esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.10 Dynamic Event Detection Algorithm  
The dynamic event detection algorithm (DEDA) is an algorithm that uses ML techniques to detect and classify events in real-time. DEDA can identify abnormal behaviour or circumstances that may indicate a fault or failure in the system. It works by analysing system data, such as logs or sensor readings, and using an ML algorithm to identify patterns and trends in the data. The algorithm can then classify events as normal or abnormal based on identified patterns. DEDA and a modified ensemble of bagged decision trees with an added boosting mechanism based on a machine-learning algorithm were proposed by [26]. The algorithm interprets the dynamic events and decomposes such events into user-specific field regions to facilitate decision-making in restoring unstable power stations. The novel algorithm, as proposed, can detect patterns in the dynamic data and distinguish the data based on the underlying events. Once the underlying event is detected, the algorithm decides locally on each power generating station and restores the system after a major fault event. The algorithms are independent of each other, as they are installed separately on each power generating station. DEDA can detect events that may indicate fault or failure in the system, such as a sudden increase in CPU usage or a spike in network traffic. Once an abnormal event is detected, the system can mitigate the threat, such as shutting down a component or alerting security personnel. An advantage of DEDA is its ability to adapt to changes in the system over time, allowing it to monitor and detect new events continuously. It can also be used with other self-healing techniques, such as automatic system reconfiguration or network rerouting, to help restore the system to a healthy state after the event. A potential limitation is its reliance on accurate and timely data. DEDA may produce false positives or miss essential events if the data are incomplete or delayed. DEDA can be computationally expensive, especially for large-scale systems or high-volume data streams, and may require significant expertise to configure or optimise.
# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 4 Evaluation
### Experimental Setup
#### 4.1.2 Datasets  
In our work, we utilized five datasets to train, validate, and test our model: DFDC [73, 74], TrustedMedia (TM) [75], DeepfakeTIMIT (TIMIT) [76, 77], Celeb-DF (v2) [78, 79], and FaceForensics++ (FF++) [80]. DFDC and FF++ are well-known benchmark datasets for deepfake detection. TM is created using a diverse range of deepfake manipulation techniques.  
The DFDC dataset is the largest publicly available dataset and contains over 100,000 high-resolution real and fake videos. The dataset was created using 3426 volunteers, and the videos were captured in various natural settings, at different angles, and under different lighting conditions. The dataset was created using eight deepfake creation techniques.  
The FF++ dataset comprises 1000 original videos collected from YouTube, which have been manipulated using four automated face manipulation methods: Deepfakes, Face2 Face, FaceSwap, and NeuralTextures [81]. The dataset includes compressed videos with quantization parameters of \(c23\) and \(c40\) and various video resolutions. The TM dataset consists of 4380 fake and 2563 real videos, with multiple video and audio manipulation techniques used. The TM dataset is used only in the training phase. The Celeb-DF (v2) dataset consists of 890 real videos and 5639 deepfake videos.  
We randomly extracted approximately 30 frames per video from each dataset to ensure diversity in our training data. To mitigate the different ratios between fake and real videos in the DFDC and TM datasets, we extracted a higher number of frames from their real videos. The DFDC dataset has a ratio of 6:1 for fake to real videos, and TM has approximately a 2:1 ratio.  
By using a variety of datasets that utilize multiple deepfake creation techniques and videos captured in multiple settings, we aim to enable better generalization and robustness to varying environments. Notably, our model is trained (and evaluated) using significantly more datasets than those used for previous models (see Table 1).
## 3 Model Calibration and Optimization
### Genetic Algorithm Optimization Framework
#### 3.2.6 Genetic Algorithm Framework  
The genetic algorithm optimization process, illustrated in Figure 7, integrates mechanistic crop modeling with evolutionary computation to resolve the nitrogen-yield tradeoff. Initialized with a population of 10 randomized fertilization plans, the algorithm iteratively refines solutions over 1000 generations through tournament selection, two-point crossover (probability: 0.85), and adaptive mutation rates (0.05-0.15). Each candidate plan undergoes DSSAT simulation to predict lint yield and nitrogen leaching.
