esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D)
### Phases of AZTRM-D
#### 4.1.4 Testing Phase  
The Test Phase acts as a crucial security checkpoint where the compiled software undergoes thorough validation. AZTRM-D includes automated security testing, such as AI-driven Dynamic Application Security Testing (DAST), fuzz testing, and runtime validation [5]. The AI Analytics and Orchestration Engine takes DAST/fuzz test results, CSPM and IaC scan findings, and simulated attack scenarios as inputs. DAST tools test the running application for vulnerabilities that might not be obvious in the source code. AI-powered fuzzing engines create random inputs to test how resilient the application is against unexpected conditions, often leading to intelligent test case generation.  
Workload security is validated using Cloud Security Posture Management (CSPM) and Infrastructure as Code (IaC) scanning tools. These tools, improved by AI, analyze deployment configurations for things like excessive permissions, exposed secrets, or misconfigurations that could create vulnerabilities [46]. The AI outputs risk-prioritized vulnerability reports, automated remediation proposals, and even automated exploit generation. If a security problem is found, for example, a container set up to run with root privileges, automated policies will block the deployment and force a fix. This phase makes sure both the application and its planned operational environment are secure before release.
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 3 Methods
### HVSR and VSR Calculation Method  
This study analyzes horizontal-to-vertical spectral ratio curves of the Fourier amplitude spectrum of the acceleration time history (HVSR) and horizontal-to-vertical spectral ratio curves of the velocity response spectrum (VRSR) using the processed strong-motion records. To facilitate comparison, both HVSR and VRSR are presented in terms of period.
## 2 Materials and Methods
### Modeling
#### 2.3.3 Model Optimization  
The three models with the best performance were selected for a hyperparameter optimization process. The optimal combination of these values allows for maximizing the model's performance and reducing the error percentage. Three optimization techniques were used: Bayesian optimization, genetic algorithms, and particle swarm optimization. Bayesian Optimization (BO):  
In this case, the hyperparameter tuning can be viewed as the optimization of an unknown black-box function. Bayesian optimization works by assuming that the unknown function is sampled from a Gaussian process and maintains a posterior distribution for this function as observations are made, or in this case, as the results of running experiments of learning algorithms with different hyperparameters are observed. To choose the hyperparameters for the next experiment, one can optimize the expected improvement over the current best result or the upper confidence bound of the Gaussian process. This has been shown to be efficient in the number of experiments needed to find the global minimum of the black-box function [46]. This process was implemented using the BayesSearchCV function from the skopt (Scikit-Optimize) package. Genetic Algorithms (GA):  
A genetic algorithm is a heuristic, biology-inspired approach based on the evolutionary process that represents an optimization procedure in a binary search space. It runs multiple times through an evolutionary cycle, which involves the selection of individuals, crossover to create offspring, and occasional mutation. After each cycle, the fitness function of each individual is calculated, and the best ones are selected to survive and reproduce. The process of generating new populations continues until a termination check is met, with the condition that each rule in the population satisfies a predefined fitness threshold [47]. The GASearch library was used for hyperparameter optimization in this case. Particle Swarm Optimization (PSO):  
Inspired by the group behavior of bird flocks and fish schools, this method is based on collective intelligence. The algorithm consists of a group of particles moving through the search space, adjusting their positions based on their own experience and that of other particles. The goal is to find a solution that minimizes a given objective function. This is achieved through iterations, improving the position until no further improvement can be found or a maximum number of iterations is reached. PSO has been shown to have wide optimization capabilities due to its simple concept, easy implementation, scalability, robustness, and rapid convergence [48]. The pyswarm library was used for this purpose.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Point-Line Feature Extraction Network
