esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Constitutive Model  
In industrial and technological applications, many materials exhibit an anisotropic elastic behavior because of the fiber orientation, such as fiber-reinforced composites, reinforced rubber, wood, arterial wall, and heart tissue. In particular, they present nonlinear elastic and anisotropic characteristics under large deformations because of the rearrangements in the microstructure, such as the reorientation of the fiber directions with deformation. Hence, the simulation of these nonlinear effects requires constitutive models formulated within the framework of anisotropic hyper-elasticity [25].  
Hyper-elastic materials are expressed in terms of the strain energy potential, which defines the strain energy stored in the material per unit of the reference volume as a function of the deformation at that point in the material. Strain-based and invariant-based formulas are used to express the strain energy potential of anisotropic hyper-elastic materials. Theories related to these methods have been applied in computational biomechanics [20; 21; 22; 23; 24; 26; 27; 28]. In particular, the Fung [20] and HGO models [22; 23; 24] have been used widely in various simulations of the arterial wall and heart tissue. In the Fung [20]model, the underlying assumption is based on a strain-based formulation in that the preferred material directions are initially aligned with an orthogonal coordinate system in the reference configuration. These directions may become non-orthogonal only after deformation. Concurrently, in the HGO model [22; 23; 24], unlike in the strain-based formula, the fiber orientation in the invariant-based formula does not need to be orthogonal in the initial configuration. Therefore, the authors proposed a constitutive law for the description of the mechanical response of arterial tissue and considered the arterial wall as a thin-walled cylindrical tube consisting of layers.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 1 Introduction
### Motivation and Contribution  
All prior works to study coverage performance of mmwave cellular network [23; 24; 25; 38; 39; 40] are based on the standard distance-dependent path loss model. The presence of blockages in an environment is abstracted to a LOS probability function that differentiates between LOS and NLOS paths by separate path loss exponents. This paper presents an alternate approach by modeling blockages in the environment as an element of large-scale fading with a certain probability of occurrence. As compared to the previous work, our main contributions can be summarized as follows:  
* The SEPLM is validated for mmwave NLOS link using experimental measurement data from NYUSIM for an urban outdoor scenario.
* A system model is proposed where LOS links follow the standard distance-dependant path loss model and NLOS links are modeled using SEPLM. A 3 GPP LOS probability is employed to determine the LOS and NLOS link state, and the impact of blockages in the surroundings is modeled as path loss parameters.
* Based on the system model, stochastic geometry is used to derive generalized mathematical expressions for SINR coverage probability and ASE. The expressions are numerically analyzed to observe the performance over a range of SINR thresholds and BS densities.  
Leveraging on the derived results, it has been observed that the SINR coverage probability varies with the path loss parameters that define different blockage environments. At high BS densities, coverage probability decreases exponentially and approaches zero. The ASE is found to be maximum for a certain BS density and approaches zero as density is further increased. Contrary to the noise-limited assumption of mmwave at a high cell radius, our proposed model shows that for cell radii 100-200 m, the coverage performance is degraded by network interference.  
The rest of the paper is organized as follows. Section 2, presents our system model and all assumptions related to our analysis. Section 3, includes the mathematical formulation of downlink SINR coverage probability and ASE. In Section 4, the simulation and analytical results with discussions are presented. Section 5 concludes the paper.
