esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Dynamic Environment in the Code  
In the simulation framework, a conceptual buffer mechanism is proposed, based on the UAV's hypothetical current position, to enhance the loading time of the environment. In simulation contexts, the buffer is a preloaded segment of data or precomputed results that encompass a dynamic point of interest. This approach avoids the need for continuous computation or loading of data for the entire simulated environment. Instead, the simulation dynamically concentrates on a localized area around the current focal point, which, in practice, would be the UAV's position.  
The benefits of such a buffer system, if implemented, would include that the simulation would manage a smaller, localized dataset, allowing for rapid loading or computation of pertinent data, resulting in faster response times. By focusing on a buffered region rather than an extensive landscape, computational power is allocated more efficiently, processing only the immediate vicinity of the UAV's hypothetical position.  
The buffer system is designed to adapt as the focal point shifts, such as if a UAV were to traverse through the environment. The buffer is not static--it would be programmed to predict the UAV's movement and start processing data for the upcoming region in advance, while phasing out data no longer pertinent to the UAV's trajectory.  
Integration with UAV systems is a key aspect of the buffer's operational concept. Navigation and sensor data, such as the UAV's projected position and path, would inform the buffer's updates, keeping relevant data ready for use.  
This buffering strategy is particularly useful for handling environments with varying complexity. As the simulated UAV approaches areas with more intricate details, the buffer's algorithms would prioritize the loading of these specifics, ensuring that the simulation remains as efficient as possible.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 5 Current Anti-Phishing Methodologies and Techniques
### Traditional Non-Computerized Anti-Phishing Techniques
#### 5.1.2 Education  
One of the most common practices for preventing phishing attacks is to educate individuals to identify phishing emails. This is mainly implemented by businesses who have training set up for their staff. This sort of training can be delivered by a variety of different methods ranging from games [73] to simulated phishing emails. Simulated phishing emails have also been developed into embedded training methods. This particular method of training is where subjects are sent fake phishing emails that have \"bypassed\" other methods of detection, to see how many would click the malicious link included. However, when the subjects clicked the link, they are instead provided with training material about phishing. This training method has been shown to have a positive impact on more persuasive phishing emails, although little improvement was noted on less persuasive phishing emails [74].
# AgriEnt: A Knowledge-Based Web Platform for Managing Insect Pests of Field Crops
## 3 AgriEnt: Architecture
### Data Layer  
A semantic Web-based knowledge base consists of a T-Box layer, also referred to as ontology, and an A-Box layer, which consists of instances of concepts described by the T-Box layer. The A-Box layer of AgriEnt consists of a resource description framework (RDF)-based repository of instances of crops, diseases, symptoms, insects, insect pests, and treatment recommendations. This layer was developed from crop insect pests' records generated by agricultural entomology experts from Ecuador. These records contain information about the insect pests, treatments followed, dates and specific places on which they occurred, among other facts.  
On the other hand, AgriEnt not only aims to support insect pests' control and management but also increases farmers' knowledge about insect pests' control and management as well as alternatives to chemical pesticides. To this end, this platform provides users with documents related to these topics. The document database used by AgriEnt contains research works from the Agrarian University of Ecuador as well as free-available academic publications. All these documents were semantically indexed by using the semantic annotation approach proposed in [34]. This approach consists in two main phases. Firstly, all documents are semantically annotated in accordance with the AgriEnt-Ontology, and secondly, a weight is assigned to each annotation to determine how relevant is a concept described by the ontology for the document meaning. The weighting process follows the adaptation of the vector-space model for ontology-based information retrieval presented in [35]. The weighting mechanism implemented by this platform considers the concepts that explicitly appear in the documents as well as all those concepts that have a taxonomic relation with them. To achieve this goal, the Dijkstra algorithm [36] is used to find the shortest path between two concepts of an ontology.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications  
### Web-Based Applications  
The use of web technologies in digital storytelling for cultural heritage preservation and presentation is often obligatory. Web-based content is available for a wider audience, not just for people who are at the cultural heritage site, museum, or a similar establishment. In most cases, users of these types of applications are very diverse. They can be of different ages and have different education and level of computer literacy, therefore user interaction on these applications needs to be simple and intuitive (which is often not easy).  
