proposed the PerCCS algorithm which uses task-driven Sparse Non-negative Matrix Factorization (SNMF) to learn about the CO\({}_{2}\) data. They explored the possibility of estimating a person count from CO\({}_{2}\) sensors already integrated in everyday room air-conditioning infrastructures. Multiple sensors (e.g., CO\({}_{2}\), light, motion, sound, temperature, non-directional doorway beam-break sensors) were combined to recognize human occupancy in [21; 22; 23]. Motivated by the works mentioned above, the main contributions of this paper include: (i) Design a WSN system to estimate human occupancy and potential fire in Fog Computing environment. Carbon oxide gases data are collected and analyzed, and the latency of control loop is compared with that obtained from Cloud platform; (ii) Extend a Fog computing simulator-iFogSim by giving it data analysis capacity. The original discrete event based framework focuses on the behavior of fog devices. Some object models may not specify with more realistic details. For example, tuples transmitted among devices which are used for describing information exchange do not carry data; (iii) Use machine learning algorithms to train and test the occupancy model based on the collected data. The remainder of the paper is organized as follows. Section 2 presents the designed system within a building. We also show the extension of iFogSim in this section. Section 3 describes simulation results. Finally, Section 4 concludes this paper with directions to the future work.
###### Abstract  
This study proposes a deep learning-based vertical decomposition model for ionospheric Total Electron Content (TEC), which establishes a nonlinear mapping from macroscale TEC data to vertically layered electron density (Ne) spanning 60-800 km by integrating geomagnetic indices (AE, SYM-H) and solar activity parameters (F10.7). Utilizing global TEC grid data (spatiotemporal resolution: 1 h/5.625\({}^{\circ}\)\(\times\) 2.8125\({}^{\circ}\)) provided by the International GNSS Service (IGS), a Multilayer Perceptron (MLP) model was developed, taking spatiotemporal coordinates, altitude, and space environment parameters as inputs to predict logarithmic electron density ln(Ne). Experimental validation against COSMIC-2 radio occultation observations in 2019 demonstrates the model's capability to capture ionospheric vertical structures, with a prediction performance significantly outperforming the International Reference Ionosphere model IRI-2020: root mean square error (RMSE) decreased by 34.16%, and the coefficient of determination (R\({}^{2}\)) increased by 28.45%. This method overcomes the reliance of traditional electron density inversion on costly radar or satellite observations, enabling high-spatiotemporal-resolution global ionospheric profile reconstruction using widely available GNSS-TEC data. It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation.  
Keywords:total electron content; electron density decomposition; deep learning; ionospheric modeling +
Footnote †: journal: _arxiv.org_
# Efficient FPGA Implementation of Automatic Nuclei Detection in Histopathology Images
###### Abstract  
Accurate and efficient detection of cell nuclei is an important step towards the development of a pathology-based Computer Aided Diagnosis. Generally, high-resolution histopathology images are very large, in the order of billion pixels, therefore nuclei detection is a highly compute intensive task, and software implementation requires a significant amount of processing time. To assist the doctors in real time, special hardware accelerators, which can reduce the processing time, are required. In this paper, we propose a Field Programmable Gate Array (FPGA) implementation of automated nuclei detection algorithm using generalized Laplacian of Gaussian filters. The experimental results show that the implemented architecture has the potential to provide a significant improvement in processing time without losing detection accuracy.  
FPGA implementation; hardware architecture; image processing; histopathology; generalized Laplacian of Gaussian filter; nuclei detection; mean Shift clustering
## 3 Proposed Method
### Adaptive Bin Estimator
#### 3.3.1 Feature-Embedding MLPs (\(E_{n}\))  
The decoded features are first passed through feature-embedding MLP layers. The embedding MLPs project the \(n\)th decoding features \(D_{n}\) with a size of (\(H_{n}\), \(W_{n}\), \(C_{n}\)) to become the \(n\)th embedding features, \(e_{n}\), with a size of (\(H_{n}\), \(W_{n}\), \(C_{e}\)). The channel dimension \(C_{e}\) of embedding features now become the same size, which is referred to as embedding space. Table 1 shows the detailed architecture of feature-embedding MLPs, where the proper channel dimension is assigned by \(C_{e}=32\) in our experiments. The subsequent layers within the AdaDRBins module utilize these embedding features to determine the appropriate bin division for each video frame.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Input Channels** & **Output Channels** & **Activation** \\ \hline FC & \(C_{n}\) & 64 & GELU \\ FC & 64 & \(C_{e}=32\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of feature-embedding MLPs while keeping the same \(H_{n}\) and \(W_{n}\).  
