esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Disciplinary Optimization of UV-C Filter for Air Disinfection
## 4 Optimization Results  
Before proceeding to the optimization process, a first DOE (Design of Experiments) series of designs was evaluated to analyze the significance of each parameter to the main objective of the study, i.e., the absorption index.  
A DOE of 64 designs was evaluated using the Taguchi Orthogonal array scheme, which guarantees uniform coverage of the design space. A statistical analysis based on a PCE (polynomial chaos expansion) regression model was performed, revealing the cumulative sensitivity index (based on SOBOL indices) represented in Figure 9.  
It follows that the height of the filter (z_height in Figure 3) is the parameter with the greatest effect on the absorption index, followed by the inner radius, blade angle, and the coordinates of the points controlling the blade curvature. Blade height, by contrast, revealed no significant effect, so this variable was disregarded in the optimization.  
Figure 10 reports the results of the optimization after the execution of a total of 300 design simulations by the Pilopt algorithm.  
Each point of the chart reports a different design, where the yellow points represent unfeasible solutions (i.e., not respecting one or more of the constraints) and the green points represent feasible solutions. The two objectives of the optimization are reported in the abscissa (mass flow rate) and ordinates (absorption index) of the chart. Note that the mass flow reported on the \(x\)-axis corresponds to a quarter of the real flux in the filter due to the axial symmetry of the model.  
Among the feasible solutions, the one indicated in Table 2 was selected as the optimal compromise among the several criteria. Table 2 reports a comparison of the optimal solution with the baseline.  
Figure 10: MDO results: mass flow vs. absorption index (feasible/unfeasible designs are represented in green/yellow color).  
Figure 9: Effect table of design variables vs. absorption index.
# Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration
## 3 Model Construction and Training Setup
Early stopping monitors the validation loss and halts training when no improvement is observed, preventing overfitting. Additionally, model checkpointing is employed to save the best-performing model during training, ensuring that the final model retains the best performance. **Accuracy:** Accuracy measures the ratio of correctly predicted instances to the total number of predictions. It evaluates how well the model's predictions match the actual outcomes. For heart disease prediction task, accuracy is defined as  
\[\text{Accuracy}=\frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}} \tag{6}\]  
This metric is particularly useful in binary classification tasks like heart disease prediction, where we assess the model's ability to distinguish between positive (patients with heart disease) and negative (patients without heart disease) cases. **Training Loss:** Training loss is calculated using the Binary Cross Entropy (BCE) loss function, which penalizes incorrect predictions. The BCE loss function is commonly used for binary classification problems. The formula for BCE loss is  
\[\text{BCE Loss}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(p_{i})+(1-y_{i})\log(1-p_{ i})] \tag{7}\] \begin{table}
\begin{tabular}{l l} \hline
**Component** & **Details** \\ \hline
**Model Architecture** & TabNet \\ \hline
**Hyperparameters** & Learning Rate: 1 \(\times\) 10\({}^{-3}\), Batch Size: 512, Client Selection \\  & Fraction: \(\rho=1\), Training Steps: \(S=100\) \\ \hline
**Loss Function** & Binary Cross Entropy \\ \hline
**Optimizer** & Adam \\ \hline
**Training Procedure** & Random shuffling, Early stopping, Model checkpointing \\ \hline
**Evaluation Metrics** & Accuracy, Training Loss \\ \hline \end{tabular}
\end{table} 
Table 1: Model architecture and training parameters. where \(y_{i}\) is the true label (0 or 1), \(p_{i}\) is the predicted probability, and \(N\) is the total number of samples. This loss function helps guide the optimization process during training, with the goal of minimizing the error between predicted and true values.
