esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Secure Image Monitoring
### Image Compression and Aggregation  
In wireless sensor networks, data aggregation is performed when some nodes compress received sensed data from others nodes. As processing, memory and power resources are constrained, data aggregation distributes the processing burden among a set of sensors and reduces the amount of information to be transmitted over the network. However, nodes that are performing aggregation should be protected from attackers.  
Data aggregation protocols aim to combine and summarize data packets from several sensor nodes, trying to reduce the amount of data to be transmitted [73]. However, when providing security, aggregation processing must be designed properly. In some cases, data aggregators must decrypt every message they receive, aggregate the messages and encrypt the aggregation result before forwarding it. In other cases, received messages are not decrypted before aggregation [73]. Watermarks may also be combined to provide security for aggregated sensed data.  
In fact, as source nodes may be compromised, authentication mechanisms may be required before aggregation. A malicious node may provide false data packets that may reduce the quality of the aggregated data. Thus, all considered sources must to be checked and validated, adding complexity to the overall solution.  
The work in [74] exploits the similarity that may be present in multimedia data, compressing sensed data for transmission. A similarity model is defined in that work, separating relevant information into pieces. One image is captured as the standard data, and another image is defined as the compared data, for each monitoring sample. That information is then used in a similarity check to authenticate source nodes of the aggregated data, avoiding the processing of malicious \"wrong\" images. In a different way, the work in [68] combines data aggregation with watermarking, considering the aggregation of scalar and image data, hence providing the authentication of source nodes.  
Aggregation is not straightforward in wireless image sensor networks, since even visual data transmitted from neighbor nodes may have low similarities [3]. However, when performed, contributing sources should be checked, and aggregated data should be protected from attackers.  
Another approach to optimize image processing is the compressive sensing paradigm [75]. It exploits the information rate within a particular signal, removing redundancy in the signal during the sampling process. The idea is to perform compression during sensing functions, reducing computation for the compression of gathered data.  
Compressive sensing may be done for scalar and multimedia data, with different particularities. In [76], the authors address pixel-level fusion of infrared and visible images of the same scene, arguing that multi-resolution fusion will have high performance. Compressive session is then performed using the blended signal and wavelet transform, reducing the amount of information to be processed without degrading image quality. Obviously, the sensed data are also subject to attacks, demanding proper security mechanisms.
# A Lightweight Entropy-Curvature-Based Attention Mechanism for Meningioma Segmentation in MRI Images
## 3 Method
### Entropy-Guided Attention  
Information entropy, as a key metric for quantifying information uncertainty, has irreplaceable advantages in the segmentation task. On the one hand, information entropy shows a certain robustness to noise. In images with significant noise, information entropy can still effectively reflect the grayscale distribution characteristics, thereby guiding the segmentation process. On the other hand, due to its sensitivity to local features, information entropy can effectively identify structures and boundaries in the image, thus improving the accuracy of segmentation. However, the current application of information entropy mainly relies on the maximum entropy threshold method for information filtering based on grayscale values. Due to the wide distribution of gray values in MRI images, it is not feasible to apply this method directly to the whole image, and it is quite challenging to determine the gray threshold. Therefore, the direct application of information entropy in image segmentation is limited.  
Based on this, we make a series of improvements in this aspect. Considering the complexity and uncertainty of setting thresholds, instead of using a thresholding approach, we divide the feature mapping into multiple small windows and then use the top-k algorithm to select the most information-rich regions. For each window, we calculate the entropy of  
Figure 2: The structure of TSP. By weighting the feature maps with three learnable vectors, three 2D feature maps are obtained from the sagittal, coronal, and transverse planes after pooling.  
the grayscale values, quantifying the amount of information in that region. The formula for calculating entropy is shown as follows:  
\[E=-\sum_{i=1}^{n}p(x_{i})log_{2}p(x_{i}) \tag{2}\]  
where \(E\) is the entropy of the entire small window, \(n\) is the range of grayscale values, \(x_{i}\) is the gray value of the image, and \(p(x_{i})\) is the probability of the gray value \(x_{i}\). The higher the entropy value, the greater the amount of information, indicating a more complex image. We then select the top-k regions with the highest entropy, which usually contain more useful information. In order to enhance the feature representation of these regions, these selected high-entropy regions are merged with the original image, thus improving the accuracy of the subsequent processing.  
\[Y=Conv(concat(X,topk(E))) \tag{3}\]  
where \(Y\) represents the enhanced outcome of EGA, \(X\) represents the feature map before processing, and \(E\) represents the information entropy obtained from the windows.