The content of these applications is also diverse. In one web application, users can see textual content, video content, interactive video content, images, 3D objects, 3D scenes, 360\({}^{\circ}\) video, interactive quizzes and games, and many variations and combinations of these. Controls and interactions for different content can vary; for some of the content, only a computer mouse is sufficient, and these controls can be familiar for most users because they are very similar to interactions with usual web pages. For some content such as 3D scenes, a user needs to use more controls to move the avatar through the 3D scene, which can be complicated for users who are not familiar with video games. The transition from simple web page content to fully interactive 3D scenes can be confusing for users. In the transition, users often do not see the same mouse cursor, and the scene is displayed in full screen, so no familiar web browser controls will be visible. It can confuse the users and induce dilemmas such as \"where is the back button?\", \"how to exit from this and return tothe web page?\", \"where is the mouse cursor?\", \"what are the controls?\". At this point, it is very important to give users enough information, that which is visible and hard to miss, while not annoying experienced users. User expectations can also vary. Younger users and gamers might expect a game-like experience with challenges and quests, but for users with no gaming experience, even moving through the 3D scene can be challenging. Scripted 3D camera movement or limited and focused camera movement could help inexperienced users. However, this should be completed with a balance of immersion, control, and usability in mind.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 2 Methods and Materials
### Materials
#### 2.2.1 Indian Pines Data Set  
The Indian Pines data set covers the Indian Pines test site, which is located in northwestern Indiana. It shows mainly agriculture and forest. The data set consists of 224 spectral reflectance bands and is 145 \(\times\) 145 pixels. The wavelength range lies between 0.4 and 2.5 \(\times\) 10\({}^{-6}\) m. The Indian Pines data set is openly available in [16].  
Figure 2 shows the scene for six different spectral reflectance bands: 1, 50, 80, 130, 180, and 220. Different bands show different layers of materials' spectral signatures.  
Figure 2: The Indian Pines data set displayed for six different spectral reflectance bands: 1, 50, 80, 130, 180, and 220.
## 4 Comparison of Simulation with Experimental Results
Figure 7 shows the corresponding simulations of the experimental results that were shown before in Figure 5 i.e., the recovered OCDMA auto-correlation envelope \(S_{\text{L=19.5};\text{T=50}}\) for the OCDMA code propagation distance \(L\) = 19.5 km, fibre link temperature \(T\) = 50 \({}^{\circ}\)C, and chirp value: (a) \(C\) = 5 resulting in the FWHM width = 14.2 ps; (b) \(C\) = 3 resulting in the FWHM width = 13 ps; and, (c) \(C\) = 1 resulting in the FWHM width = 12 ps, respectively. Figure 5: Experimentally recovered OCDMA auto-correlation by the OCDMA decoder for the fibre link temperature \(T\) = 50 \({}^{\circ}\)C and SOA bias current set to: (a) \(I\) = 42 mA (FWHM width = 14 ps); (b) \(I\) = 14 mA (FWHM width = 13 ps); and, (c) \(I\) = 8 mA (FWHM width = 12 ps), respectively. Figure 6: SOA Gain vs. Drive Current. Next, we have investigated the cumulative effect of the varying value of the transmission link GVD (parameter \(\beta_{2}\)) on OCDMA auto-correlation FWHM's relative changes for the code transmission in an \(L=19.5\) km fibre link that was exposed to a temperature of \(T=50\)\({}^{\circ}\)C. We have shown (see Figure 8) that the impact of changing \(\beta_{2}\) (0.03-0.06) ps\({}^{2}\)/nm (affected by \(T=50\)\({}^{\circ}\)C) can also be compensated using the SOA by controlling the chirp of OCDMA code carriers. One of the key performance indicators for incoherent OCDMA systems is a power ratio between the recovered auto-correlation peak and cross-correlation noise. In [27], it was shown that this ratio 5:1 would worsen to 2.6:1 due to 20 \({}^{\circ}\)C temperature fluctuations in fibre, thus strongly affecting the system BER [28]. As shown in [29], the OCDMA system transmission quality and the BER are affected by the pulse shape. As such, a skewed pulse profile of the recovered OCDMA auto-correlation will also impact the system BER and the overall system performance.
## 2 Motivation and Contribution  
The efforts of this work were devoted mainly to monitoring aquatic life and overcoming communication issues, such as restricted energy, limited memory, loss of data, and noisy readings through distributed UWSNs. Therefore, the contribution of this paper is aimed at solving problems of communication data-induced sensors dispersed in UWSNs, by proposing a new architecture for the distributed implementation of deep learning in an underwater wireless sensor network. The distributed application distributes the deep learning middle layer into each sensor to reduce traffic and noise as well as enhance the data processing speed and lower energy usage. This is accomplished by lowering the number of connections, condensing the multi-path handover, and filtering the data delivered. The predictive filtration learning method was performed in two steps: First, two adaptive filters, the finite impulse response (FIR) filter and the adaptive line enhancer (ALE), were integrated into the underwater sensor nodes. The integrated filter is proposed to remove undesirable noise from the signal by achieving a linear response phase and passing a signal without distortion using FIR filters. Further, the ALE filter perfectly isolates the noise signal from the measured signal to retrieve the signal of interest. Second, the two filters were embedded at the cluster head (CH) level. The adaptive cuckoo filter (ACF) evaluates if an element is a member of a set through positive impulse matches; if it is conceivable, it is \"possibly in a set\"; otherwise, false negative impulses are \"definitely not in the set\". We also utilized the Kalman filter to calculate the state values of the dynamic underwater sensor networking system in such a way that the expected value of the square of the difference between the forecast and the correct state is as little as possible. Long short-term memory (LSTM) technology is also used in the proposed approach to developing computational intelligence (CI) strategies for anticipating new resources and reducing noise-accompanied data in the water.
