esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 4 Blast Pressure Selection from Explosion Analysis Results
### New Methodology for Blast Pressure Application
#### 4.3.3 Blast Pressure Application Considering Propagation Features  
Figure 14 presents the pressure applied on the pressure groups and the configuration of pressure in the time domain. Figure 14a shows the application of the highest blast pressure of the drill floor on all the areas at the same time. This Overall Peak is widely used in structural analysis due to its simplicity for application; however, it could overestimate the pressure and is considered as a very conservative approach, which results in a heavy blast protection structure. Figure 14b shows the application of the average drill floor blast pressure on all the areas at the same time. This Overall Average uses the same methodology as that of the Overall Peak, except the application pressure. The overall average value of the entire drill floor area is calculated and applied. This method is simple to use; however, it is considered to underestimate the pressure since there exists higher pressure than the average pressure.  
Figure 14c shows the application of the peak pressure of each grouped area on each area at the same time. This methodology improved the Overall Peak and Overall Average blast pressure application since these two ways of selecting the pressure could over- or underestimate the blast pressure. This divides the area into groups, selects the peak pressure of the group, and uses it as the blast pressure [14].  
Figure 15 shows the application of peak pressure of each grouped area and includes the blast pressure propagation based on the explosion reviewed in Figures 11 and 13, which represent the nature of explosion. When the ignition occurs in the flammable gas cloud, the explosion propagates through the area. The methodology proposed in this study considers this nature of explosion. Based on the time gap required for propagation through the area, which is obtained from the review of the explosion result, the peak pressure of the group is applied to each group area. Ignition occurs in area No. 1 and the explosion pressure propagates through areas No. 2, No. 3, and No. 4 shown in Figure 15. The result of the explosion shows that the pressure amplitude increased in area No. 4. This can capture the nature of explosion, and the result of each pressure application is investigated in Section 5.  
Figure 13: Propagation of blast pressure (**a**) at 1.267 s after ignition, (**b**) at 1.294 s after ignition.
## 3 Results
### Phase 3--Sensor Performance Verification  
With the adjusted sensitivity of the heat flux sensor, the sensor sensitivity adjustment procedure could proceed to the verification stage using data from Phase 3. For the verification, four testing periods between autumn 2020 and spring 2021 that followed the adjustment periods were selected (see Table 1 and Figure 6) and performance data was obtained for those periods. The idea of the verification was to prove the stability of the performed adjustment on blind data in the periods sufficiently close to the point of readjustment. The performance data included indoor and surface temperature measurements as well as outdoor climatic data for the simulation of the thermal response of the wall. Similarly to the previous steps, the surface heat fluxes \(q_{\text{IN}}\) and \(q_{\text{OUT}}\) were derived from experimental-computational data and compared against the output of the adjusted heat flux sensor.  
Generally, the most important data reflecting the accuracy of demonstrated application are shown in Figure 11. Here, various accuracies of the adjusted sensor can be observed within the periods of verification. To provide a better insight into the accuracy of the adjusted heat flux sensor, a regression analysis of data was done and the \(R\)-squared values together with \(RMSE\) were determined for each period. The results of regression analysis are shown in Figure 12. The discrepancy from the adjustment phase as discussed in Section 3.2  
Figure 10: Sensor performance after sensitivity adjustment (Phase 2).  
(see Figure 9) has been clearly projected into this verification (as marked in Figure 11). Since the verification was done on the periods that followed the adjustment periods immediately, some performance deviations in the period 7 December 2020-13 December 2020 were anticipated. This observation underlines the need for a proper re-initialization based on detailed surveillance and analysis of the adjusted performance to optimize the computational model and to adapt the proposed procedure to changing environmental conditions.  
