esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Public Safety Network  
A public safety network between victims and first responders is vital for search and rescue operations. Capable and fast emergency communication is needed for an effective public safety communication network. During public safety communication, efficient communication technology will save people's lives and improve connectivity among the SAR teams. WiMAX, WiFi, ad hoc, and long-term evolution (LTE) are the potential broadband communication types dedicated for public safety networks. However, these technologies are useless if a disaster occurs. Therefore, space technology is the best solution for public safety and disaster recovery when terrestrial communication systems are destroyed [54]. In this regard, drones play a vital role in public safety and disaster recovery/emergency services because they are able to reach areas human rescue teams cannot reach immediately and provide a critical first response.
###### Abstract  
It has long been recognized that fine aggregate (FA) plays a crucial role in the performance of asphalt mixture, especially for the viscoelastic behavior. In this research, 13 types of FA (1 natural sand, 5 stone chips, and 7 machine-made sands) were selected for investigation. Three indirect indicators (uncompact void content test, flow time test, and standard test method for index of aggregate particle shape and texture ASTM D3398) and three types of direct indicators (form, angularity, and texture) were employed to evaluate the geometric characteristics of FA and conduct comprehensive studies on the indicator system. Meanwhile, the effects of FA geometrical properties on the viscoelastic behavior of asphalt mortar were investigated. The results show that only the form indicator ratio of equivalent ellipse axis (\(E\)) and angularity indicator surface parameter (\(SP\)) can effectively distinguish different types of fine aggregates. The correlation analysis reveals that the parameters of the four elements in the Burgers model are negatively related to the form index (\(E\)) but positively related to the angularity index (\(SP\)), while the parameter retardation time (\(\tau_{r}\)) exhibits the opposite. This indicates that the use of less flat-elongated and more angular FA can increase both the overall stiffness and elastic component of asphalt mortar.  
fine aggregate; asphalt mortar; geometrical characteristics; representative particle size; viscoelastic behavior +
Footnote †: journal: Applied Sciences
## 3 Evaluated Scenario  
Physicians used to make healthcare judgments solely primarily on personal experience, patient indications and symptoms, healthcare professional expertise, and diagnostic laboratory analysis. The Internet of Medical Things (IoMT) devices has emerged as a critical tool for clinicians in obtaining accurate data and making more precise diagnoses. Realtime monitoring of people's health is one of the approaches to delivering health services based on the IoT paradigm. Health monitoring systems are commonplace, and they may collect data from IoMT devices and deliver it to a gateway via a wireless communication protocol. The medical team and family members can analyze vital signs data via the WBAN (Wireless Body Area Network). From clinical treatment to chronic illness prevention and control, WBAN systems are critical in healthcare systems. These technologies, for example, are used to track and treat cardiovascular illness in realtime [35].  
Figure 1 shows the proposed scenario for health, assisted ambient living [36], and wellbeing monitoring system for elderly people based on IoT, which was developed based on the architecture presented in [37]. Wearable sensors collect vital patient information. Contextual information, such as date, time, location, and temperature, can be added to this data. Health practitioners can spot unexpected trends and draw assumptions about the elderly's status by understanding the setting.  
This scenario includes sensors, gateways, routers, fog, cloud, communication with family members' devices, and an emergency unit. Sensors collect biological and contextual signals from the bodies of patients and their surroundings. Falls, heart attacks, and other causes can be detected using this data, which are supplied to the gateway using wireless communication protocols. A gateway must handle a variety of communication protocols as a point of contact between the WSN and the router. A gateway may collect data from several subnets, convert communication protocols, and provide additional higher-level services like data aggregation, filtering, and so on [38]. A router is responsible forthe load balancing strategy. When receiving gateway data, the router must decide whether to send it to the fog or the cloud. Both the cloud and the fog present their benefits. The cloud usually is more vital than fog in terms of resource capabilities; however, the fog is positioned closer to sensor and actuator devices. In emergency, cloud/fog stores data and sends an alert to an emergency unit and the elderly's family members. Family members' devices and the emergency unit can be smartphones, tablets, or other devices, ensuring that the alert reaches its destination.
