esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of Reliability Cost
### Reliability Cost  
We propose the use of backbone routing for WSN to improve the overall reliability of the network. The backbone is formed at the cost of additional gateway nodes. Since each additional component in the system is generally treated as an overhead in resource-constrained WSN, we define the _cost_ of a system as:  
_Definition 6: Cost_ of a system is the total number of components in the system.  
A network is said to be _connected_ if every pair of nodes is connected through at least one path. Our system model assumes that the network is connected through a subsystem of \(t\) -1 components. We further assume that subsystem contains a finite number of components and hence its reliability is well above 0.  
A packet sent by node \(S\) must traverse at least all \(t\) -1 components of a subsystem before being delivered to node \(D\). Therefore, the reliability of a subsystem \(r_{s}(p)\) is given by  
\[r_{s}(p,t)=p_{f}{}^{t-1}. \tag{7}\]  
To study the reliability of a system, we assume that \(m\) subsystems exist between a \(<\)source, destination\(>\) pair. Since the subsystems are independent of each other, the system is functional (\(r(p)=P\{\phi(X)=1\}\) ) if all the components of a subsystem are functional; _i.e._\(r(p)=P\{X_{i}=1\ for\ some\ i=1,\ \ldots,m\}\), where \(X_{i}\) is the indicator variable of a subsystem. Mathematically, \(r(p)\) is equivalent to \(1-(P\{X_{i}=1\ for\ all\ i=1,\ \ldots,m\})\). Hence, using (6) and (7), the reliability of the system \(r_{p}(m,p_{f},t)\) is  
\[r_{p}(m,p_{f},t)=1-(1-p_{f}^{t})^{m}. \tag{8}\]  
The cost of the system is therefore given by  
\[C(m,t)=m(t-1). \tag{9}\]We plot (8) against different values of \(m\) to observe the increase in overall system reliability along with its associated cost (9). The results of functions (\(r_{p}\)(\(m\), \(p\)\({}_{f}\),\(t\)))and (\(C\)(\(m\), \(t\)) ) for varying values of \(p\)\({}_{f}\), \(t\) and \(m\) are plotted in Figure 10. Note that the reliability of the system increases exponentially and then saturates after a certain number of additional subsystems. Thus the reliability of a system does not improve if we add more subsystems. While reliability benefits are negligible, cost (see Figure 11(b)) of the system keeps increasing linearly with the addition of more subsystems. This would be more obvious if the components being added are resource rich and expensive like gateway nodes.
# Peptides That Block RAS-p21 Protein-Induced Cell Transformation
## 3 Anti-RAS-p21 Peptides from Expression Libraries
### Cyclic Peptides Freeze Oncogenic G12D-RAS-p21 in an Inactive GTP-Bound State
In particular, the substitution of an unnatural amino acid with a 4-methyl piperidine ring (called Aza X) for Thr 11 was found to have about a fifteen-fold decrease in its IC\({}_{50}\) compared with that for KD2, although these cyclic peptides were found to block wild-type RAS-p21 at higher concentrations (100 Um range). In addition, bicyclic forms of the cyclic peptide, such as the disulfide bicycle in which two SH groups at positions 3 and 9 form a disulfide bridge, have been synthesized and found to block the binding of G12D-RAS-p21-GppNHp to the RAF RBD with a ten-fold reduction (for the disulfide bicycle) in the IC\({}_{50}\)[29]. KD2 and other cyclic peptides that specifically bind to the GTP-bound state of oncogenic G12D-RAS-p21 have thus far not been found to inhibit cancer cell growth; this is most likely due to the fact that they do not readily penetrate the cell membranes of these cells [29]. At present, efforts to modify these cyclic peptides with agents that can induce cell membrane penetration are being conducted [29]. It should be noted that cell-penetrating forms of KD2 and related cyclic peptides would be expected to be effective in treating cancer cells that contain only the G12D mutant form of RAS-p21 and may not be effective  
Figure 4: [PERSON] et al. Structure of the cyclic peptide, KD2, that blocks the activation of the G12D-RAS-p21-GTP complex. The Thr 10 residue whose side chain -OH group makes contact with the Asp 12 residue is in the top middle of the figure and can be identified by the -OH in this area. against RAS-p21 forms that contain other amino acid substitutions at position 12 or at other positions such as at Gly 13 and Gln 61.