## 2 Experiments and Methodology
### Fabrication of bPV with Textured Reflector
The bPV module structure was glass (3 mm)/EVA (\(\sim\)200 \(\upmu\)m)/c-Si bPV cell (150 \(\upmu\)m)/EVA (\(\sim\)200 \(\upmu\)m)/Glass (3 \(\upmu\)m), laminated under 150 \({}^{\circ}\)C for 10 min with a pressure of 760 mm Hg. To comprehensively evaluate the impact of both flat and textured reflectors on the performance of the bPVs, a flat reflector was also fabricated. The flat reflector, composed of solution-processed silver ink printed on a flat EVA without hot pressing, was fabricated using identical processing conditions to the textured reflector. Reflectance measurements for both reflectors conducted under an integrating sphere to capture the sum of diffusive and specular reflections (Please see Figure 1b) revealed that the reflectance of both reflectors exceeded 90%. In contrast, the specular reflectance of the proposed reflector (\(\sim\)10%) markedly differed from that of the flat reflector (\(\sim\)80%). The specular measurement of the reflector was monitored using a setup with a detector evaluating reflected light whose incident angle is less than 5 degrees.
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 3 Research Method
### Research Questions (RQ)
#### 3.1.2 RQ2  
What are the different approaches used by researchers for effective reliable provisioning? What are the strengths and weaknesses of the existing techniques in the literature? The different methods they propose to address the reliable provisioning and the considered situations such as different types of failure and their occurrence.  
The main goal of this paper is to answer the mentioned research questions after reviewing all the related literature. In RQ1, we focused on the various strategies researchers considered to address the failure in VNF chaining. The different approaches proposed for reliable VNF placement are elaborated in RQ2. We analyze those approaches and compare the techniques to recognize the strengths and weaknesses of the existing techniques and their challenges.
# A Literature Survey on Offline Automatic Algorithm Configuration
## 6 Multi-Objective META-EAs
### Model-Based Methods  
As we mentioned above, the algorithm configuration problem can be regarded as a specific version of black-box optimization, and so black-box optimization methods can be used for tackling this problem. Model-based optimization methods developed for black-box optimization build a response surface model to model the relation between the algorithm performance and its parameter values and then use this model for the configuration of the target algorithm. Sequential model-based optimization (SMBO) builds a model and uses it for selecting configurations worth further investigation iteratively. The most important difference between model-based tuners from the model-free tuning methods is the usage of response surface methods to determine configurations that will be tested. For details of the SMBO procedure, we refer to [6].  
The most notable of the SMBO procedures is the efficient global optimization (EGO) algorithm suggested by [81] that combines the design and analysis of computer experiments (DACE) model, suggested by [82], the expected improvement criterion (see [6]) and a branch and bound method for intensification of the most promising areas. EGO is only capable of dealing with deterministic algorithms or simulations.  
There are three extensions of the EGO algorithm. The first of these extensions, the sequential kriging optimization (SKO) algorithm, is suggested by [PERSON] et al. [83]. SKO adds a noise function to the DACE model and accepts the observation noise is normally distributed, which leads to the use of EGO for handling stochastic algorithms. Secondly, [PERSON] et al. [84] suggested another extension that uses a Gaussian process model to optimize marginal performance across a set of \"environmental conditions\". Their method is expensive and can only be used for optimizing mean performance for a set of the problem instance.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 1 Introduction
A fixed compensation of the phase difference achieves the ZVS. It is not considered that the phase difference will change, and fixed compensation cannot make the system always maintain a reasonable ZVS. The above analysis shows that the existing methods have no closed-loop control of the phase difference. It is difficult to maintain the system under the optimal ZVS state at all times with the compensation of the fixed phase angle when there are large fluctuations in the phase difference. This paper proposes a frequency-tracking method based on FPGA to accurately track the phase difference and solve the above problems. The method achieves accurate control of the phase difference of the system by closed-loop control of the phase difference to make it into an optimal state, which enables the system to achieve an excellent ZVS characteristic and ensure the system reactive power loss remains at a low level. Section 2 presents equivalent modeling of the WPT system and analyzes the effects of frequency detuning, frequency splitting, and phase differences on the system. Section 3 proposes an FPGA-based frequency-tracking method to track phase differences and describes the composition and working principle of the method. Section 4 verifies the feasibility of the method through experiments. Section 5 concludes the paper,
# Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review
## 3 Search Results and Meta-Analysis
### Decision Tree  
A decision tree (DT) is an ML technique that is used for creating decision support tools in the trees of inner nodes, which represent binary options over the features [69]. For many years, there have been several methods based on the decision trees that are used to detect financial frauds. [PERSON] and [PERSON] [78] developed a DT base method to classify credit card transactions as normal or suspicious data. The method was evaluated with different accuracy metrics. The results indicated that DT performed better than the existing approaches with a high degree of accuracy. A study was conducted in [79] for auto fraud detection by using an ML technique. The authors compared three different methods including NB, DT, and RF methods, and the result proved that DT outperformed other methods.  