# Intent to Adopt Location Sharing for Logging Safety Applications
## 3 Results
### Additional LS Adoption Elements  
Overall, surveyed loggers believed that all three device types would improve safety on logging operations (Table 8, Section A). Two-way radios with LS had the most support (78.8% indicated \"yes\"), followed by devices that share locations through smartphones (72.1%). Among specific device features, a help button that could send distress calls to coworkers at the jobsite was considered beneficial by the largest number of participants (83.3%) (Table 8, Section B). More than three-quarters of loggers (79.0%) indicated a benefit of help buttons to contact emergency services, although emergency search and rescue receivers were not as valued as other types of devices (Table 8, Sections A-B). Low internal consistency of questions relating to emergency response ([PERSON]'s alpha of 0.37) could imply that loggers value LS devices that offer two-way communication in addition to the ability to contact off-site emergency services. Other features identified by loggers as having the potential to improve workplace safety included alerts when workers enter dangerous areas (76.9%); messaging through text, voice, or video (72.2%); and help buttons to contact supervisors at the office (62.4%) (Table 8, Section B). Loggers supported devices that could share automatic position updates among coworkers (72.7%), but they did not perceive a value in sharing real-time positions with supervisors (only 33.9% of respondents selected \"yes\"). Although participants were generally unconcerned with the privacy implications of sharing real-time location information with others, loggers were less comfortable sharing this type of data with supervisors (32.6% concerned) than with coworkers at the jobsite (only 18.8% concerned) (Table 8, Section C).  
Lastly, when asked to indicate degree of concern related to carrying an extra device, such as a radio or smartphone, while working on logging operations, most respondents felt either \"not at all concerned\" (56.0%) or only \"somewhat concerned\" (34.7%), and only 9.4% indicated they were \"very concerned\" about carrying an extra item (\(n=277\)).
## 3 Simulation Environment and Approaches
### Model in the Loop
#### 3.1.2 Magnetorquer Model
The control configuration block is responsible for making series or parallel reconfigurations as needed. The creation of these models was made possible through the use of Simscape Electrical components. This is because the Simscape Electrical models integrate directly with Simulink main block diagrams and an analysis of their physical behavior is possible using PS converters. The model configuration for the operation of the sensor models based on the previously developed electronic circuits is the fixed step and ode1 be (Backward Euler) solver. The fundamental sampling time in the configuration must be small enough for the correct simulation of the PWM signal that affects the circuit during the driver controller stage; however, this parameter was modified for the other simulations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of turns & 60 \\ Trace width & 0.3 mm \\ Thickness & 20 μm \\ Space between traces & 0.1 mm \\ Magnetorquer resistance & 70 \(\Omega\) \\ Outer length & 80 mm \\ Inner length & 26 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Single magnetorquer parameters.
## 4 Related Work in the ML and DL Domain
### Genetics-Based Diagnosis of ASD
[132] examined ASD-specific gene expression, enabling prospective subsequent detection by predicting ASD risk genes and identifying the temporospatial areas of brain structures at different developmental stages. Employing the BrainSpan atlas, the research analyzed a dataset spanning 13 developmental stages from eight weeks post-conception to eight years over 26 different brain regions. The proposed work utilized the SVM technique to differentiate ASD risk genes from non-ASD genes. It used the transmissible bi-objective combinatorial genetic algorithm for the best feature selection, improving the model's performance. At 13 post-conception weeks, the posteroventral parietal cortex was the most predictive brain area for genes. [PERSON] and [PERSON] [133] explored two datasets: the GE dataset, which contains gene expression data from 30 samples with 43,931 features differentiating ASD and non-ASD cases, and the PBC dataset, from the University of California, Irvine (UCI) that contains 292 samples and 20 features related to ASD diagnosis and personal details. The high dimensionality of the datasets was addressed in the study by optimizing feature selection using a combination of bio-inspired algorithms and machine learning (ML) models to boost classification accuracy. For this goal, we utilized four algorithms: GWO, FPA, BA, and ABC. The models were assessed using AUC, recall, accuracy, precision, and F1-score. Out of all the models suggested the GWO-SVM model had the best performance, with an accuracy of 99.66%. More recently, [PERSON] and [PERSON] [134] created a differential gene expression profile utilizing gene expression data and a gene embedding profile, which incorporated intricate gene connections using the data to determine disease-related genes for ASD. They utilized these profiles and the XGBoost classifier to find new connections for ASD. The proposed method found 10,848 putative gene-gene connections and 125 candidate genes, with the top three possibilities being DNA Topoisomerase I, ATP Synthase F1 Subunit Gamma, and Neuronal Calcium Sensor. They used statistical analysis to assess these candidate genes regarding specific pathways and activities. Additionally, they discovered sub-networks inside the prospective gene network, which aided in identifying association subgroups for putative genes linked to ASD. A summary of the studies related to the eye tracking data is presented in Table 6.  