#### 3.1.3 Line-Segment Detection Module  
The line-segment detection module extracted the features of the plane image through the ORB-FPN module and then input it to the line-segment extraction module to generate a midpoint with two symmetrical endpoints as the line-segment detection result. The extraction of the line segment's center point [28] used the classification model to judge whether the pixel was the center point of the line segment. Since the shape of the line segment was narrow and long, a large receptive field was required to classify the center of the line segment. Therefore, a hybrid convolution module was introduced by stacking three convolutional layers, a 3 \(\times\) 3 deformable convolutional layer and two 3 \(\times\) 3 dilated convolutional layers. The receptive field of the network was increased while reducing the parameters of the network. Then, three line-segment perception modules were used to enhance the feature representation ability. Finally, a deconvolution layer was used to restore the size of the output map to 512 \(\times\) 512, which represented the center point of the line segment on the output feature map. The displacement regression task of the line-segment extraction branch was designed to predict the angle and length of the end point relative to the midpoint. It was composed of a 3 \(\times\) 3 deformable convolution layer and two 3 \(\times\) 3 convolutional layers with a stride of 1. The relevant displacement was indexed by the position through the output map. Finally, the CAL [29] was used for the line-segment generation, and the two endpoints of the line segment were defined as follows:  
\[(x_{l_{x}},y_{l_{x}})=(x_{l_{x}},y_{l_{x}})+\frac{\alpha}{2}(\cos\theta,\sin\theta) \tag{13}\]  
\[(x_{l_{x}},y_{l_{x}})=(x_{l_{x}},y_{l_{x}})-\frac{\alpha}{2}(\cos\theta,\sin\theta) \tag{14}\]  
where \((x_{l_{x}},y_{l_{x}})\) is the coordinates of the root node, \(\alpha\) is the length of the line segment, and \(\theta\) is the rotation angle.  
In the line-segment detection, a line-aware module was introduced to effectively extract line shape features. As shown in Figure 5, the line-segment-aware module adopted the improved self-attention mechanism, which took the feature \(x^{L}\in\mathbb{R}^{W\times H\times D}\) as input and fused the self-attention features to generate the final output feature \(y^{L}\in\mathbb{R}^{W\times H\times D}\); the above process was defined as follows:  
\[y^{L}=x^{L}\oplus W_{1}(\alpha(W_{u}x^{L}\times W_{k}x^{L})\times W_{v}x^{L}) \tag{15}\]  
where \(x^{L}\) is the input feature, \(W_{1}\), \(W_{q}\), \(W_{k}\), and \(W_{v}\) are the learned weight matrices, which were implemented as \(1\times 1\) convolutions, and \(\oplus\) means that the corresponding elements of the two matrices are added.  
Figure 5: The line-aware module.
## 3 Methodology
### Optimization Timeout for RALAR Using Genetic Algorithm (GA)  
The performance of the conventional LAR protocol in a network corresponds to the route request timeout, which is the time that the node should wait before initiating a new route request [35]. This means that if a route request does not get a response with a route reply message within the timeout, the route request packet will be canceled and reinitiate the new route discovery process [36]. This is because the nodes in a conventional LAR protocol utilize the location information of the destination node before selecting the route location, which is most likely to be expired because of the fast movement of the nodes in the VANET environment [37]. The default value timeout was usually set up to a  
Figure 2: Flowchart for RALAR Algorithm.  
maximum of 2 s. However, this timeout value may not satisfy all the network cases because each scenario has its own application and different network situation. Therefore, there is a definite need for a method that can determine the appropriate value of this timeout [38]. In this regard, the proposed RALAR protocol utilized the GA optimization approach to select the most suitable timeout variable. The GA was used where the chromosome is a scalar quantity representing route request timeout [39]. The GA aims to minimize the following function  
\[Fit_{\text{real}}=Average\ E2E\ Delay+Overhead+(0.5\times Average\ Energy\ Consumption)-(100\times PDR) \tag{4}\]  
The basic idea of utilizing the GA is to discover the best value of the timeout value. In order to do this, random numbers of time values are generated by a new generation. Then, each of these values is tested in the network, and the corresponding performance is measured to determine the value of the objective function. Next, a new generation of values is obtained based on the elites or the best value in the previous iteration by using two operations, i.e., the crossover and the mutation. The GA works for the pre-determined number of iterations (number of generations) to find the best timeout value. Figure 3 shows the process of timeout optimization.