## 1 Introduction
This article makes the following key contributions:  
(1) Inserting the CPICs of a parabolic antenna into the object detection network, a new method for parabolic antenna detection is proposed, including two steps: determining the component prior and detecting the three statuses of the parabolic antenna. (2) Using the scattering prior which is obtained by electromagnetic simulations, the imaging characteristics of the parabolic antennas of satellites are analytically determined. (3) Compared with previous target detection networks, the proposed method achieves better detection effects for key components. The proposed method's overall structure is depicted in Figure 1. In Figure 1, firstly, we obtain the original echo data of the satellite model through electromagnetic simulation software, as mentioned in \"Raw data of satellite echoes\". Then, using the classical RD imaging algorithm, we obtain the \"Imaging result of satellite\". Additionally, by utilizing \"Analyzing the ICPC\" of the parabolic antenna, we extract the imaging characteristics of the parabolic antenna, providing an improvement approach for the next deep learning model. Finally, combining the ICPC of the parabolic antenna, we convert the recognition of the parabolic antenna into the detection of \"Specular reflection point\", \"Edge pair-point\", and \"Ellipse arc\", and we execute \"Training the Networks\". The organization of the remainder of this article proceeds as follows. In Section 2, the THz imaging characters of parabolic antennas are discussed. In Section 3, the Improved-YOLOv8 network is illustrated in detail. The electromagnetic simulation results and relevant analysis are provided, and the results are verified by the anechoic chamber experiment in Section 4. Finally, we give the conclusions of this paper in Section 5.
# A Multimodal Framework for Advanced Cybersecurity Threat Detection Using GAN-Driven Data Synthesis
## 4 Experimental Analysis and Results
### Evaluation of GAN Models for Data Synthesis
#### 4.3.3 Evaluation of DCGAN for Malware Image Data Synthesis  
To evaluate the synthetic image generation capability for the layer of the image-based pipeline, a Deep Convolutional GAN (DCGAN) was adopted to generate grayscale malware images that follow the structural patterns of the Malimg dataset, as previously detailed within Section 3. The primary goal of this evaluation is to assess how realistic and diverse the synthetic images are in comparison to real malware instances and to ensure they preserve intra-class visual features while introducing meaningful inter-class diversity.  
The generated images were compared to real malware samples using four key metrics that assess both structural similarity and distributional diversity. These metrics include the Structural Similarity Index Measure (SSIM), FID, average entropy, and an inter-class diversity index. The results, shown in Table 13, indicate that the DCGAN-generated samples maintain high structural integrity with an SSIM of 87.3% and achieve a moderate FID of 24.6, which proves reduced deviation between the real and synthetic image feature distribution. Even if this metric is not as low as other more generic image-oriented generation tasks, considering the specialized nature and class imbalance of the malware set of images, it can be considered very competitive for domain-specific image generation. To further examine the quality and randomness of the samples, average entropy was calculated per image and reached 6.38, indicating high pixel variability and confirming that the generated samples do not suffer from mode collapse or over-smoothing. Finally, the diversity index score at 0.72, measuring variation across generated malware classes, is an aspect that signifies adequate intra-class diversity and supports generalization across different malware families for future extensibility of the particular layer of the pipeline.  
Figure 7 demonstrates the PCA visualization diagram generated, illustrating and highlighting the inter-class and intra-class coherence. The results ensure the DCGAN model's capability to generate the training space with diverse malware-like patterns.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Value** \\ \hline Structural Similarity Index (SSIM) & 0.873 \\ Fréchet Inception Distance (FID) & 24.600 \\ Average Entropy per Image & 6.380 \\ Diversity Index (Inter-class) & 0.720 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Summary of DCGAN Performance Metrics for Malware Images.  
Figure 7: PCA Visualization Plot for real and synthetic images.
## 2 Beidou-Binocular Cooperative Navigation Model
### Binocular Vision Recognition and Perception Module  
The binocular vision recognition and perception module is a key component of the Beidou-Binocular Cooperative Navigation Model (BBCN), responsible for providing the robot with the capability for real-time perception of the local environment. Compared to the global positioning information provided by the Beidou navigation positioning module, the binocular vision system mainly focuses on identifying obstacles and dynamic changes in the robot's surrounding environment, while utilizing depth estimation algorithms to provide accurate three-dimensional spatial information. The binocular vision recognition and perception module closely collaborates with the Beidou navigation module to compensate for the limitations of the Beidou system in cases of signal interference or blockage. When Beidou signals fluctuate, the binocular vision system can generate a local environment map using methods like visual SLAM, providing the robot with short-term alternative positioning and path planning capabilities. Under normal conditions, the fusion of binocular vision recognition and Beidou navigation can further enhance the accuracy and robustness of path planning.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Data Gathering Methods  
Our prototype of the MASU framework implements three different methods for the provision of the data retrieval services (Publish, Find, Subscribe, and Data Dissemination services) included in the CoSM mechanism embedded in the MASU framework. The three methods implemented are: AllJoyn [39], CoAP [40] and GCM [41; 42]. Alljoyn and CoAP offer different mechanisms to create a Peer-to-Peer proximity networks, enabling devices to share information directly among them, without depending on any centralized server. By contrast, if the devices are not physically close but they have Internet connectivity, they can share information using the GCM service, which is connected to the CoSM Server. Next we describe the implemented prototypes.