# Research and Verification of Trajectory Tracking Control of a Quadrotor Carrying a Load
## 3 Controller Design
### Position Controller Design  
The process of deriving the control rate of the position loop is similar to the design process of the attitude angle. The control rate of the position loop controller can be obtained in the following way.  
The first step is to define the position error as:  
\[\left\{\begin{array}{l}e_{7}=Z_{d}-Z\\ e_{9}=X_{d}-X\\ e_{11}=Y_{d}-Y\end{array}\right. \tag{45}\]  
The expected intermediate virtual control amount is:\[\left\{\begin{array}{l}e_{8}=k_{7}e_{7}+\dot{Z}_{d}+\mu_{4}\gamma_{4}-\dot{Z}\\ e_{10}=k_{9}e_{9}+\dot{X}_{d}+\mu_{5}\gamma_{5}-\dot{X}\\ e_{12}=k_{11}e_{11}+\dot{Y}_{d}+\mu_{6}\gamma_{6}-\dot{Y}\end{array}\right. \tag{46}\]  
According to the derivation process of the attitude angle control law, the position control law is:  
\[\left\{\begin{array}{l}U_{1}=\dfrac{\left[\left(1-{k_{7}}^{2}+\mu_{4}\right) e_{7}+(k_{7}+k_{8})e_{8}-k_{7}\mu_{4}\gamma_{4}+\dot{Z}_{d}+g-\dot{d}_{z} \\ mL\dfrac{\left(\ddot{\beta}\sin\beta\cos\alpha+\ddot{\alpha}\cos\beta\sin \alpha+\dot{\beta}^{2}\cos\beta\cos\alpha\right)}{+\dot{\alpha}^{2}\cos\beta \cos\alpha-2\dot{\beta}\dot{\alpha}\sin\beta\sin\alpha}\end{array}\right](M+m) \\ \dfrac{M+m}{\cos\phi\cos\theta}\\ u_{x}=\dfrac{\left[\left(1-{k_{9}}^{2}+\mu_{5}\right)e_{9}+(k_{9}+k_{10})e_{ 10}-k_{9}\mu_{5}\gamma_{5}+\ddot{X}_{d}\right]}{-\dot{d}_{x}-\dfrac{mL\left( \dot{\beta}^{2}\sin\beta-\ddot{\beta}\cos\beta\right)}{M+m}\dfrac{}{U_{1}}\\ u_{y}=\dfrac{\left[\left(1-{k_{11}}^{2}+\mu_{6}\right)e_{11}+(k_{11}+k_{12})e_{ 12}-k_{11}\mu_{6}\gamma_{6}+\ddot{Y}_{d}-\dot{d}_{y}\right]}{mL\dfrac{\left( \dot{\beta}\sin\beta\sin\alpha-\ddot{\alpha}\cos\beta\cos\alpha+\dot{\beta}^ {2}\cos\beta\sin\alpha\right)}{+\dot{\alpha}^{2}\cos\beta\sin\alpha+2\dot{ \beta}\dot{\alpha}\sin\beta\cos\alpha}\end{array}\right](M+m)}{M+m}\right](M+m) \tag{47}\]
## 5 Conclusions
As cities evolve, the integrated system must be capable of adapting to changing needs and technologies without requiring complete overhauls. This necessitates forward-thinking design principles that prioritize modularity, interoperability, and energy efficiency. The integration of IoT, Digital Twins, and VR present several advantages, of which we would like to highlight the user interface and the cross-service applications. On one hand, accessing VR-based digital twins through Head-Mounted Display (HMD) devices, including VR headsets and AR glasses, unlocks a multitude of possibilities and benefits for urban management and planning. These interfaces offer users an immersive, first-person perspective of these digital, 3D replicas, enabling them to navigate and interact with the virtual environment in real-time. This immersive experience facilitates a deeper understanding of complex urban dynamics, enhances decision-making processes by visualizing the outcomes of various scenarios, and improves stakeholder engagement through interactive and engaging presentations of urban projects. Furthermore, the use of HMD devices in accessing digital twins aids in education and training, allowing city officials, engineers, and the general public to simulate and rehearse responses to emergencies, infrastructure developments, and urban planning strategies with unprecedented realism. On the other hand, the implementation of city cross-service applications, such as controlling streetlights based on parking occupancy, optimizing watering management triggered by presence detection, and signaling garbage container status through streetlight blinking, demonstrates the potential of the Digital Twin infrastructure for efficient urban management. Besides, it lays the groundwork for the invention and deployment of new applications that provide added value to both the urban technicians, the service awarded companies and, of course, the citizens. In conclusion, the Digital Twin infrastructure deployed at the IoTMADLab represents a pioneering approach to urban management and operational oversight. By bridging the physical and digital worlds through IoT-enabled devices and VR-based interfaces, the system enables real-time monitoring, analysis, and control of urban elements, paving the way for smarter, more sustainable cities of the future.