## 1 Introduction
The hyperfine splitting intervals between the hyperfine folds of (\(F^{\prime\prime}=6\)), (\(F^{\prime\prime}=5\)), and (\(F^{\prime\prime}=4\)) of the Cs 5\(p^{6}\)7\(d\)\({}^{2}\)\(D_{5/2}\) state (HFS6\({}^{\prime\prime}\)-5\({}^{\prime\prime}=-10.60(17)\) MHz and HFS5\({}^{\prime\prime}\)-4\({}^{\prime\prime}=-8.54(15)\) MHz) were measured and, therefore, the magnetic dipole hyperfine interaction constant (\(\mathbf{A}=-1.70(03)\) MHz) and the electrical quadrupole hyperfine interaction constant (\(\mathbf{B}=-0.77(58)\) MHz) for the Cs 5\(p^{6}\)7\(d\)\({}^{2}\)\(D_{5/2}\) state were derived. This paper is organized into five sections. In Section 2 we introduce relevant principles, including the relation between the hyperfine splitting and the momentum quantum number, the relation between the hyperfine splitting and the hyperfine interaction constants, and the spectroscopic schemes we chose in this work. In Section 3 we describe the experimental setup and conditions under which we performed the measurements. In Section 4 we present the optimization of experimental parameters in order to achieve a higher SNR and better resolution of the EIT-assisted DROP spectra. In Section 5 we more precisely measure the hyperfine splitting intervals and the hyperfine interaction constants of the Cs 5\(p^{6}\)7\(d\)\({}^{2}\)\(D_{5/2}\) state by analyzing the experimental data and discussing the related techniques. Finally, we outline the main points in Section 6.
# Markov-Based Malware Propagation Modeling and Analysis in Multi-Layer Networks
## 4 Epidemics Spreading in Multi-Layer Complex Networks via Markov Random Fields
### System Setup  
We consider two-layer complex topologies, as shown in Figure 1; also, we consider that each layer may be of different type, i.e., random, small-world, etc. To examine each aspect of the day-to-day networks represented by these combinations, we set the number of nodes in the first network equal to 100, 250, and 500 nodes. Meanwhile, for the corresponding topology that is connected, we defined the nodes equal to 100, 250, 500, 750 and 1000. That is, keeping the number of nodes of the first topology constant, we modified the number of nodes of the second. To cover a wide range, 25 scenarios of each correspondence between the node numbers were repeated, and finally, the average of the infected nodes of our final network was obtained.  
To better account for the interaction of the two topologies and the resulting infected nodes, we created vertical links between these two topologies. These vertical connections must also increase, as our network grows, in order to be able to properly and effectively relate the connection and interaction of the two network topologies. Therefore, we set them to equal to the number of nodes in the first topology, i.e., 100, 250 and 500.  
In order to determine the vertical connections between the two considered topologies, we select a pair of nodes, i.e., one node from the first topology and one from the second at random. This will also be a link between the topologies. We repeat the same procedure for another random pair of nodes, after first making the appropriate check for whether this pair has been used before. This way, we avoid \"losing\" some vertical connections by randomly selecting the same node pairs.
## 2 Hybrid Flow Shop Scheduling Problem
### Mathematical Model  
Using the above notation, the mathematical relationship of HFSSP is formulated as follows:  
\[\min(C_{\max})\ =\ \max_{j\in J}\left(D_{js}+p_{js}\right) \tag{1}\]  
\[\sum_{i\ =\ 1}^{m_{k}}x_{jik}\ =\ 1,\forall j\in J,k\in S \tag{2}\]  
\[D_{1j}\geq 0,\forall j\in J \tag{3}\]  
\[D_{k+1,j}-D_{kj}\geq p_{kj},\forall j\in J,k,k+1\in S \tag{4}\]  
\[y_{jj^{\prime}k}+y_{j^{\prime}jk}\leq 1,\forall j,j^{\prime}\in J,k\in S \tag{5}\]  
\[D_{k^{\prime}}-\left(D_{kj}+p_{kj}\right)+BM\cdot\left(3-y_{j^{\prime}k}-x_{ kji}-x_{kj^{\prime}i}\right)\geq 0,\forall j,j^{\prime}\in J,k\in S,i\in \{1,2,\cdots,M_{k}\} \tag{6}\]  
\[x_{jik}\in\{0,1\},j\in J,k\in S,i\in\{1,\cdots,M_{k}\} \tag{7}\]  
\[y_{jj^{\prime}k}\in\{0,1\},j,j^{\prime}\in J,k\in S \tag{8}\]  
The objective function (1) is to minimize the maximum completion time. Constraints (2) ensures that each job completes all the processing stages and can only be assigned to one machine in each stage. Equation (3) ensures that the first stage start time of each job is greater than or equal to 0. Equation (4) indicates that when the job is processed in two consecutive stages, the next process can be started after the previous process is completed. Equations (5) and (6) describe the sequential constraints of different jobs in the same machine. When two jobs are processed, the previous job must be finished before the following job can be started. Equations (7) and (8) define the value ranges of decision variables.
