esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 3 Methodology
### Classification Algorithms
#### 3.2.1 Multilayer Perceptron  
In this study, we employed the MLP as our baseline model to classify a set of input features, as it uses multiple layers of nodes to make predictions based on input features. The _MLPClassifier_ is a feedforward neural network model designed to determine the classification of a specific static analysis alarm. It consists of two fully connected layers, namely, _fc1_ and _fc2_, which perform linear transformations on the input data. The _fc1_ layer maps the input features from the _input_size_ dimension to the _hidden_size_ dimension, followed by a rectified linear unit (ReLU) activation function to introduce nonlinearity, while the _fc2_ layer further maps the output of _fc1_ from the _hidden_size_ dimension to a two-dimensional space, producing the final classification output. As is shown in Figure 4, the model architecture consists of an input layer with 768 features, a hidden layer with 128 neurons, and an output layer with 2 neurons. The input features consist of context embeddings generated by CodeBERT, which capture contextual and semantic information related to the analyzed warning details and code snippets. The input layer receives the input data with its 768 features, and the hidden layer performs computations and feature extraction. Finally, the output layer produces the predicted outputs, representing the two classes for classification. To train the MLP model, we utilize a ground-truth dataset of warning data, where each alarm is annotated as either true positive or false positive. In summary, the _MLPClassifier_ is a two-layer MLP architecture that leverages linear transformations and nonlinear activation to extract relevant features and perform binary classification tasks. It serves as a baseline model for comparing performance with the following well-established neural network architectures.
## 5 Security Analysis and Comparison
### Noise Attack  
Images usually suffer some degradation during transmission. We evaluate the suggested method's resistance to noise threats, such as salt and pepper noise in this section (SPN) [8]. The Lena image is utilized as a test image in this simulation. Figure 14 shows the encrypted images with varying levels of noise attack (noise density 0.005). As a consequence, we can infer that the suggested method has the highest level of resistance to SPN attacks. Furthermore, these results demonstrate that the suggested encryption approach is capable of recovering the original image in the midst of a noise attack.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Liposoluble Nntraceuticalticals
#### 4.1.4 Essential Oils  
Essential oils are volatile flavor substances extracted from plant tissues. They exhibit a high potential to inhibit the growth of microorganisms [115], but they are chemically unstable, prone to oxidation and volatilization, which limits their application in food preservation [116]. Encapsulation is a popular technique in the field to stabilize and release flavor compounds sustainably [117]. Among all the reported encapsulation techniques, lipid-based nanovehicles, such as nanoemulsions, SLNs, and NLCs, seem to be among the most promising (and most investigated) delivery systems for antimicrobials, including essential oils [21]. To date, however, only nanoemulsions, liposomes, and biopolymeric particles have been successfully applied in real foods [116]. Table 1 also displays some examples of using SLNs or NLCs as containers or sustained-release delivery systems for essential oils (including citral, eugenol, and carvacrol). The EE of essential oils in such lipid nanoparticles ranged from 48 to > 98%, depending on the type and addition ratio (relative to solid lipid content) of essential oils, and fabrication process (Table 1), reflecting the volatile and unstable nature of these hydrophobic flavor substances during the processing. The maximal LC could reach up to 30% for carvacrol, to be loaded in SLNs formed using the microemulsion template method [93], or up to approximately 53.7% for frankinense and myrrh essential oils, to be loaded in SLNs using the hot HPH process [95].  
As in the case of fish oil-loaded NLCs, many essential oils can be directly used as the liquid lipid to fabricate NLCs as nanavehicles for themselves. For example, [PERSON] et al. [91] successfully fabricated such NLCs as nanavehicles for citral, with a very high EE of 99.8%, and showed that the loaded citral in the NLCs was more effective as a natural preservative agent for the inhibition of various gram-positive or negative bacteria and fungi, than the conventional citral emulsion. Citral could be also evenly distributed in the solid lipid matrix, even at a high LC of 22.8% and, as expected, the incorporation of citral reduced the crystallinity of the solid lipid (glycerol monostearate) and improved the storage stability of citral [92]. A similar result of a homogenous distribution was observed for the carvacrol-loaded SLNs formed though a microemulsion template method, at a very high LC of up to 30% [93].  