## 7 Result and Discussion
### RQ2: What Are the Existing ML-Based Approaches to Detect Attacks in RPL-Based 6 LoWPAN?
[74] provided a dataset generation approach for anomaly-based IDS in IoT and IIoT networks, using the Cooja simulator to generate comprehensive IoT/IIoT datasets. The generated dataset from the simulated scenario captures information from Contiki plugins, such as \"powertrace\", which contains features related to the energy consumption of IIoT devices, such as CPU consumption and Low Power Mode (LPM). The Contiki tool, known as \"Radio messages\", contains network traffic features employed in the generation process of the IoT/IIoT dataset. In addition, the authors generated four datasets in this work; two of them contain benign traffic, and the other two comprise malicious traffic. In both benign and malicious scenarios, the authors generated the datasets from \"powertrace\" and \"Radio messages\" separately. Moreover, in the malicious scenario, the authors implemented UDP Flooding attacks. Furthermore, during the generation process, the authors computed the total energy consumption of the nodes.
## 5 Conclusions and Future Perspectives  
According to the reviewed scientific research, it can be concluded that the quantity and quality of the compounds extracted from jackfruit depend on several factors. The main factors to consider are the extraction method, whether assisted by any emerging technology or not, the conditions of extraction, and the properties of the matrix used.  
Regarding the performance of the technologies reviewed, the conventional technology with the best performance for recovering of phenolic compounds was alkaline hydrolysis extraction using NaOH in pulp samples. Additionally, extraction with methanol at 90% using peel as a matrix had the second higher total phenolic content. From the emerging technologies extraction reports, the best results were for SFE with CO\({}_{2}\) using jackfruit seeds. Concerning the application of emerging technologies as a pre-treatment, even though they may have not necessarily improved yields of extraction, their use can be beneficial for other aspects, such as process sustainability, by decreasing the use of organic solvents, shortening extraction times, and reducing the amount of energy consumed. However, more research is required to understand the effect of other emerging technologies, alone and in combination, when extracting these compounds.  
Other research opportunity areas include the following: (1) determining the most convenient technology for carotenoid extraction from jackfruit, (2) applying non-conventional assisted extraction for functional ingredients present in jackfruit pulp, and (3) evaluating conventional and emerging technologies extraction methods for essential fatty acids in jackfruit need. Moreover, future studies should consider other jackfruit varieties/species and compare results based on their place of origin. The application of jackfruit functional ingredients also requires more investigation, not only for the food and pharmaceutical industries, but also for other areas such as the cosmetic area.  
Conceptualization, T.G.-C. and V.R.-M.; methodology, F.C.C.-C. and V.R.-M.; investigation, F.C.C.-C. and V.R.-M.; writing--original draft preparation, F.C.C.-C.; writing--review and editing, T.G.-C. and V.R.-M.; visualization, F.C.C.-C., T.G.-C., and V.R.-M.; supervision, T.G.-C. and V.R.-M.; project administration, V.R.-M. All authors have read and agreed to the published version of the manuscript.  
Funding: The authors acknowledge the financial support by Tecnologico de Monterrey within the funding program \"Fund for financing the publication of Scientific Articles\". [PERSON] thanks Consejo Nacional de Ciencia y Tecnologia (CONACyT; CVU: 1078840) Mexico for scholarship funding and Tecnologico de Monterrey for academic support. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data sharing not applicable to this article. Conflicts of Interest: The authors declare no conflict of interest.
## 3 Results and Discussions
### Question Analysis
#### 3.3.2 Support Mechanisms (RQ3)  
Once the main application domains of the approaches adopted in the selected studies were identified; another important step would be to identify and categorize the support mechanisms proposed or used to support or achieve the objectives of the approaches.  
Table 11 presents the mapping of the support mechanisms identified in the selected studies, grouping them into categories and subcategories.  