## 5 Experiments
### Performance Comparison of CRGPPO-TKL
#### 5.3.1 Comparison with Composite Scheduling Rules  
The 40 composite rules were structured into a two-level hierarchy: the first level involves four machine-job assignment strategies, while the second includes ten AGV charging strategies. The composite actions were indexed sequentially: Actions 1-10 represent the first machine-job pairing combined with each of the ten AGV charging strategies; Actions 11-20 represent the second pairing, and so on. To streamline the analysis, only the best-performing AGV charging strategies under each machine-job rule are presented here; the results of the remaining combinations are provided in Appendix A.  
Table 7 summarizes the mean completion times and ODR of CRGPPO-TKL and the selected composite actions across all test instances. Figure 8 visualizes the mean ODR and variance for the selected rules. The results show that CRGPPO-TKL consistently achieved the best completion times across all 10 test instances. The most competitive composite actions were Actions 1, 2, 11, and 12, with average ODRs of 6.6%, 12.2%, 17.9%, and 19.8%, and variances of 2.26, 7.05, 13.64, and 13.28, respectively. Among AGV charging strategies, the 7 th and 9 th showed superior performance, primarily because the opportunity charging mechanism uses a fixed time duration--AGVs continue charging even if fully charged--while the full-charging strategy terminates once fully charged. As a result, full charging tends to require less time, contributing to shorter completion times. These findings suggest that strategies such as \"EAA + STC\" and \"EAA + EAC\"--both of which involve no charging--outperform others, independent of the specific charging policy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline  & \To further validate the performance differences, we conducted independent two-sample \(t\)-tests between CRGPPO-TKL and each selected composite scheduling rule across all test instances. The results show that the performance differences are statistically significant (\(p\) < 0.05) in all comparisons, confirming that CRGPPO-TKL consistently outperforms composite rule-based methods.
###### Abstract  
This paper focuses on reconstruction of dynamic velocity and displacement from seismic acceleration signal. For conventional time-domain approaches or frequency-domain approaches, due to initial values and non-negligible noise in the acceleration signal, drift and deviation in velocity and displacement are inevitable. To deal with this deficiency, this paper develops a Walsh transform and Empirical Mode Decomposition (EMD)-based integral algorithm, or WATEBI in short. In the WATEBI algorithm, the Walsh transform is employed to realize vibration signal reconstruction. Next, the EMD method is used to eliminate the residual in the reconstructed signal. Finally, the trend term in velocity and displacement is removed by linear least-squares fit. This algorithm can be straightforwardly implemented by an ordinary computer. Reconstructed displacements and velocities from vibration of a simulated single-degree-of-freedom system and two-site measured ground motions in earthquakes validated the robustness and adaptiveness of this algorithm. It can be also applied to many other areas, like mechanical engineering and ocean engineering.  
W alsh transform; EMD; WATEBI algorithm; seismic acceleration; displacement; drift +
Footnote †: journal: Applied Mechanics and Applications
## 3 System Design Overview
### Technology and Method Selection
#### 3.6.2 Consensus Mechanism Selection  
The system uses the QBFT (quorum Byzantine fault tolerance) consensus mechanism, which is optimized for permissioned blockchains. Unlike proof-of-work (PoW), which is computationally expensive and inefficient for enterprise applications, QBFT provides fast, deterministic finality, ensuring that transactions (such as mentorship agreements, token-based incentives, and proposal submissions) are processed quickly and reliably.  