## 3 Algorithm
### Gaussian Fit  
Further investigations qualify the pixel wise Gaussian fit as a tremendous improvement. The results presented in Section 4 could be achieved by the following algorithm: all pixels \((x,y)\) of the shape-from-focus stack are used as sampling points for a pixel wise Gaussian fit with four degrees of freedom, as follows:  
\[f\Big{(}x\Big{|}a,\mu,\sigma^{2},c\Big{)}\ =\ a\cdot\frac{1}{\sqrt{2\pi\sigma^{2} }}e^{(-\frac{(x-y)^{2}}{2\sigma^{2}})}+c \tag{7}\]  
The fit is initialized using the distance of the maximum phase smoothness as a first estimate for the peak position \(\mu\), and c being the ground noise of the signal. The scaling parameter \(a\) is initialized as the peak value of the maximum phase smoothness, along with the experimentally determined \(\sigma^{2}\) of 0.5, complete our initial parameters for up to 1000 iterations. The expected value \(\mu_{opt}\) of each Gaussian fit is used for the final focus map \(z_{opt,Gauss}\).  
\[z_{opt,Gauss}\ =\ \mu_{opt} \tag{8}\]  
The values with a maximum phase smoothness below 0.3 are marked in a separate mask. The corresponding height \(h\)  
\[h\ =\ -z_{opt} \tag{9}\]  
is used as a rough synthetic wavelength and serves as the starting information for the combination of further synthetic wavelengths.  
In order to being able to use this method for inline-measurements, computation-intensive calculations are processed on the GPU, since pixel wise operations can be efficiently evaluated in parallel. Having transferred the complex holograms to the GPU, the phase noise, filtering, and Gaussian fitting are computed on the GPU.
# Structure-Aware Scheduling Methods for Scientific Workflows in Cloud
## 1 Introduction
In this example, regardless of the number of VMs assigned to execute the entry-level tasks (first level), at most three VMs can be used to execute the second-level tasks. Additionally, it is evidence that the subtree rooted at task 14 is more computationally intensive compared to the subtree rooted at task 13. Accordingly, more VMs should be assigned to execute the task 14 subtree compared to the task 13 subtree. Overall, from a scheduling perspective, the structure information captures the important aspects that a solution must consider to obtain an efficient solution. In this paper, by utilizing the structure-related information, we propose a structure-aware fair-share scheduling approach that aims to achieve a balanced solution by minimizing cost and execution time. This paper extends the authors' previous work [17] by proposing a complementary structure-aware scheduling approach. The contributions of this paper can be summarized as follows:  
* We refine the level-based approach (LBA) introduced in Ref. [17] by adding further analysis and discussion. The LBA treats the available VMs as isolated agents that will benefit from executing tasks. Accordingly, tasks are assigned to VMs based on a utility function value, in which such an assignment aims to maximize the overall obtained utility value;  
Figure 1: Examples of scientific workflows [16]. * We propose the hierarchy-based approach (HBA) that uses a look-ahead mechanism in which the task scheduling considers the entire workflow structure. In HBA, tasks are divided into groups in a top-down manner that aims to reduce dependency across groups. Thus, tasks with data dependency constraints are expected to belong to the same group. Once the groups are identified, the VMs are divided between the groups using a fair-share mechanism. Such a mechanism uses the information available about the group tasks to determine the VMs share of each group. Similar to level-based scheduling, tasks are mapped to VMs, using a utility-based mechanism to optimize the available VMs usage;
* We have conducted several sets of experiments to evaluate the performance of the presented approach. Compared to the benchmark algorithms, the results demonstrated that the LBA and HBA algorithms improved the execution time and cost on average 25% and 30%, respectively. Additionally, the results have shown that the performance of the HBA is impacted by the number of available VMs. The performance of this approach is improved when the number of available VMs is sufficient to utilize the advantages of the fair-share division strategy. The rest of this paper is organized as follows. In Section 2, we discuss the most related literature. The system model and the problem formulation are presented in Section 3. Section 4 presents and discusses the level- and hierarchy-based scheduling approaches. Section 5 presents the settings and results of the experiments. The paper is concluded in Section 6.