# On Modeling Antennas Using MoM-Based Algorithms: Wire-Grid versus Surface Triangulation
###### Abstract  
This paper focuses on antenna modeling using wire-grid and surface triangulation as two of the most commonly used MoM-based approaches in this field. A comprehensive overview is provided for each of them, including their history, applications, and limitations. The mathematical background of these approaches is briefly presented. Two working algorithms were developed and described in detail, along with their implementations using acceleration techniques. The wire-grid-based algorithm enables modeling of arbitrary antenna solid structures using their equivalent grid of wires according to a specific modeling recommendation proposed in earlier work. On the other hand, the surface triangulation-based algorithm enables calculation of antenna characteristics using a novel excitation source model. Additionally, a new mesh generator based on the combined use of the considered algorithms is developed. These algorithms were used to estimate the characteristics of several antenna types with different levels of complexity. The algorithms computational complexities were also obtained. The results obtained using these algorithms were compared with those obtained using the finite difference time domain numerical method, as well as those calculated analytically and measured. The analysis and comparisons were performed on the example of a rectangle spiral, a spiral, rounded bow-tie planar antennas, biconical, and horn antennas. Furthermore, the validity of the proposed algorithms is verified using the Monte Carlo methodology.  
method of moments; numerical methods; antennas; wire-grid; surface triangulation; computer modeling +
Footnote †: journal:
# Residue Cost Formation of a High Bypass Turbofan Engine
## 2 System Description  
### General Description of the Engine Operation  
Turbofan engines with a high bypass ratio are used in considerably large commercial and military transport aircraft. Particularly in modern aircraft, this part of the engine generates 75-80% of the total thrust.  
Figure 1 shows a schematic of a turbofan engine. From far upstream, where the air velocity relative to the engine is given by the flight velocity (\(w\)), the air is brought to the diffuser (D), which decreases its velocity in the flow direction. The air mass flow rate (\(\dot{m}_{a}\)) is carried to the fan (F); a fraction of the  
air is moved to the bypass nozzle (FN), and the remainder moves to the core engine (\(\dot{m}_{a}=\dot{m}_{ca}+\dot{m}_{ha}\)). The relation between air flowing through the FN (\(\dot{m}_{ca}\)) and gas turbine (\(\dot{m}_{ha}\)) is called the bypass ratio: \(\beta=\dot{m}_{ca}/\dot{m}_{ha}\). The fraction of the air mass flow rate \(\dot{m}_{ca}=\dot{\beta}\dot{m}_{a}/(1+\beta)\) is accelerated in the FN and exhausted to the environment, whereas the remaining fraction (\(\dot{m}_{ha}=\dot{m}_{a}/(1+\beta)\)) enters the compressor (C) of the two-shaft gas turbine. In the combustion chamber (CC), the compressed air is heated (\(\dot{Q}_{b}\)) by mixing and combustion with the atomized aviation fuel Jet-A1 (kerosene). The combustion reaction between this fuel and air, which is assumed to have a molar composition of 79% N\({}_{2}\) and 21% O\({}_{2}\), is given by:  
\[\begin{split}\text{C}_{n}\text{H}_{m}+\left(n+\frac{m}{4}\right) \left(1+\lambda\right)\left(\text{O}_{2}+3.76\text{N}_{2}\right)\rightarrow \left[n\left(1-k_{4}\right)-k_{1}\right]\text{CO}_{2}\\ +\frac{m}{2}\left(1-k_{4}\right)\text{H}_{2}\text{O}+\left[3.76 \left(n+\frac{m}{4}\right)\left(1+\lambda\right)\right.\left.-\frac{k_{2}+k_{ 3}}{2}\right]\text{N}_{2}\\ +\left[\left(n+\frac{m}{4}\right)\left(1+\lambda\right)+\frac{k_ {1}-k_{2}}{2}-k_{3}\right]\text{O}_{2}+k_{1}\text{CO}+k_{2}\text{NO}+k_{3} \text{NO}_{2}+k_{4}\text{C}_{n}\text{H}_{m}\end{split} \tag{1}\]  
where \(n\) and \(m\) are the numbers of C and H atoms present in the fuel, \(\lambda\) is the excess of air, and \(k_{1}\) to \(k_{4}\) are the stoichiometric coefficients for CO, NO, NO\({}_{2}\), and C\({}_{n}\)H\({}_{m}\), respectively. This reaction determines the chemical composition of the combustion gases. The mass flow rate \(\dot{m}_{cg}=\dot{m}_{ha}+\dot{m}_{f}\) and \(\dot{m}_{f}\) is the fuel mass flow rate. The combustion gases leave the CC at a substantially high pressure and temperature and are expanded in the high pressure turbine (HPT) and low pressure turbine (LPT) to generate useful work for driving the C and F, respectively. The exhaust gases exiting the LPT are accelerated and expelled through the exhaust nozzle (N) at considerably high speeds.