[PERSON] and colleagues [109] reported that astaxanthin could be co-entrapped with carvacrol in the SLNs with beeswax as the solid lipid, at a high EE of 88.5% (for carvacrol). Most of these previous works indicated that the nanencapsulation of such essential oils (or natural antimicrobials) in SLNs or NLCs improved their antimicrobial activities, but did not show distinct toxicity on human-cultured cells [26; 85; 91; 109; 116].
# Blockchain Application in Internet of Vehicles: Challenges, Contributions and Current Limitations
## 1 Introduction  
### Motivation and Problem Statement  
Given the technological breakthroughs in communications, sensors, and electrical systems, traditional embedded systems and controllers are being overtaken with a more advanced system, known as a Cyber-Physical System (CPS). This CPS is typically linked to internet technologies to provide a link between the cyber and physical worlds [1]. CPS has recently gained popularity and has already been widely used in various parts of our lives. One example of CPS is the Intelligent Transportation System (ITS). The main objective of ITS is to create comfortable, secure, dynamic, and efficient transportation within ITS and smart cities in general [2]. Within the ITS, a concept named Internet of Vehicles (IoV) is presented,which is powered by smart vehicles, Internet of Things (IoT), and Artificial Intelligence (AI) methods. Cars are becoming smart vehicles that can communicate with other vehicles, drivers, passengers, and road-side units (RSUs) through the Internet and Dedicated Short Range Communication (DSRC) technology (i.e., short-range to medium-range wireless communication channels) [3]. Internet of Vehicles, in conjunction with the fast deployment of Electric Vehicles (EVs) and the use of renewable energy in the day-to-day activities of energy users, is leading to the emergence of a greener, smarter community. Nevertheless, load-balancing issues, security concerns, privacy leaks, and a lack of incentive mechanisms remain unsolved [4]. Another issue for IoV is the rapidly increasing number of cars and objects linked to the IoV [5]. As a result of this circumstance, a significant volume of data has to be managed on a wide scale. When the data amount is huge, the reliability of data propagation in the network becomes a major concern. Many techniques have been proposed in the literature to address the aforementioned issues [6; 7]. However, due to the resource and time demands of basic cryptographic computations on network devices, these techniques are insufficient to produce good results. In most situations, energy trading systems use a centralized method to manage transactions between EVs and other IoV objects (e.g., charging stations). This approach raises the probability of a single point of failure and, thus, the respective cost increases [8]. Moreover, dynamic wireless charging is a promising technology to charge EVs using on-road charging segments. In order to ensure the effective utilization of wireless charging, communication and coordination need to be established between the EVs and the different network entities [9]. Therefore, forming an IoV is required in this scenario. However, considering the V2X communication, wireless EV charging presents significant challenges in terms of reliable communication and secure authentication [10]. Thus, due to the aforementioned limitations, the present system and the communication protocols cannot provide sufficient response to the underlying IoV requirements and the emerging EV use cases.
# Residual Stress in Laser Welding of TC4 Titanium Alloy Based on Ultrasonic laser Technology
## 3 Experimental Process  
The experimental process was divided into three parts: preparation of specimens with continuous laser welding, using ultrasonic laser technology for measurement of the residual stress in laser welding, and hole-drilling to verify the experimental results.
###### Abstract  
Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem. In the context of using wireless signals to classify illegal Unmanned Aerial Vehicles (UAVs), this paper explores the feasibility of using GAN to improve the training datasets and obtain a better classification model, thereby improving the accuracy of classification. First, we use the generative model of GAN to generate a large datasets, which does not need manual annotation. At the same time, the discriminative model of GAN is improved to classify the types of signals based on the loss function of the discriminative model. Finally, this model can be used to the outdoor environment and obtain a real-time illegal UAVs signal classification system. Our experiments confirmed that the improvements on the Auxiliary Classifier Generative Adversarial Networks (AC-GANs) by limited datasets achieve excellent results. The recognition rate can reach more than 95% in the indoor environment, and this method is also applicable in the outdoor environment. Moreover, based on the theory of Wasserstein GANs (WGAN) and AC-GANs, a more robust Auxiliary Classifier Wasserstein GANs (AC-WGANs) model is obtained, which is suitable for multi-class UAVs. Through the combination of AC-WGANs and Universal Software Radio Peripheral (USRP) B210 software defined radio (SDR) platform, a real-time UAVs signal classification system is also implemented.  