## 1 Introduction
### Objectives and Contribution  
The aim of the study is to develop a resilient image classifier, which would function under influence of destructive perturbations and resource constraints.  
By resilient we mean a classifier, which implements the basic principles of resilience (robustness, graceful degradation, recovery, and improvement) to the selected type and level of perturbations. The less the performance of the classifier decreases under the influence of perturbation and the faster it recovers and improves, the more resilient the classifier is considered to be. Where the permissible level of performance declines and the allowable number of iterations to restore performance is known, the classifier can be considered resilient if its response to a given perturbation does not exceed the specified limits.  
The key objectives are as follows:  
* To develop a new resource-efficient model and a training method, which simultaneously implement components of resilience such as robustness, graceful degradation, recovery, and improvement;
* To test the model's and training method's ability to provide robustness, graceful degradation, recovery, and improvement.  
Structurally, the work consists of the following sections. The related works are analyzed in the Section 2. The Section 3 presents a new classifier model used to provide resilience features. The Section 4 describes a new training method used to provide resilience features. The Section 5 describes the experimental results of testing of the proposed model and training method of the classifier. The research results are discussed in the Section 6. The Section 7 concludes the paper and describes the directions of future research.  
The main contribution of the research includes a set of proposed principles, a classifier model, and a training algorithm that provides resilience capabilities to fault injections, concept drifts, and adversarial attacks. In addition, the behavior of the proposed classifier is investigated and compared with conventional approaches under the influence of the considered perturbations. The multi-section model has a hierarchical structure of class prototypes and hyperspherical class boundaries, which are optimized during training and provide perturbation absorption and graceful degradation capabilities. The novelty of the machine learning method lies in the application of a complex loss function assembled from its constituent parts in a particular way depending on the result of perturbation detection and the presence of new labeled and unlabeled data. In addition, the applied regularization provides an information bottleneck and compact distribution of classes, as well as the maximum possible interclass gaps.
# EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis
## 2 Related Work
### Applications of Ensemble Learning Models  
[PERSON] and [PERSON] [36] proposed a multi-output hybrid ensemble learning model to predict student performance using data from the Superstar Learning Communication Platform (SLCP). Similarly, [PERSON] et al. [37] introduced a bagging (bootstrap aggregation) ensemble learning model applied to one-dimensional convolutional neural networks (1D CNN), one-dimensional residual networks (1D ResNet), and a hybrid ensemble deep learning model to predict engagement in online learning. [PERSON] et al. [38] developed a novel parallel ensemble model based on hybrid machine learning and deep learning techniques for accurate fault detection and diagnosis tasks. These studies have demonstrated the effectiveness of ensemble learning methods in predictive tasks and fault detection tasks, highlighting their ability to improve accuracy and robustness in complex scenarios. In the domain of sentiment analysis, [PERSON] and [PERSON] [39] proposed SETAR, a stacking ensemble learning-based polarity identification method for Thai sentiment classification, which outperformed baseline models across all classification metrics on both training and test datasets.  
Recent studies have further expanded the applications of ensemble learning, emphasizing its potential to enhance prediction accuracy and decision-making speed. For instance, [PERSON] et al. [40] presented an innovative ensemble-based approach using prominent machine learning techniques to develop a vehicle engine prediction framework, achieving faster and higher decision accuracy. For short-term power load forecasting, [PERSON] et al. [41] proposed a multi-scale ensemble method and a multi-scale ensemble neural network, with simulation results demonstrating superior comprehensive prediction performance.  
Ensemble learning, as a robust machine learning paradigm, has transcended the boundaries of individual domains and found applications in diverse fields. By combining predictions from multiple learning algorithms, ensemble methods enhance prediction performance beyond the capabilities of single models. This collaborative approach has proven effective in tasks such as sentiment classification, student performance prediction, fault diagnosis, and power load forecasting. In the financial domain, ensemble learning has also been applied to stock prediction. For instance, one study [42] proposed a two-stage portfolio optimization method based on ensemble learning and the maximum Sharperatio portfolio theory, incorporating asset prediction information to improve portfolio performance and robustness. Another study [43] introduced an ensemble recurrent neural network (RNN) method, integrating long short-term memory (LSTM), gated recurrent units (GRUs), and SimpleRNN, to predict stock market trends. These examples underscore the versatility and effectiveness of ensemble learning in addressing complex prediction challenges across various domains.