Figure 4: Quantitative distribution of studies by application domain category over time period.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Support Mechanism Categories** & **Techniques/Mechanism** & **Studies** \\ \hline \multirow{7}{*}{Cl. Systems/Software/Tools/Programming Languages} & **Maluware Analysis** & [33] \\ \cline{2-3}  & **Malufranze, SOA** & [54] \\ \cline{2-3}  & **Big Data** & [54] \\ \cline{2-3}  & **Forensic Practices** & [53] \\ \cline{2-3}  & **Semantic Web, Data models, Functional programming, Data processing, Formal languages** & [55] \\ \cline  
After analyzing the selected studies, 4 general categories were identified and were used to group the identified support mechanisms. For categories C1, C2 and C3 a set of techniques/mechanisms applied were identified. In the case of category C1 despite the use of systems, software, among others, a set of techniques or auxiliary mechanisms were also adopted. The exception is for those studies grouped into the \"Others\" subcategory that was used to identify studies that adopted a set of market or developed solutions to achieve the study objectives. The same nomenclature was used in category C2 to identify studies that adopted a set of hardware market solutions.  
Solutions that adopted standards, theories and frameworks (C3) were grouped by the identified support mechanism (8 studies). The category C4 was the one that grouped the largest amount of work (58 of 71 studies), where it is possible to perceive a greater tendency to apply data mining techniques to combat money laundering. The techniques identified in the selected studies were grouped by class based on the classification framework proposed by Reference [7].  
It should be added that some studies were included in more than one category because they adopted different support mechanisms in their approach. For example, studies with subcategory \"Others\" in categories C1 and C2 (with exception of the study [S16], which is presented only in categories C1 and C2) are also present in category C4.  
Figure 5 presents the quantitative distribution of the selected studies by category of support mechanism where, for the case of category C4 the studies were distributed into subcategories that grouped the studies into the identified application classes. Of the 58 studies grouped in category C4, 32 used clustering techniques in their approaches, which show a greater tendency in their adoption compared to other studies.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 3 Methodology
### Designing the Loss Function  
To consider both the image and blur kernel losses and accelerate model convergence, we adopt a joint optimization method. This can be expressed as follows:  
\[L(u,k)=L_{u}+\alpha L_{k} \tag{12}\]  
where \(\alpha\) is a hyperparameter. Due to the two orders of magnitude difference between \(L_{u}\) and \(L_{k}\), we set \(\alpha=100\) to balance the gap. Settings \(L_{u}\) and \(L_{k}\) use structural similarity index (\(SSIM\)) loss and mean squared error (MSE) loss, respectively, which can be expressed as follows:  
\[\begin{array}{c}L\big{(}u,u^{gt}\big{)}=1-SSIM\big{(}u,u^{gt}\big{)}\\ L\big{(}k,k^{gt}\big{)}=\big{\|}k-k^{gt}\big{\|}_{2}\end{array} \tag{13}\]  
where \(u\) and \(u^{gt}\) represent the sharp and recovered images, respectively, and \(k\) and \(k^{gt}\) denote the true and estimated blur kernels, respectively.
# Ultimate Compressive Strength of H-Section Stub Columns Subject to Random Fitting Corrosion Damage
## 3 Results and Analyses
### Equivalent Thickness Method  
Accurately determining the minimum cross-section of randomly pitted columns is a challenging task, despite the close relationship between the failure of damaged columns and their cross-sectional area. Therefore, this section aimed to comparatively analyze various methods for equalizing thickness loss caused by random pitting corrosion. The objective was to replace the modeling of random pitting corrosion with a uniform thickness loss for assessing ultimate strength. The methods verified in this section included the \(DOV-\)based equivalent thickness, \(t_{a}\), namely, the average thickness resulting from general corrosion with the same volume loss caused by corrosion damage. Additionally, the minimum cross-sectional equivalent thickness, \(t_{min}\), and the thickness correlated to the standard deviation of depth of actual measured corrosion pits (referred to as \(\sigma-\)based equivalent thickness, a modification of the average thickness), \(t_{e}\), were considered. The definitions of these three types of equivalent thicknesses were as follows:  
\[t_{a}=t(1-DOV), \tag{2}\]  
\[t_{min}=t\times\frac{A_{min}}{A_{0}}, \tag{3}\]  
Figure 12: Influence of web width-to-thickness ratio on ultimate strength: (**a**) \(t_{f}\) = 7.5 mm; (**b**) \(t_{f}\) = 11.5 mm.  