GAN; AC-WGANs; wireless signals; classify model; USRP Article
# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 3 The Model
### Dynamic Inputs  
A first assumption is defined for the model, to allow for a middleware-based solution to multi-scale simulations. The purpose of the defined assumptions is to circumscribe our study to a specific context. The proposed middleware model and algorithms are applicable to the systems adopting the same assumptions.  
**Assumption 1** (Dynamic inputs).: _The considered mobility simulations can receive data dynamically. More precisely, they allow for exchange data at each simulated time step._  
This first assumption is necessary because, if the simulations do not allow for intervention during execution, the middleware has no leverage to influence their behavior. The only possible common use of the simulations in this case is to use them in sequence, which is of little interest in the context of this work. Fortunately, a large number of simulations are open source. Thus, even if some of them do not allow, by design, an interaction at each time step or an outside simulation control, they can be modified to do so. These modifications only involve interactions with the simulations and do not alter the core of their operation.
# A Survey of Using Swarm Intelligence Algorithms in IoT
## 3 Applications of Swarm Intelligence Algorithms in WSN
### CH Selection Algorithms Based on Si  
Routing protocols in WSNs can be divided into planar routing protocols and hierarchical routing protocols according to communication logic [18]. In hierarchical routing protocols, networks are split into clusters of different sizes. Each cluster consists of a CH and several members of the cluster. The cluster head manages or controls the member nodes in the whole cluster, coordinates the work among member nodes, takes charge of the collection of information in the cluster, the fusion processing of data and the forwarding between clusters. Sensor nodes send their perception data to the associated CH. Then, CHs add them up and send them to a BS or the sink node. Therefore, the CH energy consumption is generally faster.  
The CH selection is a key problem in WSN design. In this paper, the CH selection method is classified according to sensor coverage mode, clustering method, whether the number of clusters has changed, and the mobility of CH, whether the size of clusters has changed, Intra-cluster communication method and factors for selecting CH. The explanation of these classification methods can be found in Section 3.1.1. Also, they are compared with low energy adaptive clustering hierarchy (LEACH), a classical hierarchical routing protocol that does not employ SI methods. The basic idea of LEACH is to randomly select cluster head nodes in a cyclic manner and distribute the energy load of the entire network to each sensor node evenly. Network energy consumption will decrease, and the overall network survival time will increase. The explanation of classification attributes is described below:
## 6 Prediction of Field Strength by Construction Log Data and Future Perspectives
### Future Perspectives  
As described above, the _W/C_ ratio has a close relationship with the unconfined compressive strength and can be applied in QC/QA during construction to assure the design strength. Many construction parameters are continuously measured during the production in Japan, which includes plotting the position of mixing shaft, penetration and withdrawal speeds of mixing shaft, rotation speed of mixing blade, electric current for driving mixing blade, lifting load of mixing blades, and accumulated cement volume. It is possible to estimate the soil type and water content at each depth based on their measured values, and the field-stabilized soil strength can be estimated by Equations (4) and (5). Once the strength during production has been predicted, several countermeasures can be adopted and carried out during production to assure the design criteria.  
In fact, many attempts have been made to estimate the types and properties of ground from the construction log data in several ground improvement methods. However, due to the errors and scatters in the measured data and large variations in ground properties, it was not possible to estimate them with acceptable accuracy. It is expected that the types and properties of ground can be estimated by referring to a vast database using AI system in near future. In recent years, information communication technology (ICT) has been applied in the construction industry to increase the productivity and reliability of construction. ICT construction machines feature advanced technology, such as machine guidance systems and production control systems, to assist the operator and site management systems in processing productivity and work progress data.