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 3 Methodology
### Avionics and Controls Selection  
Because the aircraft utilizes multiple flight modes for VTOL and forward flight, a dependable controller is required with appropriate features. During VTOL operation, lift is entirely generated actively by the propulsors; however, in forward flight, lift is generated passively by the wings. A controller is necessary to also manage the transition between VTOL and forward flight. The Pixhawk 2.1 Orange Cube flight controller was selected for this study, which supports the Ardupilot operating system with various programmable options. Table 1 includes a summary of the sensors utilized, avionics, and servo motors. There are dozens of key parameters that can be altered in the flight controller. Future work could look at optimal transition control through further refinement of the extensive parameters list and seek to improve flight safety associated with unique turboelectric power system configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Description** & **Specification** \\ \hline Flight Controller & Pixhawk 2.1 Orange Cube &
\begin{tabular}{c} 14 output pins, GPS, accelerometer, and compass. \\ (Data log rate of 25 Hz) Accelerometer 0 g to 30 g, \\ error \(\pm 0.001\) g \\ \end{tabular} \\ Secondary GPS & Here2 GNSS GPS Module & Extra compass, GPS, accelerometer \\ Radio Remote/Transmitter & Futaba 14 SG & 14 Channel 2.4 GHz Transmitter \\ Radio Receiver & Futaba S.Bus Rec HV 2.4 GHz & S-Bus 2.4 GHz Receiver \\ Telemetry Radio Transmitter & RFD 900+ & 1 Watt output power, 9 mile range \\ \end{tabular}
\end{table}
Table 1: Avionics and Electronics.  
Figure 3: Final main power system diagram.
## 4 Slp-macgt Model Design  
The SLP-macgt model's mechanism involves the following steps: (1) network initialization, where each node obtains information about its hop count with the sink node and the neighbor table, including neighbors within two hops; (2) based on the virtual coordinate system, a node obtains the virtual position coordinates of itself and its neighbor nodes; (3) the relay nodes are selected based on the relay node selection strategy; (4) data packets are transmitted based on adaptive coding; and (5) the source node and the attacker complete the multi-round game until the source is changed or is found by the attacker.
## 2 Materials and Methods
### Cellular Processing  
To enhance the quality of the solution obtained by the genetic algorithm, the search space was expanded using a Cellular Processing Algorithm (PCELL). This model simulates the communication and information-processing mechanisms of biological cells.  
In this work, PCELL utilized six processing cells, each containing a distinct neural network phenotype derived from the initial solution. Additionally, each cell incorporated the proposed genetic algorithm to explore local solutions effectively.  
Algorithm 3 begins by initializing an initial solution \(S_{0}\), which serves as the basis for generating a set of \(N\) processing cells \(\mathcal{C}=\{C_{1},C_{2},\ldots,C_{N}\}\). Each cell \(C_{i}\) is assigned a unique phenotype \(\phi_{i}\) based on a transformation function applied to \(S_{0}\), given by \(\phi_{i}=  
size in each cell, \(C\) stands for the cost of applying genetic operators (mutation, crossover, etc.) to a candidate, and \(E\) indicates the cost of evaluating the objective function for each candidate; in this case, the overall temporal complexity is expressed as  
\[T_{\text{PCELL}}=O(N\cdot G\cdot P\cdot(C+E)) \tag{15}\]  
A conventional genetic algorithm that operates on a single population has a temporal complexity of  
\[T_{\text{GA}}=O(G\cdot P\cdot(C+E)) \tag{16}\]  
Comparing Equations (15) and (16) shows that the computational cost of the PCELL approach increases by a factor of \(N\). Although this factor raises the computational load, the parallel exploration of multiple cells enhances the diversity in the search for solutions.  
In backpropagation, the training process iterates over the dataset to update the network parameters, where \(I\) represents the number of iterations (epochs) and \(M\) denotes the total number of parameters (weights and biases) in the network; accordingly, the complexity approximates to  
\[T_{\text{BP}}=O(I\cdot M) \tag{17}\]  
Equation (17) indicates a lower cost per iteration compared to Equation (15); this method risks becoming trapped in local optima and does not explore the solution space as efficiently.  