Figure 7: The detailed structures and signal flows of all modules in the AdaDRBins estimator. (**a**) Structures of feature-embedding MLPs (Ek) and bin-initialization MLPs (INIT). (**b**) Structures of bin-splitter MLPs (Sk) and bias-prediction MLPs (Bk). (**c**) Structures of hybrid regression layer (HREG) and weighting-sum linear combination (LCOMB) with 8q = 32 bins.
# Satellite-Based Localization of IoT Devices Using Joint Doppler and Angle-of-Arrival Estimation
## 4 Likelihood Derivation
### Joint Likelihood of Doppler and Angle of Arrival  
Since the three measured variables are assumed as mutually independent, the joint likelihood of the Doppler and AoA is thus the product of the two likelihood functions, \(p(\mathbf{Z}^{(k)}|\mathbf{x})=g_{1}(f_{\mathrm{d}}^{(k)}|\mathbf{x})\times g_{2 }(\Phi^{(k)},\Theta^{(k)}|\mathbf{x})\). Moreover, to incorporate measurements from multiple satellites, the likelihood of the \(K\) measurements is computed as the product of a single measurement,  
\[p(\mathbf{Z}^{(1:K)}|\mathbf{x})=\prod_{(k)=1}^{(K)}p(\mathbf{Z}^{(k)}| \mathbf{x}), \tag{10}\]  
where \(\mathbf{Z}^{(1:K)}\equiv\{\mathbf{Z}^{1},\mathbf{Z}^{2},...,\mathbf{Z}^{(k)}\}\), and \(K\) is the total number of measurements taken from multiple satellites. Note that this computation method holds true under the assumption of having independent measurement errors for a number of observed measurements [38].
## 3 Backscattering Scenarios
### Simulation  
Backscattering scenarios were simulated in CST Microwave Studio to obtain the frequency response signals processed afterward with the Cauchy and VCM methods. A perfectly conducting thin wire, sphere, plate, and cylinder were used as canonical objects, illuminated with a plane wave and placing electric field probes in different positions. An example of the simulated backscattering signals of a metal sphere and the comparison of its reconstructions are presented in Figure 2. Here, the difference in the input signal for both approximations is small compared to the dimension of the data; however, it is calculated using FSV.  
On the other hand, the set of CNRs calculated for this scenario is presented in Figure 3, where the pole shifting in VCM leads to changes in the reconstructed signal.  
Figure 2: Metal sphere signal approximation using the Cauchy and VCM methods.
## 5 Optimization Control Strategies in Bems
### The Impact of Dual Optimization Techniques in BEMS for a Commercial Building  
Building optimization problems are considered MILP problems that have been solved using MPC [117]. Intelligent controlling has been used to manage loads efficiently and an ANN strategy has been adopted to maintain a comfort zone in the building for the occupant, with an MILP scheduling technique to decay the peak demand of consumer [166]. An energy management agent (EMA) consists of an ANN and MPC for the modeling and optimization of building flexibility. The Monte Carlo Tree search-based planning and control was used to find the optimal policies with an ANN. The system can predict the demand for a day ahead and has a tiny prediction error [167]. The proposed system consisted of an ANN and MPC. In contrast, an ANN was used for renewable energy (i.e., solar and wind) forecasting and ensured the optimized usages of generated energy, and MPC is adopted for intelligent home control [168].  
The ANN is used to accurately predict power consumption and indoor temperature selection by given weather, occupancy, and temperature setpoints as input. At the same time, a GA has been taken to adapt to the ANN to minimize energy consumption, and an optimization control strategy was assessed in case of the day ahead and MPC [169].  
