esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Seismic Performance of Composite Building Structures
### Seismic Performance of Composite Buildings: State-of-the-Art
SeismoStruct software ([[https://seismosoft.com/products/seismostruct/](https://seismosoft.com/products/seismostruct/)]([https://seismosoft.com/products/seismostruct/](https://seismosoft.com/products/seismostruct/))) was used for design and performance assessment, utilizing nonlinear static pushover and incremental dynamic analyses. They analyzed the effects of ductility and the number of stories on the seismic performance of the buildings, finding that all structures had behavior factors higher than the design assumptions, particularly those in the medium ductility class. [PERSON] (2016) [111] proposed a simplified approach for analyzing and evaluating the performance of steel and concrete composite frames under extreme loads, such as earthquakes. The study focused on a four-story two-bay underground composite frame. The nonlinear behavior of composite members was characterized to establish a macro-model for the frame. The pushover method was applied to determine the lateral force VS. top horizontal displacement. A method for identifying damage in composite frames was proposed, and the damage evolution of the case study frame was analyzed. The failure mode was attributed to substantial damage in the bottom CFST columns, and the seismic performance of the frame with high-strength steel was compared to that with ordinary strength steel, showing improved lateral resistance and elasticity. Furthermore, the test results of two steel-concrete composite frame specimens with encased CFST columns subjected to cyclic loading were analyzed by [PERSON] et al. (2017) [112] using fiber beam-column elements. Parameters, such as column slenderness ratio, axial compressive ratio, and reinforcement ratio, were studied. Hysteretic models for composite frames were proposed based on parameter analysis, showing favorable energy-dissipating capacity. [PERSON] et al. (2015) [113] tested a family of 96 regular plane CFT-MRFs using different ground motions to create a response databank. Using regression analysis, they derived simple formulas to estimate the strength reduction factor, ductility demands, drift, and seismic displacements. Several factors, such as the number of stories, stiffness ratio, strength ratio, deformation, and material strengths, were studied. The formulas can be used in seismic design methods and allow for the assessment of existing structures as well as for the design of new ones based on deformation.
## 2 Materials and Methods
### Backpropagation Neural Network  
The BP (Back Propagation) neural network employs the gradient descent method for error backpropagation, adjusting weights and thresholds based on the error to achieve a desired level of accuracy. During forward propagation, signals travel through the neurons. We denote the input layer neurons as \(\mathrm{X_{i}}\), the hidden layer neurons as \(\mathrm{H_{j}}\), the activation function as f(x), the weights as \(\mathrm{u\ij}\), and the thresholds as \(\mathrm{b_{j}}\). The actual and simulated values are represented as \(\mathrm{Y_{n}}\) and \(\mathrm{y_{n}}\), respectively, while E(\(\omega\),b) is the error function with respect to the variables \(\omega\) (weights) and b (thresholds).  
\[H_{j}=f(\sum\omega_{ij}x_{i}+b_{j}) \tag{4}\]  
\[E(\omega,b)=\sqrt{\frac{\left(\gamma n-yn\right)^{2}}{n}} \tag{5}\]  
\[\omega_{ij}=-\eta\frac{\partial E(\omega,b)}{\partial_{\omega}} \tag{6}\]  
To evaluate the performance of a predictive model, three commonly used performance indicators are the Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and the coefficient of determination (R\({}^{2}\)). A lower MAPE and RMSE, or an R\({}^{2}\) closer to 1, indicate a better predictive performance for the model. The formulas used to calculate these performance metrics for prediction results are as follows:  
Figure 5: PSO Flowchart.  
\[E_{MAP}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{|Y_{i}-y_{i}|}{Y_{i}}\times 100\% \tag{7}\]  
\[E_{RMS}=\sum\limits_{i=1}^{n}\sqrt{\frac{\left(Y_{i}-y_{i}\right)^{2}}{n}} \tag{8}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(Y_{i}-y_{i}\right)^{2}}{\sum\limits_{ i=1}^{n}\left(Y_{i}-y_{i}\right)^{2}} \tag{9}\]  
In the formula, \(n\) denotes the number of data points; Y\({}_{i}\) represents the actual value; y\({}_{i}\) represents the predicted value; and Y\({}^{-}\) (or sometimes denoted as uY or simply mean (Y)) denotes the average of the actual values.