## 2 Materials and Methods
### Coverage Path Planning  
In order to achieve full coverage sampling of the region, when planning a path, we first generate an ordered sequence of all sub-regions of interest, and the sequence represents the access order of sub-regions of interest. The coverage path is generated based on this sequence, and the UAV flies according to the path and sequentially visits each sub-region of interest to perform gas sampling operations. The Genetic Algorithm is an algorithm commonly used to solve the TSP problem in UAV path planning research [34]. It is a random global search algorithm proposed according to the theory of \"survival of the fittest\" in evolution theory [35], which uses coding techniques to transform optimization problems in practical fields into strings of chromosomal data. One chromosome corresponds to one possible solution. Then, according to the process of biological evolution in nature, excellent solutions are selected for genetic variation; that is, iterative optimization.  
In this study, we decompose the entire study area from both horizontal and vertical perspectives, resulting in more path points than the traditional CPP algorithm. The TSP solver is non-terminal polynomial-time hard (NP-hard), and the more waypoints it takes, the more time it takes to compute. To solve this problem, we transform the path of an entire space into a path with only one interested subspace, and obtain the path of its adjacent subspace by searching for the inverse order of the path points, since the entire space is uniformly decomposed, the number of the sub-region of interest in each subspace is the  
Figure 1: Coverage sampling design. (**a**) Basic decomposition unit. (**b**) Distribution of sampling points in a subspace. The red solid line represents the outline of the study area A. The yellow dot is the sampling point. (**c**) Space decomposition.  
same and the coordinate positions are the same except for the height. To solve this problem, we transform the path of an entire space into a path with only one interested subspace, and obtain the path of its adjacent subspace by searching for the inverse order of the path points, since the entire space is uniformly decomposed, the number of the sub-region of interest in each subspace is the same, and the coordinate positions are the same except for the height. In order to quickly plan the optimal detection path suitable for the detection task, this paper improves the population initialization method, fitness function and genetic operator based on the traditional genetic algorithm.
# Detection of Targets in Road Scene Images Enhanced Using Conditional GAN-Based Dehazing Model
## 3 Image Dehazing Model  
Image dehazing is considered a generative problem. A network learns from the training samples how to transform the degraded image into a clear image. As compared with the traditional methods that rely on a physical model, a deep learning-based generative model does not demand explicit computation of parameters such as a transmission map and atmospheric light. Model optimization is driven by the hazy/clear-image-pair dataset. To strengthen the learning algorithm for better dehazing, the realness of the generator result is challenged using a discriminator which is adversarially trained.  
We adopted the structure of cGAN, as shown in Figure 1, as our proposed image dehazing model. cGAN is a supervised learning model with two major modules--generator and discriminator. Like GAN, the generator module \(G\) learns the mapping from a random noise vector \(\mathbf{z}\) to the output \(y\) (\(G\): \(z\to y\)). The generator output (fake data), together with the real data, is then passed to the discriminator module \(D\). The discriminator is trained to determine whether the generator output is real or fake. The generator and discriminator, formed as an adversarial pair, work together in optimizing the realness of the generated image. The structure of cGAN is almost the same as GAN but with the additional information of \"label\". This additional input will guide/constrain the generator module to generate the desired kind of output. [PERSON] and [PERSON] proposed Dehaze-GAN [36] for image-to-image translation. We selected it as the base model. We then modified and extended the network for it to become our proposed image dehazing model. In the proposed model, the real data are the clear image and the label is the hazy input. With the random noise vector \(z\) and output \(y\), an additional variable \(x\) is added such that \(G\): \(\{x,z\}\to y\). The details of the generator network and discriminator are explained in the following sub-sections.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 2 Theoretical Foundation
### Prediction Model
#### 2.2.2 Artificial Neural Network Model  
ANN is a tool which can detect all possible interactions between the input and output from the provided training data. It also provides a flexible architecture for nonlinear modeling in fault detection, diagnosis [44], and prediction [45]. The framework of ANN depends on the characteristics of the data. Thus, the number layers and neurons are configured based on the essence of the input data. The widely used three-layer ANN model presented in Figure 1 is applied to model time series data, in which the neurons are acyclically linked.  