To summarize, we provide a plot showing the number of works in our review related to each modality. Figure 3 allows to distinguish the most used modalities and then, estimate their contribution to detecting or classifying ASD. Based on these results, we can observe that MRI and questionnaire are the most used modalities for detecting ASD in our study, with 12 and 11 works, respectively.
## 3 Results
### Grain Structure Evolution  
Figure 4 shows the phase fraction, orientation, and inverse pole figure maps (IPF) obtained from the EBSD analysis of the corresponding SEM micrograph shown in Figure 4a. Figure 4b reveals that the matrix consists of near equiaxed FG-Al (green), FG-Al\({}_{3}\)BC (red), and elongated CG-Al\({}_{3}\)BC (red) phases that arrange themselves heterogeneously throughout the microstructure. Such microstructural formation is likely due to ongoing deformation, showing a heterogeneous structure. Here, grain size, smaller than 2 \(\upmu\)m is defined as FG. FG-Al occupies most of the visual field area and becomes the matrix phase, leading to 41%  
Figure 3: Scanning electron microscopy images showing the presence of micron-size Al\({}_{3}\)BC/AlB\({}_{2}\) (dense grey) clusters in a fine grain _fcc_-Al (brighter) matrix in (**a**) low magnification and (**b**) high magnification. (**c**) Energy-dispersive spectroscopy elemental distribution maps of B, graphene (C), and Al, showing distribution of the phases, with some presence of O.  
of the overall composition being FG-Al, with around 19% FG/CG-Al\({}_{3}\)BC phases being present within the FG-Al matrix. Additionally, the darker regions, along with the red areas in the map, likely correspond to the unindexed AlB\({}_{2}\) phase. Such heterogeneous microstructures indicates the possible dynamic recrystallization, which usually happens during thermal processing processes [45; 46; 47]. Comparing the orientation map with the phase map, the recrystallized _fc_-Al (FG-Al) occupying the FG regions, and the CG regions occupied by the deformed Al\({}_{3}\)BC phase can be observed.  
According to the IPF image, the orientation of FG-Al (green), FG-Al\({}_{3}\)BC (red), and elongated CG-Al\({}_{3}\)BC (red) phases is random and the misorientation between them is high-angle. Similar phenomena were observed in Al\({}_{3}\)BC/6061 Al composites, where a dynamic recrystallization-induced fine-grain structure was observed in the Al matrix, which is likely due to dislocation accumulation via plastic deformation by extrusion [18; 19]. Therefore, the present results suggest that hot pressing significantly initiates dislocation accumulation and local plastic deformation (SGB deformation) in _fc_-Al grains. Further deformation refines the Al grains into near-equivxed FG-Al grains and brittle agglomerated Al\({}_{3}\)BC reinforceents into FG-Al\({}_{3}\)BC within the FG-Al matrix, thereby leading to a heterogeneous structure within the composite. Interestingly, during thermal processing, such heterogeneous hard FG-Al\({}_{3}\)BC and FG-Al arrangements are likely to limit/hinder migration of grain boundaries and promote mechanical incompatibility at regional boundaries between FG-FGs and FG-CGs, resulting in a strain gradient.
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.7 From Evaluation Methodology to Structured Results  
The above outlines the main steps and methods for the performance and validity evaluation of the developed artificial neural network algorithms; it also informs the structure of the investigation and results of this study. Instead of exploring methods of algorithm optimisation, the research interest, in this case, focuses on the impact of quality and quantity of the provided input data. As such, a single algorithm was developed, adhering to the requirements of the \"Architecture and Structure\" section above. Then, the same algorithm was trained with varying numbers of input data (as presented in Section 2.4 Test Cases) and was thereon treated as 4 separate predictive models. Each model was subsequently evaluated, using the aforementioned indices and metrics, to reach a conclusion regarding the most effective regressor. As a final step, the dominant model was tested and evaluated again, against completely unknown data combinations. A brief outline of the work sequence followed so far and leading to the following results would include:* An initial review of the existing bibliography proposing a protocol for the development of ANNs.