## 4 Description of the Proposed Topology Construction
### Design Issues of the Proposed Logical Topology
#### 4.5.3 Changing Lower-Level Leaders  
The lower-level leaders should be changed periodically to distribute the energy load. PEGASIS suggests changing the leader node in each round. However, for the proposed topology, if the lower-level leaders are changed at every round, it causes extra energy expenditure for negotiations to select leaders, as well as causing delay. In addition, the higher-level chain can be fully utilised if the lower-level leaders are changed after a number of rounds. Conversely, if the lower-level leaders are not swapped with other member nodes for a long time, they will quickly be drained of energy due to excessively long transmissions. Therefore, in the proposed logical topology, lower-level leaders are changed after \(R\) rounds, where the value \(R\) depends on the following criteria: (i) total energy dissipation in the network; (ii) maximum number of round before the first sensor node dies; and (iii) the delay introduced in the network for different number of rounds.  
Figures 13-15 show the simulation results, which are used to determine when the lower-level leaders should be changed. Figure 13 shows the relationship between \(R\) and the total energy spent in the network. The figure shows that there is no correlation between total energy consumption and \(R\). In contrast, Figure 14 shows that as the value of \(R\) increases, the network lifetime decreases. This is because, when the same sensor nodes are working as leaders for long periods, they deplete energy quickly compared with other sensor nodes. Figure 15 shows that the time delay decreases as the value of \(R\) increases. In conclusion, from the experimental results, shown in Figures 13-15, it is proposed that the lower-level leaders should be changed after \(C_{N}/2\) rounds.  
Figure 13: Leader selection time _vs_. total energy dissipation.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADSL & Asymmetric digital subscriber line \\ Alloc-ID & Allocation identifier \\ AMCC & Auxiliary management and control channel \\ APON & Asynchronous transfer mode passive optical network \\ BWmap & Bandwidth map \\ C-RAN & Cloud-radio access network \\ CDR & Clock and data recovery \\ DBA & Dynamic bandwidth allocation \\ DDSPON & Distributed dynamic scheduling passive optical network \\ EPON & Ethernet passive optical network \\ EqD & Equalization delay \\ FS & Framing sublayer \\ GMI & Gigabit media independent interface \\ GPON & Gigabit passive optical network \\ GTC & GPON transmission convergence layer \\ HEC & Hybrid error correction \\ IEEE & Institute of Electrical and Electronics Engineers \\ ISP & Internet services provider \\ ITU & International Telecommunication Union \\ LLID & Logical link identifier \\ MAC & Medium access control \\ MDI & Medium-dependent interface \\ MLM & Multi-longitudinal mode \\ MPCD & Multi-point control protocol \\ NG-PON2 & Next Generation Passive Optical Network Stage 2 \\ NRZ & Non-return-to-zero \\ LoF & Loss of frame \\ LoS & Loss of signal \\ ODN & Optical distribution network \\ OLT & Optical line termination \\ ONU & Optical network unit \\ ONU-ID & ONU identifier \\ ORL & Optical return loss \\ P2 MP & Point to multipoint \\ PCBd & Physical control block downstream \\ PCS & Physical coding sublayer \\ PHY & Physical interface \\ PLOAM & Physical layer operations, administration and maintenance \\ PMA & Physical medium attachment \\ PMD & Physical medium dependent \\ PON & Passive optical network \\ PSBd & Physical synchronization block downstream \\ \end{tabular}
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 2 The Motivation  
In this section, we present three different motivating factors that lead to this work.  
1. Currently, any multicast architecture deployed in the network has the following problems: 1. [label=()] 2. They have either weak or no admission control mechanism. 3. Multicast mechanisms that use a shared key for admission control have key revocation issues. 4. Multicast mechanisms that depend on a Public-Key Infrastructure (PKI) have single-point failure. Even though there are several articles available in the literature discussing various performance and security related issues in multicasting protocol, to our knowledge, none of these articles address all the above issues in total.