\[t_{e}=t_{a}+k\sigma. \tag{4}\]  
where \(t\) is the original thickness of plate elements (flange or web) of as H-section column. \(A_{min}\) is the area of the minimum cross-section of the pitted column, while \(A_{0}\) is the area of section of the intact column. Additionally, \(\sigma\) is the standard deviation of depth of random corrosion pits, and \(k\) is a constant coefficient that requires determination for the subsequent investigation. In this study, \(\sigma\) is assumed to be 0.6 mm in association with pseudo-random corrosion pits based on the measured corrosion data.  
The randomly pitted columns analyzed in the previous section were reintroduced here. Using the equivalent thicknesses obtained from Equations (2)-(4), the numerical models of these columns were reconstructed with a uniformly reduced thickness. Since the average thickness determined by Equation (2) is widely endorsed in current specifications, it was used as a reference in the subsequent analyses for comparison with those obtained from Equations (3) and (4). To facilitate result presentation, the ultimate strength derived from the equivalent thickness was denoted as \(F_{uv}\), while the ultimate strength obtained from random pitting corrosion model was represented as \(F_{u}\). Once the equivalent thickness was determined through the comparative study, its suitability was further confirmed by referencing the current specifications, including the Chinese Standard (GB 50017\(-\)2017) [26] and the European Standard (Eurocode 3) [27].
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 4 Prestressed Concrete Beams with Corroded Strands
### Test Specim  
To observe the behavior of the corroded strands in a prestressed concrete member, four simply supported beam specimens were manufactured and tested. The dimensions and details of the specimens are presented in Figure 15. The post tensioned beams had a section of \(150\times 220\times 2000\) mm with a single seven-wire \(\Phi\)15.2 mm strand.  
Table 2 lists the information on the material and strands' corrosion status for the four manufactured PSC beam specimens. Strands used in the test beams were artificially corroded through exposure to NaCl (to create humid and dry conditions in cycles) for several months, as shown in Figure 15a. The corroded strands had very similar corrosion shape to the actual corrosion shapes observed from existing Bridge B (Figure 2) in the 0-10% range of section loss. Before the corroded strands were introduced to the test beams, section loss was evaluated by Equations (1)-(3). The RB specimen ID refers to the reference member without corrosion. QB refers to a member with corrosion at 1/4 of the span length (450 mm from the support), and MB refers to a member with corrosion in the center of the span length.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Location of** & **Section Loss** & **Section Loss** & **Effective** & \multirow{2}{*}{**Material Properties**} \\  & **Corrosion** & **(Strand)** & **(Most Corrected)** & & & \\  & & & **Wire)** & **Force** & & \\ \hline \hline
**RB** & - & 0\% & 0\% & & & Concrete: 44.07 MPa \\
**QB** & 1/4 & 4.82\% & 21.56\%. & & Yield strength (rebbar): 400 MPa \\
**MB1** & 1/2 & 6.67\% & 22.24\% & & Tensile strength (rebbar): 560 MPa \\
**MB2** & 1/2 & 7.51\% & 18.89\% & & Yield strength (strand): 1630 MPa \\  & & & & Tensile strength (strand): 1865 MPa & Elastic modulus of strand: 194.5 GPa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Beam test specimens.  
Figure 15: (**a**) Test setup and (**b**) detail drawing.  
The average yield, ultimate strength, and elastic modulus of the prestressing steel from the tensile test result were 1630 MPa, 1865 MPa, and 194.5 GPa, respectively. The characteristic yield and ultimate strength of the deformed bars were 400 MPa and 560 MPa, respectively. The concrete was manufactured to have a compressive strength of 40 MPa, and the average compressive strength was 44.07 MPa from concrete cylinder tests when the experiment was conducted. Regardless of corrosion status, the effective prestress force on the strands was equally applied as 160 kN.  
Flexural tests were performed using the displacement control method under a four point bending condition. The span length of the specimen was 1800 mm, and the loading position was 600 mm from a support. Loading was added until the compressed concrete reached a compressive strain of 0.003, even if the strand fractured earlier due to corrosion.