[PERSON] and [PERSON] [67] investigated the transaction behavior of credit cardholders to differentiate between normal and abnormal transactions. The authors applied different ML algorithms such as Random Tree (RT) and NB. The methods were evaluated on two synthetic datasets. The results of the evaluation show that RT performed better compared to other methods. Another similar approach to detect fraud in the auto insurance sector was introduced in [42] by utilizing an adaptive oversampling method. The method was able to delete the imbalance classes from the insurance datasets.  
The genetic algorithm (GA) draws inspiration from natural evolution [35]. It uses a set of suggestions that are often represented as binary strings called chromosomes to search for the best solutions [75; 100]. Genetic programming belongs to the area of evolutionary algorithms that widens the use of genetic methods to support the exploration of computer programs' space [76]. Many approaches used GA in the literature. For example, in [35] [PERSON] and [PERSON] employed GA for financial fraud detection in companies. [PERSON] et al. [71] present a new technique for fraud detection in credit card transactions to address the issue of the conventional methods in detecting minority class objects in the imbalanced datasets using the K-means and GA techniques. The authors first employed the K-means method to group and classify the minority instances; then, they applied the GA method in each group to create new instances in order to obtain a new training dataset. In addition, the GA algorithm was also utilized by [PERSON] et al. [61] to provide a solution to these problems of detecting fraudulent credit card transactions. The study was applied to a real-life application project using transactional data from the real world.
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 2 Method
### Completion Strategy for Multi Missing Values
#### 2.2.1 Nonlinear Regression Analysis  
The regression analysis studies the influence the degree of one variable to the other and estimates or predicts other variables' changes. However, in practice, the changes of most variables are not one variable- but multi-variable-dependent. Moreover, the relation between the explained variables and many explanatory variables, such as the concrete dam deformation, is non-linear. According to the theory of the statistical model, the concrete dam deformation is mainly affected by three components--water pressure, temperature, and time effect. Each component includes more than one influence factor, so it is a multivariable nonlinear regression problem. The statistical model uses several factors to fit the deformation trend and obtains the multiple regression equation of deformation. Therefore, when a continuous multi-value is missing in the deformation time series of a certain measuring point, with known environmental quantity data, the regression relationship between the two can be established from known values in the sequence. The expression of the multivariable nonlinear regression analysis model is:  
\[\delta_{t}=f(\phi_{1},\phi_{2},\cdots,\phi_{n})+\varepsilon \tag{22}\]  
where \(f\) represents the general function between \(\delta_{t}\) and the influence factor, and \(\phi_{i}\) is the influence factor of concrete dam deformation.  
After the equation between the deformation value and its influence factor is established, according to the measured data, the coefficients of each factor in the model can be determined by the least square method, and the multiple regression model is thus established. The value of missing information can be obtained by substituting the influence factor data of missing information segments into the above expression.  
Given that the statistical model is established based on statistical methods and combined with dam theory, when the monitoring data sequence is long, if the factors in the statistical model are representative, the model can accurately reflect the deformation trend of concrete dams. Therefore, the multivariable nonlinear regression analysis model has been widely recognized in the dam construction field.
## 7 Discussion
### Speech Command Word  
From the results of the experiments, it was observed that speech command selection affects the speech recognition accuracy, as some speech command words were found to be more resilient to corruption at higher noise levels, maintaining over 90% success rate at 75 dB, whereas the average rate at 75 dB was just over 65%. In order words, the success of speech command words such as SW4 'right', SW8 'land', and SW12'stop', at higher noise levels, suggests that some speech command words are more noise resistant than others and that a careful selection of these as part of the speech command phrase could improve the robustness and accuracy of speech recognition in spite of high noise levels, thereby contributing to a more successful human aerobic speech control interaction.