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
## 2 Background
### Effect of Laser Pulse Width
When the pulse width increases from femtoseconds to picoseconds, the energy transfer tends to resolve the energy imbalance in the nanoparticles. In this case, the effect of Coulomb explosions in the particles decreases [45]. Near-field enhancement at the nanoparticle surface also occurs using ultrafast laser pulses [46]. When a nanostructure is irradiated with a short laser pulse, the electromagnetic field is amplified at the point at which an abrupt change in shape occurs. This results in a sudden energy concentration at that point, leading to particle fragmentation [47]. A model with constant electron and phonon temperatures can be used for lasers with nanosecond pulse widths. Also, because there is enough time for thermal diffusion within the particle, the heat at the interface interacts with the surroundings during laser irradiation. Therefore, the photothermal effect becomes dominant [20; 48; 49]. Melting, evaporation, or both occur at the particle surface as a result of the temperature imbalance between the particle surface and interior, which likely also causes particle fragmentation. The three above-mentioned mechanisms of laser fragmentation are summarized in Figure 1. The effect on nanoparticle formation of mechanical shockwaves generated by laser-induced generation of plasma is shown in Figure 2.
# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
###### Abstract  
The accuracy of satellite positioning results depends on the number of available satellites in the sky. In complex environments such as urban canyons, the effectiveness of satellite positioning is often compromised. To enhance the positioning accuracy of low-cost sensors, this paper combines the visual odometer data output by Xtion with the GNSS/IMU integrated positioning data output by the satellite receiver and MEMS IMU both in the mobile phone through adaptive Kalman filtering to improve positioning accuracy. Studies conducted in different experimental scenarios have found that in unobstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 50.4% compared to satellite positioning and by 24.4% compared to GNSS/IMU integrated positioning. In obstructed environments, the RMSE of GNSS/IMU/visual fusion positioning accuracy improves by 57.8% compared to satellite positioning and by 36.8% compared to GNSS/IMU integrated positioning.  
GNSS; IMU; visual; adaptive filtering; position +
Footnote †: journal: applied sciences
## 3 Results
### Evaluation  
To obtain initial feedback from users, two questionnaires were handed out after short individual playtesting sessions with all participants.  
In the context of the SUS evaluation, there was a wide range of perceptions. The SUS scores ranged from 50 to 85 (see Table 5), with an average of 69, which is considered satisfactory but not good or excellent. The UES delivered preliminary results with averages ranging from 3.97 to 4.77 across all dimensions (See Table 6). The average of the Focus Attention (FA) dimension was 4.15, indicating good attention focus on the application during its use. The average of the Perceived Usability (PU) dimension was 3.97, which was the lowest value among all UES areas. This score is considered satisfactory but indicates some shortcomings in usability, which aligns with the SUS results. The average of the Aesthetic Appeal (AE) domain was 4.1, indicating good aesthetic appeal and that users found the design pleasant and engaging. The average of the Reward Worthiness (RW) dimension was 4.77, the highest average value among all dimensions, indicating that users considered the information or rewards provided to be worthwhile.  
These results were also supported by feedback from the participating users. One major aspect mentioned by all participants was the difficulty of moving/teleporting with the given gesture. Although this was part of the tutorial, there were still a lot of problems while using it in the game. Another aspect that was mentioned was the possibility of losing objects while moving around. When this happened, it was difficult to retrieve or find them if unnoticed (e.g., while teleporting and holding an object in the other hand). Another problematic aspect mentioned was the lack of clear information (i.e., goals) or support to solve riddles. Additionally, the lack of ability to interact with everything was also mentioned as unclear (e.g., not all drawers and chests can be opened).  