# A Gentle Introduction to Applications of Algorithmic Metatheorems for Space and Circuit Classes
## 2 The Concepts and Ideas Behind Courcelle's Theorem
### Decomposing the Problems: Tree Decompositions
#### 2.2.3 Defining Decompositions  
It turns out that not only do sensible and correct strategies always have the two properties (called the \"connectedness property\" and the \"covering property\"), a tree together with bags having them _is_ essentially already a strategy for catching [PERSON]: Suppose the detectives are positioned at the vertices of a bag \(B(n)\) of a node \(n\) of \(T\) and suppose [PERSON] is on a certain vertex \(v\in V\). [PERSON] is restricted to the vertices \(u\) that are reachable from \(v\) in \(V\setminus B(n)\). By the connectedness property, the nodes of \(T\) that contain a given \(u\) form a connected subset of \(T\). By the covering property, the union of these subsets is still a connected subset of \(T\). This means that this union is completely below a single child of \(n\). By moving to the positions in the bag of that child, our detectives will eventually catch [PERSON].  
To sum up, the easiest way of _defining_ game strategies is to define a tree decomposition of a graph \(G=(V,E)\) as any tree \(T\) with node set \(N\) together with a function \(B\colon N\to 2^{V}\) that assigns a _bag_\(B(n)\subseteq V\) to each node \(n\) of \(T\) such that two conditions are met:  
1. _Connectedness property_: for each vertex, \(v\in V\), the set \(\{n\in N\mid v\in B(n)\}\) is connected in \(T\).
2. _Covering property_: for each edge, \((u,v)\in E\), there is a node \(n\) with \(u\in B(n)\) and \(v\in B(n)\).  
The _width_ of a tree decomposition is the maximum size of any bag in it minus one. The _tree width_ of a graph is the minimum width of any tree decomposition for it. We have already seen that the tree width of a tree is one, the tree width of a cycle is two, and the tree width of an \(n\)-vertex clique is \(n-1\).
# Onboard Processing in Satellite Communications Using AI Accelerators
## 2 Onboard AI Processing
### Complexity  
When assessing the applicability of the AI technique, the complexity and runtime analysis of the algorithm must always be discussed and taken into account. Consequently, it is crucial to quantify the resources required to execute each selected AI-based technique. This includes the following:  
* Computational complexity. Number of operations as a function of the input size of an algorithm.
* Computational time. The execution time of an algorithm depends on the machine that executes it.
* Memory. Memory in neural networks is needed to store input data, weight parameters, and activations as input propagates through the network.
* Power consumption. Power is critical in both phases of AI-based techniques, the training phase and the testing phase. All available processing elements are used in a highly parallel fashion for training. For the inference phase, algorithms are optimized to maximize performance for the specific task for which the model is designed.
###### Abstract  
This study introduces a robust model-based framework designed for the verification and validation (V&V) of Attitude Determination and Control Systems (ADCSs) in nanosatellites, focusing on magnetic actuation while still being applicable to larger spacecraft platforms. By employing Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and Hardware-in-the-Loop (HIL) methodologies, this framework enables a thorough and systematic approach to testing and validation. The framework facilitates the assessment of long-term maneuvers, addressing challenges such as initial small-attitude errors and restricted 3D movements. Two specific maneuvers are evaluated: detumbling and nadir pointing, utilizing quaternions and a comprehensive suite of sensors, including six sun sensors, a three-axis magnetometer, a three-axis gyroscope, GPS, and three magnetorquers. The methodologies--MIL, SIL, PIL, and HIL--integrate the behaviors of digital sensors, analog signals, and astrodynamic perturbations. Based on an optimized SIL environment, Monte Carlo simulations were performed to optimize control gains for nadir pointing, achieving a mean pointing accuracy of 11.69\({}^{\circ}\) (MIL) and 18.22\({}^{\circ}\) (PIL), and an angular velocity norm of 0.0022 rad/s for detumbling. The HIL environment demonstrated a mean pointing accuracy of 9.96\({}^{\circ}\) and an angular velocity norm of 0.0024 rad/s. This comprehensive framework significantly advances the design and verification processes for nanosatellite ADCSs, enhancing the reliability and performance of nanosatellite missions.  