The nonlinear function \(f\) of \(Y_{nt}\) sequence from \(Y_{n(t-1)}\) to \(Y_{n(t-N)}\) is expressed as follows:  
\[\hat{Y}_{nt}=\omega_{0}+\sum_{j=1}^{H}\omega_{j}f\left(\omega_{0j}+\sum_{i=1} ^{N}\omega_{ij}Y_{n(t-i)}\right)+e_{t}, \tag{6}\]  
where \(\hat{Y}_{nt}\) is the prediction result at any given time \(t\), \(\omega_{ij}\) and \(H\) denote the hidden layer neuron weights and number, respectively. The corresponding input layers are \(\omega_{j}\) and \(N\). \(f\) represents a \"sigmoid\" function in this research, while \(e_{t}\) is a noise or error term.  
Figure 1: Schematic diagram of ANN.  
Additionally, to avoid the local minimum and overfitting, k-fold cross validation is applied to train the ANN. Finally, we evaluate the test data with the trained ANN [46].
# An Improved Weighted and Location-Based Clustering Scheme for Flying Ad Hoc Networks
## 3 Preliminaries
### Cluster Head Selection  
Due to the high mobility and frequent topology changes, there are many factors that can affect the performance of FANETs, including network load, communication bandwidth, and node mobility. Therefore, when selecting a cluster head, multiple factors need to be considered comprehensively, so that the clustering scheme can adapt to complex environments.  
In this paper, a cluster head selection algorithm is developed, where the relative mobility, adaptive node degree, node remaining energy, and average distance between nodes are jointly considered. A minimum threshold is set for each of the factors. When the four factors are all greater than the minimum threshold, the node is eligible to participate in the selection of CHs. The weight of the CH candidate node \(i\) is calculated as  
\[W_{i}=\omega_{1}E_{i}+\omega_{2}N_{i}+\omega_{3}M_{i}+\omega_{4}\frac{1}{D_{i}} \tag{9}\]  
where \(\omega_{1},\ \omega_{2},\ \omega_{3},\ \omega_{4}\) are the weights of the four factors, respectively, and \(\sum_{i=1}^{4}\omega_{i}=1\). The specific values are determined by the requirements of the practical application scenarios. In the above weight expression, \(E\) is the remaining energy ratio, \(N\) is the adaptive node degree, \(M\) is the relative mobility, and \(D\) is the average distance between nodes. The four factors are normalized to mitigate the effect of different scaling units.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 2 Background
### Previous Work
#### 2.3.5 General Strategy to Key Recovery
Given the chunk candidates \(\text{c}^{i_{0}}_{j0},\ldots,\text{c}^{i_{n}}_{j_{n}}\), with \(0\leq i_{0}<\cdots<i_{n}<\mathcal{N},0\leq j_{i}<m_{i}\), we define \(\text{combine}\big{(}\text{c}^{i_{0}}_{j0},\ldots,\text{c}^{i_{n}}_{j_{n}} \big{)}\) as a function that out puts a new chunk candidate \(c\) computed by setting \(c=\big{(}c_{j_{0}}^{i_{0}}score+\ldots+c_{j_{n}}^{i_{n}}score,c_{j_{0}}^{i_{0}} value\|\ldots\|c_{j_{n}}^{i_{n}}value\big{)}\). When \(i_{0}=0,i_{1}=1,\ldots,i_{N-1}=\mathcal{N}-1\), \(c\) is known as a full key candidate. The key enumeration problem entails choosing a chunk candidate \(c_{j_{i}}^{i}\) from each list \(L^{i}\), \(0\leq i<\mathcal{N}\), to form full key candidates \(c=\texttt{combine}\big{(}c_{j_{0}}^{0},\ldots,c_{j_{N-1}}^{\mathcal{N}-1} \big{)}\) conditioned to a rule on their scores [27]. An algorithm that enumerates full key candidates \(c\) is known as a key enumeration algorithm (KEA). For our key-recovery problem, we use an algorithm that combines two key enumeration algorithms and is based on one introduced in [11,37]. Moreover, we also exploit the in-memory representation of the secret key of SIKE mechanism to improve our results. This key-recovery algorithm will be described in Section 4.2.