It should be noted that the investigated object serves recreational purposes which is typical of a seasonal regime of occupation. As for this, some parts of the object, such as the entry foyer or some technical rooms, are considered \"free-running\" rooms, i.e., without space heating and/or cooling during the entire year. This stands also for the room, where all the sensors were placed, which is apparent from the interior temperature development during the measuring period (see Figure 6). This fact can seriously affect the results as the operating conditions might dramatically change over time, which can affect the sensor performance and physical behavior of construction materials.  
Figure 11: Performance verification of the self-adjustment technique.
# 2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation
## 2 Materials and Methods
### Methods
#### 2.2.2 Normalized Cross-Correlation Based on LOG Operator  
The Gaussian Laplacian can highlight the image's details by analyzing edge extraction operators while extracting the edge information [41]. This paper proposes combining normalized cross-correlation coefficient with image edge information to overcome the shortcomings of normalized cross-correlation. The Laplacian of Gaussian (LOG) operator extracts image edge information. Normalized cross-correlation (NCCL) based on the LOG operator is obtained. The Laplacian image is obtained by convolution of the reference image and DRR image with the LOG operator. The zero-crossing point in the Laplacian image is no longer needed to obtain the image's detailed edge. However, two Laplacian images' consistency is directly measured to use image edge and detail information effectively. This paper uses cosine similarity to measure the similarity between Laplacian images. The new similarity measure composed of normalized cross-correlation coefficient and Gaussian Laplacian operator \(S2(I_{R},I_{D})\) is shown in (8), and the relationship between \(I_{D}\) and \(I_{M}\) is shown in Equation (9).  
\[S2(I_{R},I_{D})=NCC(I_{R},I_{D})+NC(LoG*I_{R},LoG*I_{D}) \tag{8}\]  
\[T_{g}=argmax_{T}\ N\ CC(I_{R},P(T(I_{M})))+NC(LoG*I_{R},LoG*P(T(I_{M}))) \tag{9}\]  
The normalized cross-correlation coefficient between the reference image \(I_{R}\) and the DRR image \(I_{D}\) is \(NCC(I_{R},I_{D})\). The cosine similarity between the Laplacian images \(NC(LoG*I_{R},LoG*I_{D})\) is obtained by convolution of the reference image \(I_{R}\) and DRR image \(I_{D}\) with the LOG operator. The optimization process based on the LOG operator can be obtained by substituting Equation (8) into (10). Since the panel's coordinates are fixed, and NCC and NC are calculated pixel by pixel, both consider the image coordinate information. In addition, the NCC part considers the gray information of the global image. The NC part takes the unique edge information and internal details such as lines and outliers. For medical image registration, the distribution of NCC values is between 0 and 1. The closer the NCC value is to 0, the more significant the difference between the two images. The closer the NCC value is to 1, the stronger the correlation between the two images and the higher the similarity degree is. When the NCC value equals 1, the two images are the same. The value of NC is also distributed between 0 and 1, which has the same meaning as NCC. Therefore, the closer the image registration value is to DR2, the better the image registration effect is.
# Application of Machine Learning Algorithms in Real-Time Monitoring of Conveyor Belt Damage
## 4 Conclusions  
The analysis proved that the strain gauge-based measurement system provided a reliable sequence of signals for the real-time monitoring of the conveyor belt work. As a result of continuous monitoring of the belt operation, the system generated a significant amount of data that underwent an extended analysis based on the machine learning (ML) algorithms. The signals were collected from three strain gauges, namely, T1, T2, and T3, after 20 repetitions, and statistical parameters were calculated accordingly. It was found that for damage monitoring, the most significant features were the arithmetic mean value from the T1 strain gauge, followed by the T1 Root Mean Square, T2 Root Mean Square T2 Mean, and T2 standard deviation. The results indicate the existence of an identifiable correlation between the actual condition of the rubber belt and the signal recorded by the strain gauge system. So, it was experimentally proven that the strain gauge-based system could be successfully used for the real-time monitoring of the conveyor belt operation.  
