esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Damage Level of Slab-Column Joints under Blast Load
## 1 Introduction  
Recently, a number of cases have been reported in which structures are seriously damaged by explosion loads caused by terrorism or accidents. It is necessary to protect citizens' property and lives through blast-resistant design of structures against such extreme events.  
The explosion load is characterized by very high pressure in a very short time. In addition to the peculiarity of the load, the inhomogeneity of the material of concrete causes difficulties in the blast-resistant design of reinforced concrete (RC) structures [1; 2; 3]. For this reason, the blast-resistant design of RC structures has generally been overly conservative, mainly by increasing the thickness of the members or by reinforcing bars excessively. Therefore, reliable blast-resistant performance evaluation and prediction methods are required for the rational blast-resistant design of RC structures.  
While many studies have been conducted on the blast-resistant behavior of single members, such as columns and beams, sufficient studies have not been conducted in the case of joints composed of two or more single members [4; 5; 6]. However, the joint may cause the collapse of the entire structural system upon its failure, so more attention is required in design [7; 8]. Therefore, this study focused on the slab-column joints, which is one of the common types of joints.  
Currently, support rotation is the only evaluation factor for the blast-resistant performance of RC members in various design standards, including American Society of Civil Engineers(ASCE) and Department of Defense (DoD) [9; 10]. Moreover, no specific guide is provided for the blast-resistant design of joints connecting structural members. According to the previous study, it was confirmed that the support rotation alone was not sufficient to evaluate the blast-resistant performance of the joint [11]. In some cases, even though the support rotation was within the limits of the design criterion, severe fractures in the joint region were observed. As such, the need for additional evaluation factorsfor blast-resistant performance was confirmed, but no specific solution has been proposed so far. Therefore, this study analyzed the behavior of slab-column joints under blast loads from various viewpoints as well as displacement and support rotation. As a result, it was found that effective fracture volume could be used as a new blast-resistant performance evaluation factor. The effective fracture volume is the total volume of the joint concrete destroyed by the blast load, excluding the concrete cover. In addition, a model for predicting the damage level of the slab-column joints according to the amount and location of explosives was proposed using the effective fracture volume. This could be used not only for the blast-resistant design of new structures but also for determining the level of blast-resistant performance of existing structures.
## 2 Materials and Methods
### Multilayer Perceptron Model  
Numerous artificial neural network models have been developed for a wide range of applications across various disciplines [34; 35; 36; 37]. Among these, the multilayer feed-forward artificial neural network (MLP) is one of the most commonly utilized models and was selected for use in our study [38; 39; 40; 41]. The selection of MLP in this study is motivated by its structural simplicity and high interpretability. Given the smaller dataset size, MLP presents a lower risk of overfitting compared to more complex models and has demonstrated dependable performance across numerous studies. Additionally, compared to more complex ML models, MLP is easier to train and interpret, making it well-suited for the objectives and dataset size of this study. All MLP models were implemented using Python (version 3.8.0). Before being input into the networks, the data were normalized using the min-max method [38; 39]. The training of the MLP was conducted through supervised learning, where a sequence of input and output variables from the training dataset was provided [40; 41]. Through iterative adjustments of the connection weights, an optimal input-output mapping function was established [36]. Throughout the training process, careful consideration was given to several factors, including the choice of optimization algorithm, the configuration of model parameters, and the determination of the maximum number of training iterations [35; 36; 37]. The neural network architecture and connection parameters were fine-tuned until the model's loss function was stabilized, thus achieving optimal fitting performance [38; 39]. Following successful training, the model's generalization ability was evaluated using an external testing dataset [34; 35; 36; 37; 38; 39; 40; 41].  