###### Abstract  
In this paper, unmanned aerial vehicle (UAV)-assisted vehicular communications are investigated to minimize latency and maximize the utilization of available UAV battery power. As communication and cooperation among UAV and vehicles is frequently required, a viable approach is to reduce the transmission of redundant messages. However, when the sensor data captured by the varying number of vehicles is not independent and identically distributed (non-i.i.d.), this becomes challenging. Hence, in order to group the vehicles with similar data distributions in a cluster, we utilize federated learning (FL) based on an attention mechanism. We jointly maximize the UAV's available battery power in each transmission window and minimize communication latency. The simulation experiments reveal that the proposed personalized FL approach achieves performance improvement compared with baseline FL approaches. Our model, trained on the V2X-Sim dataset, outperforms existing methods on key performance indicators. The proposed FL approach with an attention mechanism offers a reduction in communication latency by up to 35% and a significant reduction in computational complexity without degradation in performance. Specifically, we achieve an improvement of approximately 40% in UAV energy efficiency, 20% reduction in the communication overhead, and 15% minimization in sojourn time.  
unmanned aerial vehicles; vehicle selection; federated reinforcement learning; personalized federated learning; attention mechanism Article
###### Abstract  
This study investigated the characteristics and strength of the dissimilar joints between carbon fiber reinforced plastic (CFRP) epoxy composites and aluminum alloys using two different heating methods, Ni/Al reactive multilayer films (RMF) and a low power continuous wave diode laser. To enhance the adhesion, the top resin layer of the CFRP and the surface of the aluminum alloy were patterned by femtosecond laser. Polycarbonate (PC) was used as a filler material during the joining processes. ANSYS simulation was applied to elucidate the thermal kinetics of the self-propagation reaction and the thermal profile, and evaluate the possibility of joining CFRP to aluminum using Ni/Al RMFs. The SEM image of the cross-section shows that melted PC flowed into the CFRP-aluminum alloy interface, suggesting strong mechanical bonding. A tensile strength of 9.5 MPa was reached using Ni/Al multilayers as heat sources, which provides a new way for joining CFRPs and aluminum alloys in space or under water.  
laser joining; CFRP to aluminum alloy; reactive multilayer films +
Footnote †: journal: Applied Sciences
## 3 Effect of Joint Geometry on Peel Stress
### Effect of Fiber Angle on the Peel Stress  
This section keeps all previous joint geometry specifications constant, and the only variant is fiber angle. The angle of fibers in adherends 1 and 2 was changed, and its effect on the peel stress was investigated as listed in Table 2. As shown, peel stress amplitude was increased with an enhancement in the fibers' angle. Overall, the joint strength will decrease at a specific loading by increasing the fiber angle. The percentage of increase in the peel stress at each step ascends approximately linearly. The variation of peel stress relative to a joint with zero fibers angle is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Arrangement of Adhered No. 1** & **Maximum Peel Stress Magnitude (KPa)** \\ \hline \([0_{12}]\) & 73.566 \\ \([\pm 15_{6}]\) & 111.11 \\ \([\pm 30_{6}]\) & 425.03 \\ \([\pm 45_{6}]\) & \(5.9467\times 10^{3}\) \\ \([\pm 60_{6}]\) & \(5.755\times 10^{5}\) \\ \([\pm 75_{6}]\) & \(2.8548\times 10^{8}\) \\ \([\pm 90_{6}]\) & \(8.4404\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variation in maximum peel stress due to changes in fiber angle.  