## 2 Background
### Different Types of Sensors
They detect and quantify movement or vibrations in objects along different axes. Accelerometers are used in various applications, such as the aircraft and aviation industries, for stability control. * Gyroscope sensors: These sensors measure the rotation or angular velocity rate around one or more axes. They provide information about an object's orientation in space and can detect changes in direction. Gyroscopes are often used in navigation systems, where they help determine the vehicle's orientation and movement accurately.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 3 Results and Discussion
### Experimental Setup
#### 3.1.4 Performance Metrics and Federated Learning System Setup  
Given that this work primarily centers around countering data poisoning attacks and model poisoning attacks, both of which intend to undermine the global classification accuracy of the model, we have chosen the model's accuracy as our evaluation metric. A higher accuracy for the global model signifies the effectiveness of the corresponding defense method. By default, in the case of MNIST, we consider a federated learning (FL) system comprising 100 clients. For CIFAR-10, the FL system consists of 40 clients. Unless specifically stated, we assume that 20% of the clients in each dataset are malicious.
## 5 DT Technology in the Lifecycle of Transportation Infrastructure
### Maintenance and Upgrade Phase
These approaches ensure continuous monitoring of structural integrity and load-bearing capacity, enabling infrastructure managers to proactively respond to signs of fatigue, corrosion, or other defects. In maintaining roads, bridges, and tunnels, the analysis and evaluation of 3D models of transportation infrastructure facilitate the timely identification of road damages and defects, thereby enhancing maintenance efficiency and quality. DT systems capture and deliver real-time, precise data such as traffic patterns, critical driving behaviors, geographic data, and variations in road wear or friction [95]. Utilizing GIS technology, geometric models of transportation infrastructure employ various data fusion algorithms to produce simulations that mimic the physical and operational characteristics of these structures [96]. Ref. [3] performed an extensive analysis incorporating environmental conditions, employing BIM, finite element modeling, and statistical approaches to forecast bridge strain. Ref. [97] proposed a \"BIM-based bridge risk inspection model\" aimed at minimizing inspection risks by thoroughly evaluating internal, natural, and human-related risks, providing a comprehensive prediction of bridge risks. By leveraging real-time data and the simulation capabilities of DTs, managers can assess the lifespan of infrastructure, calculate the remaining service life of structures, and design life extension strategies tailored to various operational and environmental conditions. This proactive, data-driven method of assessing conditions and predicting lifespan enables transportation officials to implement preventive measures before infrastructure deteriorates, significantly reducing the safety hazards associated with aging infrastructure. Evaluating and diagnosing facilities involves simulation diagnostic algorithms, performance metrics, anomaly detection techniques, and data model evaluations that support the structural service state diagnosis of urban transportation infrastructure. For instance, using metrics like wind speed, direction, vehicle loads on bridges and roads, and settlement figures for tunnels, roads, and hubs, along with data on key bridge deformations and vibrations, can establish critical performance model diagnostic indicators. These indicators generate risk levels, early warnings, and deformation forecasts for transportation infrastructure. Facility evaluation and diagnostics incorporate diverse metrics and algorithms for an exhaustive performance evaluation and anomaly diagnosis. Based on the characteristics of the chosen indicators, suitable visual representations, such as tables, bar charts, scatter plots, heat maps, line charts, and pie charts, are used to display the results. These analyses clarify various issues related to model analysis, including data quality, congestion, traffic bottlenecks, flow and travel time, signal strategy, and model errors, performance, and stability.