The input features for the MLP models were extracted from the clinical data obtained from patients' medical histories and were linked to the target output variables, which included gender, smoking status, alcohol use, voice usage, coffee consumption, comorbidities, surgical history, and the status of voice training. The chosen features were utilized to develop the MLP model using the training dataset. Each neuron's activation function was set to the hyperbolic tangent function, while the softmax function was used at the output layer. The learning process of the model was driven by the adaptive momentum (Adam) optimization algorithm, with cross-entropy serving as the loss function. The model's performance was assessed through a five-fold cross-validation method on the training dataset, which allowed for the identification of the optimal number of hidden layer units and the maximum iteration limit. To reduce the risk of overfitting, a regularization coefficient of 0.001 was implemented. After the evaluation phase, the final MLP model was trained on the complete training dataset using the optimal parameters determined during cross-validation.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 2 Model Proposed
### Use of GA and A* for Solving the Travel Salesman Problem
#### 2.5.2 Search with A*  
For path finding, the popular Djikstra algorithm determines all possible nodes, taking into account their respective costs. Once the paths have been visited, the algorithm finds the various paths to the solution and determines the best by comparing their costs [19]. It is the most popular algorithm due to its efficiency in pathfinding.  
A* is one popular search algorithm, proposed in 1968 by [PERSON] et al. [20]. It is used to find the best path between two points, minimizing the cost of the path. Because of its good performance and low computational cost, it is popular for optimizing paths, for example in robotics [21]. It differs from the Djikstra algorithm in that it uses an heuristic to estimate the cost of the path that needs to be traversed to reach the destination. If the heuristic is good, A* has a good performance. If the heuristic is bad, the performance may be compromised.  
A* is a complete algorithm, which means that if there is a solution it will always find that solution at some point of the search tree. It is also optimal, which means it always finds the best solution possible. It is an informed search method, which relies on an heuristic to make a decision. The heuristic is an estimate of the distance between the current node and the target point. In the present research, the heuristic is the straight line distance between the point of the map where the user is and the next target point to be visited.  
The algorithm works trying to minimize the sum of the distances already traveled to node \(n\), which is usually called \(g(n)\), plus the distance still expected to travel to the goal, given by the heuristic \(h(n)\), as shown in Equation (1). In the equation, \(f(n)\) is the function that needs to be minimized.  
\[f(n)=g(n)+h(n) \tag{1}\]  
For each candidate path, A* creates a search tree from the starting node to the goal node. Then for each node \(n\) of the path, it calculates \(f\). The best path is the path with the lowest \(f\).
## 3 Results and Discussion
### Sensitivity and Linearity
[14], where the linear operation range of a fluxgate sensor had no relation with the excitation conditions; it was, however, directly affected by magnetic core materials due to the independence of the excitation and detection mechanisms. Figure 4: (**a**) The sensor response for different levels of excitation frequency at an excitation current of 70 mA; (**b**) The sensitivity of the sensor as a function of the magnitude of the excitation current.
## 1 Introduction
The developed design of the OGFDM with the multi-carrier system involves five important levels of processing which are known as the acceleration level, filtration level, oversampling level, allocation level, and modulation level. Regarding the acceleration level, the upgraded design of the OGFDM is extra accelerated to provide a wider BW usage and better transmission rate than the initial OGFDM. Concerning thefiltration level, the advanced Hilbert filters [19] are applied proficiently on the multi-carrier system ensuring an orthogonal transmission with the upgraded design of the OGFDM. As regards to the oversampling level, the flexible oversampling process [20] is adopted to accommodate any probable interference among the increased filters, that in turns can improve the attained BW efficiency. Relating to the allocation level, a dual number of frequency subcarriers are utilized to extend the scalability of the mobile network. About the modulation level, the adaptive modulation scheme [21] is widely utilized with the progressive multi-carrier OGFDM system to achieve an extra enhancement for the capacity of the transmission channel. Hence, depending on the transmission conditions, the frequency subcarrier with the resilient modulation scheme can be reused in a more efficient way than the fixed modulation system [22]. The performance in terms of the channel capacity and the BER of the promoted OGFDM waveform is fundamentally deliberated in the PHY of an electrical back-to-back wireless transmission system. The rest of the paper is organized as follows: Section 2 discusses the key concepts of the proposed system physically and mathematically. Section 3 evaluates experimentally the system performance utilizing a MATLAB simulation. Section 4 summarizes the outlines of the paper.