Figure 2: Game setting and room overview as a top-down view; each picture represents one floor where users can interact with the environment and switch between using the included stairs.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 6 Experimental Results
### Path-Smoothing Experiments  
There are many turning points on the routes in the park. If the path at the turning point is not smooth enough, it will not only affect the vehicle turning efficiency, but also may lead to path-planning failure. Therefore, this paper adopted minimum snap to optimize the trajectory at the turning point and obtain the continuity curve between two points using time, velocity and acceleration constraints. This makes the route smoother.  
Figure 9 shows the original path and the path after smoothing. From the figure, it can be seen that after the path processing, the smoothness of the path at the corners is obviously optimized, and the path planning can be carried out better in an environment containing a large number of corners.  
Figure 8: Comparison of the number of nodes.  
Figure 7: Traditional A* algorithm and improved A* algorithm. **(a)** Case 1, **(b)** Case 2, **(c)** Case 1 (d) Case 2. (The red number in the figure represents the number of nodes in the path, the blue solid line is the planned route, the black grid is the obstacle, and the white grid is the driving area).
# Designing Procedure Execution Tools with Emerging Technologies for Future Astronauts
## 2 Habitat Integration  
### Project Description  
This project investigated the integration between crew and habitat systems in order to assist astronauts during procedure execution. Smart Habitat Augmented Reality Procedure (SHARP) was designed and constructed to demonstrate how real-time spacecraft state feedback can guide a user to diagnose and solve a system-specific issue. SHARP translates traditionally formatted procedures into a real-time step-by-step visualization displayed through a user interface on an iPad. The SHARP application aimed to decrease response time and cognitive loads the users might face by clearly illustrating the steps necessary to resolve an incident. To walk the user through the procedure in an interactive manner, SHARP integrated IoT sensors and AR to visualize the actions required. The system automatically marked steps as completed when the prototype's sensors identified that the expected action had been performed.
# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 2 Background Information  
The previous OCR approaches have struggled with handling the complexities and intricacies of mathematical notation, causing suboptimal conversions. Mathematical scholars have turned to natural language processing (NLP) techniques to respond to these challenges to enhance OCR accuracy in image-to-LaTeX conversion. Integrating NLP strategies into OCR models can potentially improve mathematical expression recognition and conversion by leveraging semantic information and including linguistic context. Applying deep learning architectures like recurrent neural networks, convolutional neural networks, and transformer models, can enable OCR systems to capture subtle patterns and relationships within mathematical expressions [5]. Advancements in retraining techniques, like bidirectional encoder representations from transformers, can also capture the contextual embedding that helps accurately recognize mathematical symbols. This paper's main objective is to critically examine the challenges associated with OCR accuracy in the image-to-LaTeX conversion and propose creative solutions to enhance the performance of OCR models. This report explores innovative techniques that leverage NLP methods to address the limitations of current OCR systems. The main limitation of current OCR models in accurately recognizing and converting mathematical expressions is handling the complex equations characterized by several symbols and intricate structural arrangements [2]. Identifying and interpreting such equations is critical for accurate conversion to LaTeX format. Various mathematical symbols also tend to be challenging due to their visual similarity, making it difficult for OCR models to differentiate between similar-looking symbols accurately [4]. Another challenge arises from noise or distortion in the input images that negatively affects OCR performance (see Table 1). OCR models can better interpret signs based on their surrounding context and infer their intended meaning by encoding contextual information [6]. For instance, understanding whether a symbol represents an operator, a variable, or a function is essential for accurate conversion to LaTeX. Incorporating LaTeX syntax constraints during data augmentation guides OCR models to learn more accurate and compliant conversions, ensuring that the output adheres to LaTeX syntax rules. Studies can reduce the likelihood of generating syntactically incorrect LaTeX code during conversion by enforcing these constraints [5]. The constraint-based augmentation strategy improves accuracy and reliability in OCR outputs [11]. Additionally, mathematical expressions often exhibit dependencies and relationships between symbols, such as subscripts, superscripts, or fraction components. We can enhance the accuracy of OCR outputs by correcting recognition errors and ensuring the integrity of the converted
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 5 Discussion  
The analysis of the data shown in Figures 2-17 results enables us to draw the following conclusions.  