## 2 Materials and Methods
### Quantitative Real-Time PCR for MicroRNAs Expression  
Retro-transcription was carried out according to the Applied Biosystem's High Capacity cDNA Reverse Transcription kit, (#4368814, Applied Biosystem, Life Technologies, Monza-Italy). Quantitative real-time PCR was performed using the TaqMan probes and the specific TaqMan Universal Master Mix II, no UNG, in 96-well plates (#4440040, Applied Biosystem, Life Technologies, Monza-Italy) with an Applied Biosystem PRISM 7900 HT Sequence Detection System, in triplicate. MiR-16 was used as the endogenous control. The specific miRNA sequence probes used (Applied Biosystem) were:  
- has-miR-133a (UUUGUGUCCCCUUCAACCAGCUG; #002246);  
- has-miR-133b (UUUGUCCCCUUCAACCAGCUA; #002247);  
Figure 1: Design of the study.  
- hsa-miR-135a-5p (UAUGGCUUUUUUAUUCCUAUGUA; #000460);
- hsa-miR-206 (UGGAAUUGAAAGGAAUGUGUGUG; #000510);
- hsa-miR-29b (CUGGUUUCACAUGUGGCUUAGC; # 002166);
- hsa-miR-let-7b (UGAGGUA
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 1 Introduction  
The fifth-generation (5G) of wireless networks aims to enhance the energy efficiency, end-to-end latency and capacity of networks. The radio access network has become more centralized as the number of base stations (BS) has increased exponentially [1]. Because it enhances scalability and decreases network maintenance, a centralized radio access network (C-RAN) reduces capital costs. A fronthaul (FH) connects baseband units (BBU) and distant radio heads in order to support a C-RAN. As shown in Figure 1, optical fronthaul (OFH) links, such as radio over fiber (RoF) links, constitute the mainstay in OFH. Due to its cost-effective and easy paradigm, which improves the network's range, this transport approach is a significant option for high-capacity wireless transmission for transporting analogue or digital signals in OFH.
## 3 Results
### Testing the Model Forecasts
The red triangles demonstrate the obtained quantile scores from the ETAS model and the black squares illustrate the quantile scores of OU law. The blue dashed lines represent the 0.025 th and 0.05 th quantiles. Figure 10.0 The obtained quantile scores from the R-test to compare the forecast based on the OU law versus the ETAS model for the three sequences: **(a)** the 3 November 2002, Mw 7.9 Denali for \(m\geq 3.0\); **(b)** the 23 January 2018, Mw 7.9 Kodiak for \(m\geq 3.2\); **(c)** the 30 November 2018, Mw 7.1 Anchorage for \(m\geq 2.8\). The black squares illustrate the quantile scores in the case with the constant forecasting time interval of \(\Delta T=7\) days. The red triangles show the obtained quantile scores from the second case with the constant target time interval of \([T_{s}\), \(T_{e}]=[0.06\), \(2]\) days. The blue dashed lines represent the 0.025 th and 0.05 th quantiles. Figure 10.1 The sample information gain of the ETAS model versus the OU law over the number of observed data for the three sequences: (**a**) the 3 November 2002, Mw 7.9 Denali for \(m\geq 3.0\); (**b**) the 23 January 2018, Mw 7.9 Kodiak for \(m\geq 3.2\); (**c**) the 30 November 2018, Mw 7.1 Anchorage for \(m\geq 2.8\). The black solid squares illustrate the sample information gain for the first case with the fixed the forecasting time interval of \(\Delta T=7\) days. The red triangles demonstrate the sample information gain for the second case for the constant target time interval \([T_{s},\,T_{e}]=[0.06,\,2]\) days. Figure 10.2 For both cases the Bayesian \(p\)-value of the BPD from the ETAS model and the OU law are illustrated for the three sequences: (**a**) the 3 November 2002, Mw 7.9 Denali for \(m\geq 3.0\); (**b**) the 23 January 2018, Mw 7.9 Kodiak for \(m\geq 3.2\); (**c**) the 30 November 2018, Mw 7.1 Anchorage for \(m\geq 2.8\). The green squares and the red triangles illustrate the obtained \(p\)-value from the first case, with the fixed forecasting time interval \(\Delta T=7\) days. The yellow diamonds and purple circles demonstrate the \(p\)-value for the second case with the constant target time interval of \([T_{s},\,T_{c}]=[0.06,\,2]\) days. The blue dashed lines represent the 0.025 th and 0.05 th quantiles.