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix B State-of-the-Art Anomaly Detection Models
### Density-Based Models
#### b.1.2 K-Nearest Neighbours (KNN)  
Usually used for classification purposes, KNN [41] is a supervised algorithm that can also be used for anomaly detection. For each observation, it selects its closest \(K\) observations, generally in terms of distance but other similarity metrics can be considered. The anomaly score of an observation is computed as the function of its distances to the \(K\)-nearest neighbours, which is the weighted average of the distances. The points with the highest anomaly scores are considered anomalies.
###### Abstract  
Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) (x = 0.5, 0.6, 0.7) nanoparticles were synthesized by a thermal decomposition method. The synthesized particles were identified as pure spinel ferrite structures by X-ray diffraction analysis, and they were calculated to be 46-51 nm in diameter by the Scherrer equation, depending on the composition. In the FE-SEM image, the ferrite nanoparticles have spherical shapes with slight agglomeration, and the particle size is about 50 nm, which was consistent with the value obtained by the Scherrer equation. The lattice parameter of the ferrite nanoparticles monotonically increased from 8.34 to 8.358 A as the Zn concentration increased from 0.5 to 0.7. Initially, the saturation magnetization value slowly decreases from 81.44 to 83.97 emu/g, then quickly decreases to 71.84 emu/g as the zinc content increases from x = 0.5, through 0.6, to 0.7. Ni\({}_{1-x}\)Zn\({}_{x}\)Fe\({}_{2}\)O\({}_{4}\) toroidal samples were prepared by sintering ferrite nanoparticles at 1250 \({}^{\circ}\)C and exhibited faceted grain morphologies in the FE-SEM images with their grain sizes being around 5 \(\upmu\)m regardless of the Zinc content. The real magnetic permeability (\(\upmu^{\prime}\)) of the toroidal samples measured at 5 MHz was monotonically increased from 106, through 150, to 217 with increasing the Zinc content from x = 0.5, through 0.6, to 0.7. The cutoff frequency of the ferrite toroidal samples was estimated to be about 20 MHz from the broad maximum point in the plot of imaginary magnetic permeability (\(\upmu^{\prime\prime}\)) vs. frequencies, which seemed to be associated with domain wall resonance.  
superparamagnetic; spinel ferrite; NiZn ferrite; nanoparticles +
Footnote †: journal: Applied Sciences
# See-Through Near-Eye Display with Built-in Prescription and Two-Dimensional Exit Pupil Expansion
## 2 Design Rules
### Exit Pupil Expansion  
Instead of duplicating the exit pupil into many, our EPE leveraged the oblique intersection of the viewing cone. As shown in Figure 6, the plane of the ISG intersected the viewing cone at angle \(\alpha\), thereby forming an elliptical exit pupil, which had a vertical length \(A_{i}\) that can be written as  
\[A_{i}=A_{p}s\text{\it{in}}+\frac{A_{p}cos\alpha}{\text{\it{tan}}\Big{(}\frac{ FOV}{2}+\alpha\Big{)}} \tag{8}\]  
and a horizontal length that is defined by the width \(W_{i}\) of the ISG. Say \(A_{p}\) = 6 mm and \(\alpha\) = 14.48\({}^{\circ}\), \(A_{i}\) = 13.25 mm. Let \(\beta\) symbolize the angle of beam incident to the OSG, as shown in Figure 7, where only the axial rays are depicted. The horizontal length of the elliptical exit pupil will be elongated to \(A_{o}\)  
\[A_{o}=\frac{W_{i}}{cos\beta} \tag{9}\]  
For \(W_{i}\) = 3.8 mm and \(\beta\) = 75.52\({}^{\circ}\), \(A_{o}\) = 15.2 mm. As a result, the ISG is of \(3.8\times 13.25\) mm\({}^{2}\) and the OSG of \(15.2\times 13.25\) mm\({}^{2}\). In case of misalignment, their actual sizes were supp  
Figure 5: Decomposition of multiplexed grating into three sub-gratings. Say the periods of sub-gratings are \(p_{A}\), \(p_{B}\), and \(p_{C}\), respectively, then the collective period \(P_{m}\) of the multiplexed grating shall be the least common multiple of the above three.  