First, it is difficult to implement information hiding without significant container distortion. The above dependences of \(BER(PSNR)\) clearly demonstrate this. By increasing the volume of the hidden message \(k\) and /or the power amplification factor \(P\), we certainly distort the container, i.e., decrease PSNR. For example, chip codes \(N1\) and \(N2\) allow hiding only 1-2 bits of the message when \(PSNR>30\) dB and \(BER<10\%\). Spreading sequences \(N3\) and \(N4\) are somewhat better; they make it possible to hide 1-2 message bits when \(PSNR>35\) dB and \(BER<10\%\). However, this is still not enough. Finally, the Walsh-Hadamard signals perform well. If they are used, it is possible to hide 16 or more message bits and provide \(PSNR>30\) dB and \(BER<1\%\). This confirms the earlier conclusion about the preference of such chip codes for hiding information. This is also consistent with our findings from Spread Spectrum Image Steganography [23; 24].  
The second conclusion is a possible trade-off between the amount of hidden data and the introduced distortion of the audio container. For example, as follows from Figure 17, halving the \(k\) value leads to an increase in PSNR by about 5 dB. This can be used to configure the desired stegosystem parameters in a specific practical application.  
The third and probably the most important conclusion is that it is almost impossible to achieve high volumes of hidden information with the known techniques. For example, even for the best case shown in Figure 17, with \(PSNR=35\) dB and \(BER<1\%\), only \(k\leq 16\) bits of information can be hidden. It is possible to improve these values by traditional techniques only by increasing the length of the chip codes, which inevitably leads to an increase in the computational complexity of the transformations. This could, however, become a problem. Indeed, to restore each closed beta according to rule (8), it is necessary to calculate the correlation according to Equation (5). Therefore, you need to perform \(n\) additions and \(n\) multiplications, where \(n\) is the length of the chip codes. Our experiments correspond to the case of \(n=1024\), and we had a performance comparable to other modern data hiding techniques. However, as \(n\) increases, performance decreases, which can significantly complicate practical implementation.  
Another way is the implementation of adaptive techniques, for example, as in [55; 56], or the use of new, advanced methods of hiding information, for example, based on addressing chip codes [58]. These and other methods are a promising direction for our further research.
# Indirect Assessment of Railway Infrastructure Anomalies Based on Passenger Comfort Criteria
## 4 Indirect Method for Infrastructure Condition Assessment Based on Comfort Criteria  
Railway track infrastructure maintenance interventions are commonly decided based on measurements obtained by inspection vehicles and not on the dynamic response of in-service railway vehicles.  
Passenger comfort is affected by the vibrations that the carbody experiences due to motion and railway track infrastructure irregularities. As discussed, railway track irregularities can be identified based on acceleration records according to visible peaks in those measurements. Passenger comfort levels are also assessed through acceleration measurements. Thus, a strong link between isolated railway track irregularities and passenger comfort levels can be identified. The present method relies on that connection. Based on the natural excitation created by the passage of railway vehicles over the track and on the fact that a defective railway track system induces higher vibration, it is expected that high discomfort levels will be accomplished when in the presence of an abnormality on the railway track infrastructure. Thus, it was hypothesised that railway track infrastructures require maintenance if multiple trains with different suspension mechanisms report instantaneous floor discomfort levels at the same geographic location.  
Therefore, to investigate the defined hypothesis, a condition-based maintenance identification methodology was developed. The proposed methodology includes two stages: the experimental data measurement and acquisition and the data analysis. Acceleration measurements were performed based on a 3-axial accelerometer, aided by a precision GPS to determine the vehicle's location. The ISO 2631 standard provided the reference evaluation method for assessing passenger comfort. Then, a MATLAB algorithm was developed to match multiple railway vehicles' discomfort locations and identify maintenance needs locations. It should be highlighted that the methodology was applied on ballasted tracks, that is, the track type of the Northern line of the Portuguese railways. Nevertheless, there are no expected assessment differences when applying the present method to ballastless tracks.