The PCELL approach exhibits a temporal complexity of \(O(N\cdot G\cdot P\cdot(C+E))\), which exceeds that of a traditional (\(O(G\cdot P\cdot(C+E))\)) due to the factor \(N\). This extra cost justifies itself by the improvement in the exploration of the solution space. Additionally, the backpropagation method has a complexity of \(O(I\cdot M)\), which generally offers higher efficiency per iteration but may not provide the global search robustness achieved by evolutionary methods.  
This comparison clarifies the implications of the increased computational cost of the PCELL approach compared to traditional methods and highlights the trade-off between computational efficiency and the ability to explore diverse solutions.
## 2 Theoretical Background
### Vertical Coupling Vibration Model of Train-Rail-Bridge  
When a train is running on a bridge, the bridge will vibrate and deform due to the trainload, and the vibration deformation of the bridge will be fed back to the train, which will cause the vibration deformation of the running train. Therefore, in the problem of train-bridge coupling vibration caused by trains crossing, the dynamic response of the bridge and the train contains the modal or geometric parameter information of the bridge structure [12]. With the rapid development of vibration signal testing technology and the improvement of vehicle-rail-bridge coupling vibration theory, structural damage identification based on dynamic response has received widespread attention in recent years because of its advantages, such as strong mobility, high efficiency, and economy. It can detect continuously and quickly without road closure and parking operation, so it has a good application prospect [14].  
The whole coupling system is divided into train-rail-bridge systems. The Euler-Bernoulli beam element with six degrees of freedom (DOFs) is used to model rail and simply supported beams [35], and the direct stiffness method is used to couple the rail and the bridge. [PERSON]'s contact theory connects the train and the rail system. Multi-rigid-body dynamics are used to model the train system with 10 DOFs [36]. When considering various coupling relations and irregularities, the structure is discretized, and the coupling elements of an arbitrary length combination, as shown in Figure 3, are obtained.  
Figure 2: Iterative process of IGA and GA: (**a**) \(f_{1}(x)\); (**b**) \(f_{2}(x)\).
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 2 Related Work  
With the development of intelligent transportation systems (ITSs), the significance of vehicular networks has gained prominence. Ensuring accurate data transmission is of paramount importance, and undoubtedly, routing protocols play a pivotal role in this regard. In numerous past research endeavors, many scholars have proposed various solutions to address routing challenges within VANETs. Subsequently, this discussion will encompass a tripartite review of routing protocols encompassing the following: traditional mobile routing, traffic-aware routing, and machine learning-based routing.
## 4 SENSED-SG Design and Protocols  
Our WSN-based solution called SENSED-SG furnishes a comprehensive asset management system for the Smart Grid that seeks from its very beginning to meet every imposed requirement and circumvent all the technological challenges. For the sake of clarity and due to the limitations of current solutions (e.g., ZigBee), our analyses and studies are focused on the T&D segment without any loss of generality. Going into detail, we implement specific communication layers to satisfy the main requirements mentioned above.  
The WSN here proposed is composed of COTS (Commercial Off-The-Self) devices (_i.e._, MicaZ [38]) as a platform for sensor integration. This choice is motivated by the following reasons. Firstly, the use of well-tested open-hardware and low-cost COTS sensing devices improves the reliability of our solution and eases future potential hardware replacements. Secondly, MicaZ-compatible devices show a very low power consumption, even when compared with other low-power consumption alternatives as demonstrated in [39] and [40]. To help further reduce power consumption and boost network performance, MicaZ integrates in its platform the CC2420 radio interface that has been demonstrated to be reliable and effective enough even under harsh interferences due to the adoption of DSSS and OQPSK modulation techniques [41]. And lastly, the TinyOs operating system [42] is fully supported by this platform. It is the preferred operating system for many wireless sensor-based systems [43, 44, 45]. Moreover, many studies show its good performance (in terms of processing and memory resources) and energetic efficiency in comparison with other similar alternatives [46, 47, 48]. Next subsections explain and justify our proposal from an architecture protocol perspective and following a bottom-up approach.