* The development of one ANN algorithm based on the peer-reviewed protocol.
* The training of the ANN algorithm with varying degrees of input data (ending up with 4 regressors of the same architecture but different levels of input data).
* A comparison of the performance of the 4 regressor models (same architecture/different input data) and an evaluation of the impact of the quality of offered input data on each model's predictive capability.
* The identification of the best-performing ANN and validation against a completely new set of data.
* An outline of observations, conclusions, and recommendations regarding the impact of input data quality and ways of mitigating the problem.
## 3 The Establishment and Analysis of BBD Experimental Design and Prediction Model
### BBD Experimental Design and Grey Relation Analysis
#### 3.1.2 Grey Relational Analysis
The objective weights of each index can be calculated using information entropy [29], and the calculation process is as follows:  
\[P_{ik}=\frac{\xi_{i}}{\sum\limits_{i=1}^{m}\xi_{i}} \tag{11}\]  
\[e_{k}=-\frac{1}{\text{ln}(m)}\times\sum\limits_{i=1}^{m}(P_{ik}\text{ln}\,P_{ ik}) \tag{12}\]  
\[D_{k}=1-e_{k} \tag{13}\]  
\[\omega_{k}=\frac{D_{k}}{\sum\limits_{k=1}^{n}D_{k}} \tag{14}\]  
where \(P_{ik}\) is the coefficient reflecting the k-th evaluation factor's significance for the i-th experimental trial. \(e_{k}\) denotes the information entropy of index k, \(D_{k}\) denotes the information redundancy of index k, and \(\omega_{k}\) represents the weight of index k. Table 5 displays the resulting data. \begin{table}
\begin{tabular}{c c c c} \hline
**The Comparison Sequence** & **F** & **T** & **V** \\ \hline
1 & 0.586445367 & 0.27777778 & 0.571428571 \\
2 & 0.096818811 & 0 & 0.494897959 \\
3 & 0.914246196 & 0.774509804 & 0.576530612 \\
4 & 0.53386584 & 0.64379085 & 0.658163265 \\
5 & 1 & 1 & 0.178571429 \\
6 & 0.885200553 & 0.385620915 & 0.589285714 \\
7 & 0.622406639 & 0.826797386 & 0.744897959 \\
8 & 0.268326418 & 0.735294118 & 0 \\
9 & 0.83264177 & 0.225490196 & 0.803571429 \\
10 & 0.994467497 & 0.715686275 & 0.655612245 \\
11 & 0 & 0.183006536 & 0.747448498 \\
12 & 0.484094053 & 0.816993464 & \\
13 & 0.56846473 & 0.493464052 & 0.619897959 \\
14 & 0.576763485 & 0.418300654 & 0.571428571 \\
15 & 0.560165975 & 0.385620915 & 0.589285714 \\ The Reference Sequence & 1.0000 & 1.0000 & 1.0000 \\ \hline \end{tabular}
\end{table} 
Table 4: Normalized data. Calculation of Grey Relation Degree  
The GRD is the weighted sum of the GCC, and the calculation formula is as follows:  
\[\gamma(x_{0}^{*},x_{i}^{*})=\sum_{k=1}^{n}\omega_{k}\xi(x_{0}^{*}(k),x_{i}^{*}(k)) \tag{15}\]  
where \(\gamma(x_{0}^{*},x_{i}^{*})\) is the grey relational degree, \(n\) is the total count of metrics, and \(n=3\). The results of the grey correlation coefficient and grey relation degree are shown in Table 6.