The uncertainty of environmental variables and users' preferences has been tackled using a data-driven machine-learning approach. Furthermore, a lifelong multitask framework was adopted to exploit structural similarities in control policies as there were different room sizes in buildings. Kernel-based learning was pursued as well to mitigate the non-linearity policy. Finally, a dual decomposition method was employed to cope with DR constraints across the spaces, transforming the overall problem into a series of unconstrained stochastic optimization problems for individual rooms. The method was verified via numerical experimental based on semi-real data sets [170].  
Three artificial intelligence techniques were used to solve the problem of energy demand planning in smart homes. First, the modification of the elitist none-dominated sorting genetic algorithm II to demand-side management was applied and accounted for electricity fluctuations over time, priority in the use of equipment, operating cycles, and a battery bank. Second, the forecast of demand-side consumers, distribution generation, renewable energies, and weather for a day ahead from the nearest meteorology office was considered for demand-side management by employing the SVR technique. Third, the k determined user comfort levels through the cluster technique [171]. Table 2 shows a full explanation of each optimization control technique, along with its benefits and drawbacks.
## 2 Three-dimensional Object Detection Techniques
### Multimodal Methods
#### 2.2.1 Two-dimensional Image and Point Cloud  
[PERSON] et al., 2018, used both RGB images and depth information for 3D object detection. In this research, CNN is used on an RGB image to generate region proposals, and then in the proposed regions depth information is merged to create a 3D frustum. After that, 3 Dinstance segmentation and \"amodal 3D box estimation\" is performed to view the object behind an obstacle. This method works at real-time speed and exhibits high recall, even for detecting small objects. But this method relies heavily on the region proposed by the 2D object detector at the beginning. Therefore, an appropriate algorithm for region proposal in the 2D plane is essential for the overall success of the entire process [61]. Similar methods are observed in [62; 63; 64; 65].  
LiDAR-only methods (i.e., point cloud-based) are improved by incorporating RGB images in Point-RCNN [31], VoxelNet [37; 66] and PointPillars [44]. These methods are similar to PointPainting [67; 68; 69; 70].  
Another blended method between a point cloud and an RGB image fuses features of point louds and features of images [71; 72; 73; 74; 75; 76; 77; 78; 79; 80]. From collective features, the region of interest is identified. In these regions, 3D object detection is performed afterwards. Although combining RGB and LiDAR point cloud information is complex, it exhibited better performance in outdoor scene 3D object detection than the standalone LiDAR voxel method [91].
# Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
## 4 Performance Evaluation
### Results and Discussion
For the NNRW and NNRW2D models, the weights and bias are randomly configured from \([-1,1]\). The comparison models are fully trained with the sigmoid activation function, and 30 independent trials are performed on each dataset. As shown in Table 2, the indexes accuracy, precision and recall score are calculated. The SCN-H model has the highest recognition rate, followed by the SCN-, NNRW- and  
Figure 6: Testing results of the SCNs on different factor \(\alpha\). SCN2D-based models, and the NNRW2D-based model obtains the lowest accuracy. The experimental results show the SCN-based models are superior to the NNRW-based models on the training samples with same data dimension. And the SCN model built with the hybrid data has learned better fault representation on each dataset. We also evaluate the effectiveness and advantages of the proposed ensemble SCNs on the JNU, SEU and UoC datasets with the existing ensemble models: DNNE [29], SCNE [30], RNNE-RVFL and RNNE-SCN [32]. For the SCNs ensemble algorithm, the average, voting and negative correlation learning (NCL) [40] strategies are used for ensemble learning. As shown in Table 3, the SCN ensemble models with NCL strategy obtain higher values on  
Figure 8: Performance comparison on SEU dataset. Figure 7: Performance comparison on JNU dataset. JNU and SEU dataset, and are comparable on the UoC dataset. The evaluation results show that the SCNs ensemble model with sub-learners built on multiple source training data obtains comparable fault classification results. Moreover, for the SCN-ensemble methods, due to the proposed method only having three sub-learners, the average and NCL strategies obtain higher resulted values than the voting strategy.