QBFT is selected over proof-of-stake (PoS) and delegated proof-of-stake (DPoS) because it provides enhanced security against Sybil attacks while maintaining low energy consumption. It ensures that only approved validators participate in transaction validation, reducing the risk of malicious actors influencing the network.
# RF-EMF Exposure near 5G NR Small Cells
## 2 Materials and Methods
### Typical Exposures
#### 2.4.3 The Non-User  
Because of 5G NR's lean broadcast signaling, the exposure from an NR base station is almost completely dependent on the usage. Furthermore, MaMIMO techniques are used to focus the emitted energy towards the user, so non-users are effectively less exposed.  
Therefore, for the exposure of a non-user, a distinction is made between the two extreme cases: (a) a non-user without any users connected to the base station under study, and (b) a non-user surrounded by a multitude of users (defined here as 'being in a mature network'). Whereas the former's exposure is simply \(E_{avg,min}\), the latter's depends on the distribution of the users, their usage, and the MaMIMO/beamforming capabilities of the base station antenna. Both the usage and the distribution of the users are stochastic variables in space and time, and in [19], a simulation study was performed to evaluate the 95 th percentiles of the resulting _spatiotemporal duty cycle_ (DC) (i.e., a measure of the spread of the base station's emitted power in space and time) of base station radios with varying antenna array sizes and thus varying MaMIMO capabilities. In general, the higher the number of antenna elements in the array, the lower the DC, as MaMIMO capabilities increase, e.g., to form more narrow beams directed towards users and away from non-users. For this study, the assumptions in Table 3 were adopted, for which \(DC_{site\#1,95}=0.20\) and \(DC_{site\#2,95}=0.96\) were found in [19]. Assuming fewer active users and/or shorter typicalusage, lower DCs were also found in [19]. However, a more detailed analysis was out of the scope of this study.  
As the site-specific spatiotemporal DC is a stochastic measure for the percentage of time a non-user is actually exposed by a base station servicing numerous active users. The exposure level \(E_{avg,non-user\ with\ other\ users}\) is then calculated as  
\[E_{avg,non-user\ with\ other\ users}=\sqrt{DC_{site,95}}\ E_{avg,max}\ [\mathrm{V}/ \mathrm{m}], \tag{5}\]  
with \(E_{avg,max}\) representing the exposure of a single user demanding as many of the base station's downlink resources as possible and \(DC_{site,95}\) the 95 th percentile of the spatiotemporal DC obtained in [19] for the specific site parameters (number of antenna elements and MaMIMO scheme, Table 3). Adding the DC in Equation (5) effectively accounts for the spreading of the base station's (maximized) radiated power over the area it services.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 5 Conclusions  
In order to accelerate the convergence speed of ant colony optimization algorithm in MANET routing, the IEMACO algorithm is proposed to select the next hop according to the transition probability _offset coefficient_. On this basis, this paper defines the pheromone generation method based on the remaining lifetime of the node and the remaining lifetime of the link, which combines node distance and residual energy for pheromone update. The simulation results show that the improved ant colony optimization algorithm can speed up the convergence of the algorithm under the constraint of global convergence, making it more suitable for mobile Ad Hoc networks with changing network topology. Furthermore, the suggested protocol for IEMACO can balance the network load and prolong the survival time of the network. At the same time, when the network topology changes, it can sense and select the path with better link quality in advance, reduce the route discovery frequency, and improve the overall performance of the network. In the future study, we will consider the algorithm performance under different parameter settings in the method of overcoming local optimal defects.  
Conceptualization, D.Y. and E.X.; Methodology, H.Z.; Software, E.X.; Validation, D.J.; Writing Original Draft Preparation, D.Y. and H.X.  