Figure 7: Dimensionless maximum peel stress comparison for three solution methods.  
As expected, the adherend with zero angle fibers has the most strength since the fibers are aligned with the applied tensile force. The strength of the joint is reduced by increment in fibers' angle, and obviously, the adherend with [\(\pm\)90\({}_{6}\)] arrangement is the weakest because the fibers are perpendicular to the force.
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 1 Introduction
The study aimed to investigate whether two of the most popular open-source artificial intelligence models (OSAIMs), ChatGPT 4o (omni) and Gemini Pro, could analyze a video file containing a single-phase contrast-enhanced MRI image in sagittal and frontal projections, depicting a posterior fossa tumor corresponding to medulloblastoma in a child. While most existing research focuses on AI models for static image analysis in MRI,particularly in tumor detection [2; 9; 14], this study explores the application of these models in dynamic video-based MRI analysis, addressing an area that remains underexplored. Unlike previous studies that focus on AI's role in static image interpretation or diagnostic tasks in neuro-oncology [14], this research emphasizes the challenges and opportunities in applying AI models to sequential video data. This shift to video-based analysis introduces additional complexities such as motion tracking and identifying temporal patterns, areas where AI applications are still evolving. Furthermore, by evaluating the performance of ChatGPT 4o and Gemini Pro across different MRI planes (sagittal and frontal), this study provides a comparative framework that highlights the need for more specialized AI training for distinct imaging modalities. While previous work has shown promising results in static radiology, this research pushes the boundaries by assessing AI's ability to handle dynamic MRI sequences, contributing new insights into AI's role in complex diagnostic tasks in pediatric neuro-oncology. To this end, we separately implemented the video in sagittal and frontal sequences using three different sets of questions (prompts)--from the most general to the most specific. The hypotheses we posed are  
**Hypothesis 1**. : _Both models could recognize the content of the video._  
**Hypothesis 2**. : _Both models correctly identified the area of the neoplastic change._
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 4 Discussion
### Protocols Designed for Hierarchical Networks
#### 4.3.1 Protocols that Use a Global Time Reference  
Protocols in this subgroup [13; 17; 18] are based on the same idea that is used for protocols with a global clock designed for a plain network. They use a combination of logical and physical clocks to ensure causal message order.
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
## 4 Simulation and Discussions
### PFC Mode  
The purpose of this part is to double check the performance of the proposed dead-beat control cooperating with observer and \(\delta\) control. Parameters are the same as Table 1 and the flowchart is also the same as Figure 5. The difference between the PFC mode and pure reactive power compensation mode is the codes for \(\delta\) calculation, which is explained in Reference [2] in detail. The simulation is divided into two time intervals. In the first time interval which is from 0 to 0.2 s, \(V_{G}\) is set to 108 V. In the second time interval from 0.2 s to 0.4 s, \(V_{G}\) is set to 112 V. The simulation results are shown in Figure 8, where four channels are recorded. Line voltage and line current are recorded together in the first channel to show the effectiveness of PFC.  
In Figure 8, it is obviously seen that zero crossing points in the first channel are the same and also in the same direction, which means that line current is controlled in phase with that of line voltage, even if \(V_{G}\) changes. CL voltage in the second channel is controlled at 110 V. ES voltage and NCL voltage are recorded in the third and fourth channels, respectively. It is noticed that the phase angle between ES voltage and ES current are not strictly 90\({}^{\circ}\). For instance, at 0.105 s when \(V_{G}\) is 108 V, ES current which is in phase with NCL voltage, leads ES voltage for more than 90\({}^{\circ}\), meaning that ES provides some active power. At 0.365 s when \(V_{G}\) is 112 V, ES current leads ES voltage by less than 90\({}^{\circ}\), meaning that ES absorb some active power. The reason is that only two control objectives can be achieved at the same time, of which one is the CL voltage and another one is the PFC function.  
The effectiveness of the proposed dead-beat and \(\delta\) control has been validated by the simulations results above.