2. Router authentication is an important problem in a corporate network. Without proper router authentication in place, any malicious router (or a host that pretend to be a router) may propagate fictitious routing information. The current router authentication scheme has several performance issues.
3. In our earlier paper [1], we proposed the PrECast infrastructure to solve several attacks that are related to IP-Version 4 (IPv4) broadcasting. However, this framework assumes the existence of a secure multicast tree in a corporate network.  
We now address these issues in detail in this section.
## 3 Proposed Method
### 1D-CNN Backbone Model
Similarly, the rest of the temporal convolutional blocks and max-pooling layers realize the mappings \(\phi_{4}\), \(\phi_{5}\), \(\phi_{6}\), \(\phi_{7}\), \(\phi_{8}\), \(\phi_{9}\), and \(\psi_{2}\), \(\psi_{3}\), \(\psi_{4}\) to analyze the signals at different levels of the hierarchy of multi-scale features and reveal the information relevant to schizophrenia. These mappings, together with the mapping \(\chi_{1}\) implemented by the first FC1 layer, learn the feature extraction function \(f_{1}\), i.e.,  
\[f_{1}(x;\overset{\sim}{\theta})=\chi_{1}\circ\psi_{4}\circ\phi_{9}\circ\phi_{8 }\circ\psi_{3}\circ\phi_{7}\circ\phi_{6}\circ\psi_{2}\circ\phi_{5}\circ\phi_{4 }\circ\psi_{1}\circ\phi_{3}\circ\phi_{2}\circ\phi_{1}(x), \tag{2}\]  
where \(\overset{\sim}{\theta}\) represents the learnable parameters of the feature extractor. The features learned by \(f_{1}\) passed to the classifier \(f_{2}\), which is implemented by the FC2 with two neurons and a softmax layer; it first computes the activations \(\mathbf{a}= \begin{bmatrix}a_{1}&a_{2}\end{bmatrix} ^{T}\) and then the posterior probability of each class using the softmax function:  
\[p_{i}=p(C_{i}|\,x)=\frac{e^{a_{i}}}{\sum_{j=1}^{2}e^{a_{j}}},\ i=1,2. \tag{3}\]  
Finally, the class label (schizophrenia or healthy control) is predicted based on the posterior probabilities. In this way, the proposed model builds the end-to-end function \(f\) that takes an EEG trial \(x\) as input and predicts the class label \(l\), i.e.,  
\[f(x;\theta)=f_{2}\circ f_{1}(x)=l, \tag{4}\]  
where the feature extractor \(f_{1}\) and classifier \(f_{2}\) are learned together, and \(\theta\) stands for the learnable parameters of the model. The detailed architectures of the proposed 1D CNN model (Model-1) and three other models are presented in Table 1. \begin{table}
\begin{tabular}{c c c c}
**Model 1** & **Model 2** & **Model 3** & **Model 4** \\ \hline \hline Conv B1 & Conv B1 & Conv B1 & Conv B1 \\ \(1\times 11/5/1/16\) & \(1\times 11/5/1/32\) & \(1\times 11/5/1/16\) & \(1\times 11/5/1/16\) \\ \hline Conv B2 & Conv B2 & Conv B2 & Conv B2 \\ \(16\times 1/0/1/16\) & \(16\times 1/0/1/32\) & \(16\times 1/0/1/16\) & \(16\times 3/1/1/16\) \\ \hline Conv B3 & Max-Pooling & Max-Pooling & Max-Pooling \\ \(1\times 3/1/1/16\) & \(1\times 2/2\) & \(1\times 2/2\) & \(1\times 2/2\) \\ \hline Max-Pooling & Conv B3 & Conv B3 & Conv B3 \\ \(1\times 2/2\) & \(1\times 3/1/1/16\) & \(1\times 3/1/1/12\) & \(1\times 3/1/1/12\) \\ \hline Conv B4 & Conv B4 & Conv B4 & Conv B4 \\ \(1\times 3/1/1/12\) & \(1\times 3/1/1/16\) & \(1\times 3/1/1/12\) & \(1\times 3/1/1/12\) \\ \hline Conv B5 & Max-Pooling & Max-Pooling & Max-Pooling \\ \(1\times 3/1/1/12\) & \(1\times 2/2\) & \(1\times 2/2\) & \(1\times 2/2\) \\ \hline Max-Pooling & Conv B4 & Conv B5 & Conv B5 \\ \(1\times 2/2\) & \(1\times 3/1/1/12\) & \(1\times 3/1/1/8\) & \(1\times 3/1/1/8\) \\ \hline \end{tabular}
\end{table} 
Table 1: Best four models (kernel size/padding/stride/no. of kernels).