Figure 6: Illustration of the vertical expansion of the exit pupil by the in-coupling slanted grating (ISG). \(\alpha\) is the angle between the optical axis of viewing cone and the plane of the ISG, \(A_{p}\) the aperture of the projection lens, \(A_{i}\) the vertical length of intersected exit pupil.  
beam incident to the OSG is perpendicular to the bevel or the ISG, then \(\beta\) is equal to the bevel angle \(\gamma\), which affects the thickness of the plano-concave lens. Incidentally, for the off-axis rays, problems such as pupil mismatch, vignetting, etc. merit special care.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Iron-Based NPs
#### 4.3.4 Genotoxicity  
[PERSON] et al. [227] investigated the impact of FeNPs with different surface chemistries on the uptake, root morphology, DNA damage, oxidative stress, and cell death in _Alium cepa_ roots after 24 h exposure. It was shown that FeNPs with low zeta potential, conductivity, and high polydispersity index, which were adsorbed on root surfaces, damaged the root tip, epidermal, and root hairs due to their colloidal destabilization and smaller size. High uptake of NPs was responsible for considerable DNA damage, chromosome/nuclear aberrations, and micronuclelei formation. On the other hand, FeNPs, which were characterized with higher dissolution and substantial uptake, induced genotoxicity. Both types of FeNPs generated strong oxidation stress and in treated _A. cepa_ roots ROS-mediated apoptotic and necrotic cell death was observed.  
Elicitation of the production of hyoscyamine and scopolamine in HR of _Hyoscyamus reticulatus_ L. by Fe\({}_{2}\)O\({}_{3}\) NPs allows achieving ca. 5-fold higher amounts than in the con trol. This is a promising method that can be considered to produce tropane alkaloids [65] (Table 2). Moreover, 5.4- and 12-fold higher amounts of hypericin and hyperforin, compared to the control observed in cell suspension culture of _Hypericum perforatum_ L., using 100 mg/L Fe\({}_{2}\)O\({}_{3}\) NPs as an elicitor, suggest this method is very encouraging [141] (Table 1).
## 4 Discussion
### Stiffness Degradation  
To estimate the stiffness degradation in a specimen during the entire loading process, the stiffness degradation coefficient (\(K_{j}\)) is introduced as follows:  
\[K_{j}=\frac{\sum_{i=1}^{n}P_{j}^{i}}{\sum_{i=1}^{n}u_{j}^{i}} \tag{7}\]  
where \(P_{j}^{i}\) and \(u_{j}^{i}\) are the peak point load and its corresponding displacement in the \(i\)th loading cycle at the \(j\)th drift level, respectively, and \(n\) is the number of cycles.  
Figure 20 shows the curves of the stiffness degradation coefficient (\(K_{j}\)) of the specimens versus the drift ratio. Greater end-plate thickness and the use of stiffeners significantly increased the joint stiffness and decreased the rate of stiffness degradation. The thicker steel tube wall resulted in greater stiffness but slowed the stiffness degradation with an insignificant magnitude. The use of a weak beam decreased the joint stiffness and increased the rate of stiffness degradation.  
Figure 19: Strength degradation coefficients of the entire loading process. (**a**) Specimens SJ-1 through SJ-5; (**b**) Specimens SJ-3, SJ-4, SJ-6 through SJ-8.  
Figure 20: Stiffness degradation coefficients: (**a**,**c**) Positive direction and (**b**,**d**) Negative direction.
###### Abstract  
In addition to natural electromagnetic fields (EMFs), so-called artificial electromagnetic fields exist in the biosphere, with mobile communications being one of their main sources. This study aimed to determine the impact of EMF at a frequency of 1800 MHz on the concentrations of selected hormones, sperm motility, viability, morphology and behaviors in laboratory rats. We used 28 rats divided into two equinumerous groups: control (\(n=14\)) and experimental (\(n=14\)). The rats in the experimental group were exposed to EMF for 12 weeks (for 10 min, four times daily); at the same time, the control specimens were kept in standard conditions. After 12 weeks, half of each group was killed, while the other half was maintained for another 4 weeks with no EMF emission. Elevated corticosterone levels and decreased thyroid-stimulating hormone levels were observed in the experimental specimens, which persisted for 2 weeks after the cessation of EMF emission. Exposure to EMF also resulted in decreased sperm motility and viability, as well as increased rat anxiety. This study shows that exposure to EMF (1800 MHz) may affect the endocrine status of the body and the behavior and reproductive functions of animals. However, hormonal disorders appear to be reversible.  