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 2 Related Work  
Different methods and systems have been proposed in the literature for monitoring vehicle violations. This research focuses on speed monitoring, passenger number monitoring, and seatbelt status monitoring through the use of edge, fog, and cloud computing.  
The edge computing approach has been recently used extensively because of its ability to share computational and communication resources with terminal devices, and it consequently improves the overall performance [19; 20]. In the vehicular context, edge computing has the potential to support efficient traffic management and monitoring. However, this field still faces challenges, including the amount of data, cooperation among vehicles, and privacy, and these challenges can be tackled by different methods, such as federated learning [21]. Edge computing has been used for traffic management (e.g., [22; 23]), but with little focus on traffic violation monitoring (e.g., [12; 24; 25]). Similarly, fog computing has been used for traffic management (e.g., [19; 26]) but with little focus on monitoring violations (e.g., [27]). The proposed edge and fog computing systems are dominated by the monitoring of speed and traffic signals, while passenger counting and seatbelt status checks have not been addressed within these computing approaches. To the best of our knowledge, this is the first study that combines speed estimation, passenger counting, and seatbelt status checks using IoT with edge, fog, and cloud computing.
# Improving Adversarial Robustness via Distillation-Based Purification
## 2 Related Work on Adversarial Defense
### Adversarial Training  
_Adversarial Training_ involves training a model on adversarial examples (AEs) along with the normal training data. The idea is to expose the model to adversarial attacks during training, so it can learn to resist them. Formally, adversarial attacks manipulate an original image \(x\) into the adversarial example \(x^{\prime}\) using the following method:  
\[x^{\prime}=x+\delta\quad s.t.\ \|\delta\|_{\infty}\leq\epsilon \tag{1}\]  
where \(\delta\) indicates the adversarial noise to be injected. The strength of the attack is controlled by ensuring that the \(L_{\infty}\) norm of the noise does not exceed a hyper-parameter, \(\epsilon\). The noise introduces subtle changes to the original image that are imperceptible to the human eye [18]. Various adversarial attacks have been developed over the years. For example, Fast Gradient Sign Method (FGSM) [8] creates AEs by adding a perturbation in the direction of the gradient of the loss with respect to the input data. Projected Gradient Descent (PGD) [9] is an iterative version of FGSM, which applies the perturbation step multiple times, each time projecting the adversarial example back into a valid input space. A Carlini and Wagner (CW) [11] attack is a more sophisticated optimization-based approach that aims to find the smallest perturbation necessary to induce misclassification, often resulting in more subtle changes and thus challenging AEs than the aforementioned two methods. In addition, it minimizes the distance of the original image from the corresponding AE, making it more likely to be misclassified using a distance function, such as \(L_{0},\ L_{2},\ L_{\infty}\), as an objective.  
AdvProp [19] enhanced robustness of the model by adversarial training using a mini-batch consisting solely of normal data as well as a supplementary minibatch consisting of PGD-generated AEs. The AEs in the supplementary minibatch have different underlying distributions than normal examples, which helps to mitigate the issue of distribution mismatch and makes it easier for the model to learn valuable features from both clean and adversarial domains. Robust Contrastive Learning (RoCL) [20] proposes a novel adversarial training approach without the need for labeled data. It uses instance-wise adversarial attacks and a contrastive learning framework to maximize the similarity between transformed examples and their adversarial perturbations. Ref. [21] explores adversarial training with imperfect supervision, specifically with complementary labels (CLs), and proposes a new learning strategy using gradually informative attacks to address the challenges of this setting. The authors aim to reduce the performance gap between adversarial training with ordinary labels and CLs (such as noisy or partial labels).
# First Earth-Imaging CubeSat with Harmonic Diffractive Lens
## 2 A CubeSX-HSE Nanosatellite-Based Remote Sensing Platform  
### Nanosatellite Design  
The CubeSX-HSE nanosatellite was created by the Moscow Institute of Electronics and Mathematics at the National Research University Higher School of Economics (HSE). Its design is based on the 3U CubeSat platform OrbiCraft-Pro [22]. With this modular platform, new satellites can be rapidly configured from pre-designed components, off-the-shelf electronics, and open-source software libraries.