## 4 Results and Discussions
### Timestep Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Experiments** & **Independent Variable** & **Dependent Variable** & **Results** \\ \hline \multirow{3}{*}{7} & Class B: MorningFemale (1) vs. & & & \\  & EveningFemale (0) students with & & Session & Engagement & No significant impact \\  & engagement & & & & \\ \hline \multirow{3}{*}{8} & Class B: MorningFemale (1) vs. & & & \\  & EveningFemale (0) students with & & Session & Non-Engagement & No significant impact \\  & non-engagement & & & & \\ \hline \multirow{3}{*}{9} & All MorningMale (1) vs. EveningMale (0) students with engagement & & Session & Engagement & Male students increase engagement in the morning session \\ \cline{3-7}  & All MorningMale (1) vs. EveningMale & & Session & Non-Engagement & Male students decrease non-engagement in the morning session \\ \hline \multirow{3}{*}{10} & All MorningFemale (1) vs. EveningFemale & & Session & Engagement & No significant impact \\  & (0) students with engagement & & Session & Non-Engagement & No significant impact \\ \hline \multirow{3}{*}{11} & All MorningFemale (1) vs. EveningFemale & & Session & Non-Engagement & No significant impact \\  & (0) students with non-engagement & & Session & Non-Engagement & No significant impact \\ \hline \multirow{3}{*}{12} & All Morning (1) vs. all Evening (0) students with engagement & & Session & Engagement & Engagement increases in the morning \\ \hline \multirow{3}{*}{13} & All Morning(1) vs. all Evening(0) students & & Session & Engagement & Non-engagement \\  & with non-engagement & & Session & Non-Engagement & decreases in the morning \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._We took the session as an independent variable and non-engagement as the dependent variable. By analyzing the Z-value (\(-2.88\)), we concluded that there was a negative impact of a session on the engagement of students with 99% acceptance. Thus, the null hypothesis was rejected because it fell in the acceptance region. There is a significant impact such that morning students have a decrement in non-engagement as compared to evening students. Furthermore, the diagnostic test also confirms that our model fits the collected data, as shown in Table 7.
## 3 Data Processing  
### Data Pre-Processing  
#### 3.1.1 Noise Cancellation: Fluctuation in the Sampling Data  
Eye-tracking data were first checked for fluctuations in the sampling rate which can lead to noise in the eye-tracking data introducing spurious variation into the eye movements [36; 37]. Sampling rate fluctuations were found if the time that passed between two samplings were bigger than the known inter-sample range (8.3 ms, with an error margin of \(\pm\)0.4 ms). Following common practice [37], when fluctuations were detected, two data points before, and two data points after the identified fluctuation were deleted from the dataset. After this filter, a percentage of total data exclusion was calculated.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 1 Introduction
### Proposed Configuration  
The aircraft designed in the project, designated as Wigeon, has a tandem-wing configuration and leading edge open rotors, a capacity of four passengers and one pilot, a range of 400 km, and is fully electric. The transition between horizontal and vertical flight is achieved via the rotation of the wings. The final configuration can be seen in Figure 1.  
The proposed characteristics stem from a market analysis conducted during the project, targeting market entry in 2030 [3]. To achieve this target date, technology-readiness levels expected in 2030 were used in the design process, and certification was taken into account by following the _Special Condition for small-category VTOL aircraft_ by EASA [4], all of which is further detailed in the original project report [3]. The project primarily focuses on the central-European market, where this type of configuration could achieve an 8% market share by 2035, and is also expected to reach a market share of 2% in the North America and Asia-Pacific regions [3]. The market analysis concluded that a 400 km range opens the possibility of connecting big European cities, such as Paris and London, also offering wide-ranging connections between other main European cities and facilitating connections between mainland Europe and the islands surrounding it. Furthermore, the Wigeon has been designed with the objective of easy market introduction, without requiring an extensive infrastructure for its operation, nor extensive certification campaigns for critical systems. As such, the selected power source was that of batteries instead of hydrogen [2] and its size is limited to be operable in existing helipads, for which all horizontal dimensions are constrained to be below 14 m [3].