electromagnetic fields; hormones; behavior; sperm; rats
## 4 Applications
### Drug Delivery  
MWCNTs (functionalized or not) have been exploited in the drug delivery, biochemistry, and medicine fields. [PERSON] et al. [13] described the effect of different levels of the carboxylation of MWCNTs on the dissolution rate of sulfamethoxazole and griseofulvin, two therapeutic hydrophobic drugs used as antibiotic and antifungal agents, respectively. The anti-solvent synthesis of micron-scale drug particles was the applied technique [13]. The reached data suggested that the degree of functionalization may help to control the release of the drugs, as decreasing the C:COOH ratio in the functionalized MWCNTs promoted a significant increase in the dissolution rates [13].
## 4 Classification of Lung CT Images
### AlexNet Architecture for Classification of Lung CT Images  
[PERSON] et al. won ILSVRC-2012 (ImageNet Large Scale Visual Recognition challenge) by proposing the AlexNet architecture for the first time [39]. AlexNet contains five convolutional layers, five pooling layers and three fully-connected layers.  
Since in original AlexNet architecture, 2D images are used, in this study, in order to classify a 3D CT scan dataset, 3D-AlexNet architecture is implemented. For non-linearity, the Relu activation function is applied to the output of each convolutional layer and fully connected layers. Softmax classifier is used as AlexNet's default classifier as well. Moreover, in this study, batch size and the learning rate of the network are determined to be 32 and 0.01, respectively, and it is trained in 30 epochs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Type** & **Number of Kernels** & **Kernel Size** & **Output Size** \\ \hline Convolutional & 96 & \(3\times 3\times 3\) & \(96\times 114\times 114\times 114\) \\ Max pooling & & \(2\times 2\times 2\) & \(96\times 113\times 113\times 113\) \\ Convolutional & 128 & \(3\times 3\times 3\) & \(128\times 111\times 111\times 111\) \\ Convolutional & 256 & \(3\times 3\times 3\) & \(256\times 109\times 109\times 109\) \\ Max pooling & & \(2\times 2\times 2\) & \(256\times 54\times 54\times 54\) \\ Convolutional & 324 & \(3\times 3\times 3\) & \(324\times 51\times 51\times 51\) \\ Convolutional & 324 & \(3\times 3\times 3\) & \(324\times 49\times 49\times 49\) \\ Max pooling & & \(2\times 2\times 2\) & \(324\times 24\times 24\) \\ Convolutional & 512 & \(3\times 3\times 3\) & \(512\times 12\times 12\times 12\) \\ Max pooling & & \(2\times 2\times 2\) & \(512\times 6\times 6\times 6\) \\ Fully connected & & & \(1000\times 1\times 1\times 1\) \\ Fully connected & & & \(1000\times 1\times 1\times 1\) \\ Softmax/RBF-based SVM & & & \(2\times 1\times 1\times 1\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of two proposed 3D-CNN architectures in the classification of lung CT scan images.  
Figure 4: Architecture of the two proposed 3D-CNN.  
Additionally, a summary of the layers and output size of lung images through training by 3D-AlexNet architecture is given in Table 2.
## 5 Performance Evaluation
### Dataset Description  
We utilized San Francisco's dataset [27; 28] which contains around five millions records of the measured parking availability covering a total of 420 parking segments in San Francisco. Parking availability observations were recorded at approximately 5-min intervals for each parking segment during 6 weeks.  
Each line of the dataset keeps the situation of a parking segment at a specific timestamp. We consider the columns correspond to the following content:  
* timestamp: timestamp reporting when the SFPark API was polled, rounded to the closest minute;
* segmentid: ID of the parking segment;
* capacity: total number of parking places in the segment (the capacity may vary over time, due to parking restrictions);
* occupied: current number of occupied places in the segment.  
In addition, there is a complementary file where each line of that corresponds to a parking segment and its columns correspond to the following content:  
* segmentid: ID of the parking segments;
* streetname: name of the related street;
* startx, starty, endx, endy: WGS84 coordinates of start and end point of parking segment.