# Fractional Sliding Mode Harmonic Control of an Active Power Filter
## 1 Introduction
The key property of the method is to select the order of the fractional module and choose suitable parameters to achieve the best performance. 3. A simulation study further proves the good performance in eliminating the harmonic current while stabilizing the DC side voltage. Meanwhile, the superiority of the proposed control scheme was verified through detailed comparisons and analysis. The rest of this paper is organized as follows. In Section 2, a dynamic model of APF is established. In Section 3, a fractional-order SMC method is proposed and analyzed. Simulation studies are given in Section 4. Eventually, Section 5 gives the conclusions.
## 2 Results and Discussion
### Possible Reaction Mechanism  
Based on previous reports [38; 39] and the results in our study, a probable catalytic cycle was proposed for the hydration of alkyne using SnCl\({}_{4}\cdot\)5H\({}_{2}\)O as a catalyst, as shown in Scheme 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Entry** & **Alkyne** & **Product** & **Yield \({}^{\text{b}}\) \%** \\ \hline
1 & (1a) & (2a) & 91 \({}^{\text{c}}\) \\
2 & (1b) & (2b) & 98 \\
3 & (1c) & (2c) & 93 \\
4 & (1d) & (2d) & \(>\)99 \\
5 & (1e) & (2e) & 49 \\
6 & (1f) & (2f) & 81 \\
7 & (1g) & (2g) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hydration of other alkynes using SnCl\({}_{4}\cdot\)5H\({}_{2}\)O as a catalyst \({}^{\text{a}}\).  
Generally, the hydration of alkyne may follow pathways: one is proposed based on Hg\({}^{2+}\) salts as catalysts and another is proposed using Bronsted acids as catalysts. Sn\({}^{4+}\) salts show strong Lewis acidity, so SnCl\({}_{4}\) could form hydrates with water and the proton in H\({}_{2}\)O was activated. In this process, the carbon-carbon triple bond of alkyne was protonated by H\({}^{+}\) which was produced by the activated H\({}_{2}\)O which was contained in solvent and catalyst and then coordinated with SnCl\({}_{4}\)(H\({}_{2}\)O)OH\({}^{-}\) to form intermediate II. Then intermediate II was reductive elimination and rapid tautomerization to produce methyl ketone.  
**Scheme 2.** The possible mechanism for the hydration of alkyne in the presence of SnCl\({}_{4}\)-5H\({}_{2}\)O.
# Approaches for Dealing with Seasonality in Clinical Prediction Models for Infections
## 3 Materials and Methods
### White-Box Models  
The aim of a clinical model is to provide support when making clinical decisions, not to replace the experts who make them. Therefore, the interpretability of the model and the ability to understand the rationale behind its predictions are crucial for its acceptance, even if this results in a slight decrease in its accuracy. Models that are easily understood and applied by users are commonly referred to as _white-box models_ or _interpretable models_. Examples of such models include logistic regressions and decision trees, which are widely used in clinical settings. On the other hand, certain Artificial Intelligence approaches, such as deep learning and bagging (when using a large number of members [25; 26]) generate complex models, often referred as _black-box models_, which are more difficult to interpret [27]. These models have historically been less well accepted despite producing very accurate predictions [28].  
In this work, we experiment with both logistic regression and decision trees. Logistic regression is one of the most common techniques employed to build clinical prediction models [29]. These models have the following structure:  
\[log\Big{(}\frac{\mu}{1-\mu}\Big{)}=\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{p}x_ {p}\]  
where \(\mu=P(Y=1)\), in which \(Y\) is an indicator variable denoting the occurrence of the event of interest and \(x_{1}\cdots x_{p}\) are the features from the dataset used in the model. The \(\beta_{i}\) components are usually estimated by means of maximum likelihood, although they are sometimes modified in order to avoid overfitting [30].  
Decision tree models consist of a series of nested conditions that partition the feature space into homogeneous groups that can be assumed to be of a particular class with an acceptable margin or error [31]. These models can be represented as a set of _if-then_ rules, or more traditionally as an inverted-tree graph in which the split conditions are the nodes and the predicted classes are the leaves. As a result, these models are highly interpretable [31].