Keywords:attitude determination and control system; CubeSat; hardware in the loop; model-based design; verification +
Footnote †: journal: Medical-Based Design and Testbed for CubeSat Attitude Determination and Control System with Magnetic Actuation
## 3 Data Processing and Kinematic Analysis
### Object Tracking and Management
#### 3.4.5 Combination  
In the last step, the updated states of each filter are used to generate the final states and covariances using the model probabilities of tracks as follows.  
\[\hat{x}_{(q,k)}=\sum\limits_{i}x_{i,q,k}\ \mu_{i,q,k} \tag{19}\]  
\[P_{(q,k)}=\sum\limits_{i}\left[P_{i,q,k}+(\hat{x}_{q,k}-x_{i,q,k})(\hat{x}_{q, k}-x_{i,q,k})^{T}\right]\ \mu_{i,q,k} \tag{20}\]
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 4 A Numerical Experiment
### Experimental Results
#### 4.2.2 Exploring Optimal Hyperparameters of LSTM
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Referred** & **Feature** & **Method** & **Validation Accuracy** \\ \hline (This paper) & Kurtosis, skewness, crest factor & LSTM with 8 time steps, 3 LSTM layers, 115 hidden neurons & 0.9814 (0.099) \\ [PERSON] et al. [40] & RMS, kurtosis, RMSE 1  & 0.7420 \\  & Kurtosis, waveform factor & LSTM with 8 time steps, 3 LSTM layers, 150 hidden neurons & 0.7846 \\  & & hidden neurons & 0.9312 \\  & & Back propagation (BP) network with 3 hidden layers containing 150 hidden neurons & 0.7841 \\ [PERSON] et al. [3] & Kurtosis, waveform factor, waveform entropy & Stacked autoencoder (SAE) with 3 hidden layers containing 150, 100, 50 hidden neurons each & 0.8677 \\  & & Convolutional neural network (CNN) with 2 convolutional layers (5 \(\times\) 5 \(\times\) 32, 5 \(\times\) 5 \(\times\) 16) and 2 pooling layers & 0.9203 \\ [PERSON] et al. [41] & RMS, kurtosis, skewness, RMSE 1, 8 frequency-based features (total 12 features used) & CNN with 5 convolutional layers, 3 pooling layers, and 3 additional fully connected layers except for one before output layer & 0.9858 \\ [PERSON] [42] & Seven time-based features (including RMS, kurtosis, skewness, etc.) and 4 frequency and wavelet features (total 11 features) & k-NN with features resulting from PCA & 0.9167 \\  & & & k-NN with features resulting from LNPP 3  & 0.9722 \\ [PERSON] et al. [43] & Five features (max. value, centroid, abs. centroid, kurtosis, and simple sign integral) & Autocorrelation-aided random forest with feature selection and pre-processing including binary classification & 0.9790 \\  & & & \\  & & & \\  & & & \\  & & & \\  & & \\  & & & \\ \end{tabular}
\end{table} 
Table 5: Comparison of validation accuracy obtained from this research and others. pre-processing is required, and high complexity brings about a lack of scalability which stems from the nature of the decision tree. Compared to [3], we could observe the following matters. The combination of kurtosis and waveform factor (0.7841) is not desirable, which is worse than using kurtosis alone (0.7946 in Table 4). This tendency might result from multicollinearity between kurtosis and waveform factor, which showed a high correlation coefficient of 0.85 in a previous feature selection experiment. Furthermore, from the comparison of results obtained by feature vectors with the same length, the validation accuracy obtained in this study is higher than that obtained in [3]. In terms of the structure of LSTM, in addition, the number of hidden neurons in each LSTM layer used in this study is smaller than that of [3], which supports that features used as HIs in this paper can express the state of bearing components more elaborately. As a result, desirable levels of hyperparameters could be reached by the GA, based on efficient and effective HI derivation.