The work was funded by Educational Commission of Hubei Province of China grant number D20162702, in part by the National Key Research and Development Program of China grant number 2016 YFB1200202, the National Natural Science Foundation of China grant number 61771365, the Natural Science Foundation of Shaanxi Province grant number 2017J2022, and by the 111 Project grant number B08038.  
The authors declare no conflict of interest.  
Figure 11: Number of dead nodes versus node packet rate.
## 5 Efficiency
### Theoretical Analysis
If we do not consider data integrity and sender authentication, as in the previous schemes [3, 5, 6], the message size and private key size in the proposed scheme would be reduced to \(C_{ID}+C_{m}\) and \(C_{m}\), respectively, without any signature and public key overhead because each meter \(s_{i}\) can perform \(c_{i}=Enc_{k_{i}}(m_{i})\) and forward the aggregated result \(C+c_{i}\) to the next meter until it reaches the concentrator, where \(C\) is the ciphertext aggregated so far. This approach would thus be the most efficient of the schemes and resolve the imbalance problem (but would not be secure against active attacks, as with the previous schemes). Figures 3 and 4 illustrate the same experimental results on different scales for clear comparison. They show the total message size for metering data aggregations in relation to the number of participating smart meters. (For the proposed scheme, the signature is included in the result.) The message size in Figures 3 and 4 are represented in bits on a linear scale and a log scale, respectively. In the simulation, we set \(C_{\mathbb{G}}=512,C_{p}=160,C_{n}=160^{2},C_{n^{2}}=160^{4},C_{ID}=64,C_{t} =32\), and \(C_{m}=32\). Each element size in the groups is chosen for an 80-bit security level. As Figure 3 demonstrates, the proposed scheme requires the least communication overhead since the message size is only determined by the number of meters in the aggregate group and the size of an identity and a message (that is, the metering data), which are much smaller than that of the element in \(\mathbb{Z}_{n^{2}}^{*}\). Therefore, efficiency is much improved in the proposed scheme in terms of communication overhead.
## 2 Literature Review  
### Target Detection  
The principal objective of perforating robot target detection techniques is to employ machine vision systems for the precise identification and localization of target objects. The initial target detection methods, including R-CNN [8], Fast R-CNN [9], and Faster R-CNN [10], employ region proposals to identify potential regions for consideration, subsequently performing classification and regression. Despite their enhanced performance, these methods are characterized by elevated computational costs, rendering them unsuitable for real-time applications. To address the issue of real-time performance, [PERSON] et al. [11] introduced YOLO (You Only Look Once) in 2016. This innovation streamlines target detection into a regression task by leveraging a single convolutional neural network to predict bounding boxes and category probabilities directly over the entire image, thereby negating the necessity for prior region screening. This approach allows for a comprehensive consideration of image information, thereby reducing background misclassification and enhancing detection accuracy. Subsequently, the team proceeded to refine YOLO with several subsequent iterations [12; 13], which markedly enhanced the algorithm's accuracy and robustness. Nevertheless, it remained inferior to the two-stage detector in terms of localization precision. In the same year, [PERSON] et al. [14] proposed SSD (Single-Shot MultiBox Detector), which also achieves rapid and highly accurate detection without the generation of candidate regions. The SSD is capable of responding flexibly to the detection of objects of varying sizes, ranging from large to small. This is achieved through the extraction of features from different layers of the network, which enhances the comprehensiveness and accuracy of the detection process. In SSD, multiple sets of predefined frames are allocated to each feature map unit, with varying dimensions and scales. These are designed to match and predict objects of corresponding sizes. To optimize the performance of the single-stage detector, particularly in the context of multi-scale targets, it is evident that SSD still exhibits shortcomings in the detection of small targets, the handling of complex backgrounds, and the accuracy of prediction. Despite these shortcomings, SSD exhibits notable advantages in real-time target detection. To enhance the performance of single-stage detectors, particularly when dealing with multi-scale targets, RFBNet [6] introduces an innovative Receptive Field Block (RFB) based on SSD. This aims to augment the network's capacity to detect multi-scale targets while maintaining high processing speed and efficiency to meet real-time requirements.