## 3 Results and Discussion
### Pore Structure of PolyHIPE Membrane
#### 3.2.2 Tortuosity  
Tortuosity (\(\tau\)) accounts for the increase in the distance of a diffusing ion due to bending and branching of pores, which affects the effective diffusivity of ions in the PolyHIPE membrane. The tortuosity is usually defined as the ratio of the actual path length through the pores to the Euclidean distance [25]:  
\[\tau=\frac{L_{actual}}{L_{Euclidean}} \tag{3}\]  
where \(L_{actual}\) is the actual path length through the pores, and \(L_{Euclidean}\) is the shortest distance between the start and end points in Euclidean space. The tortuosity of porous material can be estimated based on an SEM image analysis process [26; 27]. In this study, the values of \(L_{actual}\) and \(L_{Euclidean}\) of PolyHIPE membrane was determined from the SEM images (Figure 3a), of which the images can be processed by using both Adobe Photoshop CS5 and the built-in SEM program with several binarization parameter settings to improve the accuracy and eliminate uncertainties of path length determination (Figure 3b). However, the SEM images show partly the inner structure of the samples [28]. In order to obtain acceptable tortuosity measurement, the SEM image was taken from various sections of PolyHIPE membrane, and by taking five visually identifiable routes of each binarized image the tortuosity can be estimated (Figure 3b). The measured tortuosity of PolyHIPE is within the range of 1.08-1.21.  
Alternatively, \(\tau\) can be also estimated by a number of theoretical models from porosity [29]. For a porosity \(\Phi\) higher than 0.5, the tortuosity can be estimated by using the equation \(\tau\) = 0.8(1 \(-\)\(\Phi\)) + 1 [30]. In this study, since the porosity of PolyHIPE membrane is about 0.77, the calculated tortuosity is equal to 1.18, which lies within the range of tortuosity estimated by SEM image analysis. The low tortuosity is apparently due to the high porosity of PolyHIPE.
## 3 Results and Discussion
### Effect of Humidity on Marine Oil Ignition in Engine Room
In this research, the heated-surface ignition time of marine diesel oil was measured experimentally, and the data collected by thermocouples and images captured by a high-speed camera were used to determine the ignition delay time. The temperature distribution and time variation of the marine oil vapor above the heated surface were obtained by thermocouple data collection. The images of the ignition moment and evolution process of the initial flame were collected with millisecond accuracy by image acquisition with a high-speed camera. Figure 9 shows the change rule of the ignition delay time of the leaking marine oil above the heated surface when the humidity of the fishing vessel's engine room changes. It is found that the increase in humidity in the engine room has a significant impact on the ignition delay time of the marine oil on the heated surface. Taking the heated-surface temperature of 465 \({}^{\circ}\)C as an example, when the humidity is 60%, the ignition delay time of the leaking oil is approximately 3.36 s. When the humidity increases to 75% and 85%, the ignition delay times of the marine oil on the heated surface go up to 7.05 s and 8.49 s, which are nearly twice as high. This change rule is particularly obvious when the heated-surface temperature is relatively low, and the increase rate decreases gradually when the heated-surface temperature rises. When the typical hot-surface temperature rises to 505 \({}^{\circ}\)C, the ignition delay times of marine oil under different humidity conditions are 0.84 s, 2.10 s, and 3.57 s, respectively. The water in the fishing vessel's engine room absorbs environmental heat and converts it into vapor, and the leaked marine oil above the heated surface absorbs the environmental heat and converts it into flammable vapor. Since the specific heat capacity of seawater is higher than that of marine oil, the relevant temperature rises due to absorbing the same heat is lower than that of marine oil. Therefore, in the case of absorbing the same heat, the seawater and engine room environment have a more obvious temperature difference. This results in seawater absorbing more heat than marine oil during convective heat transfer. In the case of the same heat transferred by the heated surface in the engine room, the thermal feedback of the engine room is inhibited to a certain extent with the increase in humidity. As a result, the ignition delay time of leaking marine oil on the heated surface is increased. According to the analysis of the experimental and numerical results, the change in the ignition delay time on the heated surface after the marine oil leakage is predicted by the change in the heated-surface temperature, which has an important role in preventing a fire accident in the fishing vessel's engine room. Figure 8: Heated-surface ignition occurrence of marine oil with different humidity. Figure 9: Ignition delay time of marine oil with elevated humidity in fishing vessel’s engine room.