A further analysis of 18 features was performed using 10 ML algorithms. Four of them were able to correctly classify the undamaged belt, the belt with three preset defects, and the one with five defects. Thus, it can be concluded that the system provided real-time information enabling the detection of the belt damages that appeared during the conveyor operation. It is planned to test more ML algorithms in future research. Moreover, it is important to investigate the possibility of detecting damages appearing and dynamically developing during the belt's operation in order to prevent failures.
# Development of a Virtual Reality-Based Environment for Telerehabilitation
###### Abstract
This aim was to assess the effectiveness and applicability of these assistance methods in the rehabilitation process. The patients received assistance through force fields using a robotic device for upper limb rehabilitation. The results indicated that the personalized assistance provided by the therapist better matched the patient's needs compared to fixed assistance. In [17], the authors explored the efficacy of combining repetitive transcranial magnetic stimulation with adaptive gait training to improve lower limb function and regulatory mechanisms in subacute stroke. [PERSON] et al. described a study in which a robot designed for lower limb rehabilitation in stroke patients can be used for both on-site and telerehabilitation [3]. In [18], the authors presented a study describing a new remote rehabilitation system that integrates an IoT-based connected robot intended for wrist and forearm rehabilitation. To facilitate the work of doctors in telerehabilitation and to find methods that can be used to treat patients better, artificial intelligence techniques are used [19]. [PERSON] et al. conducted a study in which the potential of human position estimation from monocular 2D videos is demonstrated, as a marker-free solution, being accessible in musculoskeletal telerehabilitation approaches [20]. In [21], the authors presented an efficient model for tracking a person for a certain range of motion using artificial intelligence technology. This paper presents the development of a VR-based environment for personalized telerehabilitation. Compared to existing robotic rehabilitation systems, this system allows the therapist to test different rehabilitation training exercises on a virtual patient before implementing them with the actual patient. The patient is informed and can view in VR the exercises to be performed. This approach enhances the safety of the robotic rehabilitation system, especially for home-based training. Thus, the therapist can create, test, and implement a training plan via a multimodal control interface, then send it to the real robot locatedremotely or in the patient's home environment. An assistant supervises the rehabilitation process through a webcam, which streams video to the robotic control interface. Section 2 describes the proposed VR environment for the safe use of the lower limb rehabilitation robot and the multimodal user interface, Section 3 presents the initial validation test results with healthy subjects, while Section 4 concludes the paper.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 8 Optimization Using the Modified GA Approach
### Selection  
The algorithm starts with a randomly generated population. The population of size N is selected, and a dictionary is created to hold the population. Each individual is associated with chromosomes (positions) and a cost function. Each position in a chromosome is filled with randomly generated values between the lower limit of \(-1\) and the upper limit of +1, which are called genes. Among the various selection methods, the roulette wheel was adopted, where the probability of individuals is calculated as  
\[P_{i}=\frac{F_{i}}{\sum_{i=1}^{N}F_{i}} \tag{24}\]  
where \(F_{i}\) is the fitness function and \(N\) is the population size. In order to increase the average fitness of the population, a new population is generated from the previous one. It has a higher probability to be included in the new generation depending on the fitness value of the chromosomes. The fitness value is computed for each chromosome as \(\sum_{i=1}^{n}F_{i}\).  
The fitness function \(F_{i}\) is given by  
\[F_{i}=min\sum_{i=1}^{N}\rho cu_{i} \tag{25}\]  
Figure 4: Flowchart of the modified GA.  
In Figure 5. entire set of boxes outlined in green color is a chromosome. A chromosome is made up of genes. The green colored box within a chromosome is called a gene and collection of chromosomes is called a population.
# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
## 3 Results
### Risk Appetite Assessed via Time Trade-Off (TTO) Method (Phase 3)  
Analyzing the assigned utility scores (likely a normalization of accepted years, where 1.00 represents preference for the current treatment or acceptance of 20 years of perfect health), all evaluators had means close to 1.00 (ranging from 0.8563 for Perplexity to 0.9625 for Gemini) and medians of 1.0000. This indicates a strong general tendency among GenAIs not to trade-off years of life to avoid moderate pain, preferring the current situation or the alternative only if it offers the maximum duration of perfect health. However, Perplexity is notable for the lowest mean and the highest standard deviation (0.22904), suggesting greater variability and a slightly greater tendency to accept the trade-off compared to the other GenAIs. Skewness and Kurtosis values confirm the previously observed non-normal distributions.  
Comparing mean TTO utility scores between pairs of evaluators, the only statistically significant difference (\(p\) < 0.05) was between Gemini and Perplexity (\(p\) = 0.045). Gemini had a significantly higher mean utility score than Perplexity, confirming that Gemini is more risk-averse (less willing to sacrifice years of life) in the TTO context compared to Perplexity. Other comparisons did not show significant differences, suggesting general similarity in the TTO approach among ChatGPT, Owen, DeepSeek, and, to some extent, Gemini and Perplexity apart from their direct comparison.  
The Friedman test was used to compare GenAI preferences (expressed as the minimum number of years of perfect health accepted in exchange for the current treatment) for each patient age group (20, 40, 55 years). For the first and second scenarios (age of the subject = 20 and 40, respectively), no significant differences were found between evaluators (Table 4). In the third scenario (age = 55 years) analyses of the mean ranks suggests a potential trend: Perplexity had the highest mean rank, indicating potentially greater willingness to accept fewer years of perfect health (higher risk tolerance in this TTO context), while Gemini had the lowest mean rank (2.25), suggesting the opposite (see Figure 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Chi-Square** & **\(p\)-Value** & **Mean Ranks (Evaluator: Rank)** \\ \hline
20 years & 6.389 & 0.172 & GPT: 3.44, GEMINI: 2.50, QWEN: 3.44, DEEPSEEK: 2.50, PERLEXITY: 3.13 \\
40 years & 1.884 & 0.757 & GPT: 3.31, GEMINI: 2.94, QWEN: 2.75, DEEPSEEK: 2.63, PERLEXITY: 3.38 \\
55 years & 9.466 & 0.05 & GPT: 2.81, GEMINI: 2.25, QWEN: 2.94, DEEPSEEK: 2.69, PERLEXITY: 4.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Risk appetite assessed via Time Trade-Off (TTO) method.
# Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning
## 7 Conclusions  
In this study, a method was proposed to analyze the characteristics of tweet content as a function of tweet diffusion to classify the buzz class of tweets. Initially, the tweets were classified into buzz classes. Tweets that had an image and more than 1000 likes were considered buzz tweets, whereas tweets that had an image and less than 1000 likes were considered non-buzz tweets. In the proposed method, the text features of the tweets were extracted using the pre-trained BERT model, and the image features were obtained from pre-trained models such as VGG16. The neural network was then trained for multiple tasks. The results of the evaluation experiments showed that the correct response rate for buzz class prediction with the proposed method using both text and image features was higher than that using the features alone. However, it is not clear whether BERT or VGG16 is moresuitable for buzz class prediction, so it is necessary to compare the proposed method using other options such as the simpler bag-of-words feature.  
In this study, the prediction results were not evaluated for the number of likes and RTs in multi-task learning. Considering the comparison results with the buzz class prediction model using a single task, it would be effective to consider the number of likes and RTs in multi-task learning. However, the scale of the dataset used in this study is insufficient to train a model to predict the number of likes and RTs with high accuracy. In the future, we would like to construct a larger dataset to evaluate the prediction accuracy (or prediction error) of the number of likes and RTs.  
The textual content of buzz and non-buzz tweets can be positive or negative, short or long. By analyzing the relationship between text and image content, a more accurate and flexible model for buzz prediction can be created. In the future, we plan to create a more accurate buzz prediction model by considering the emotional polarity of the tweet text, the sender's profile, and other attributes as features, as well as the relevance of the attached image.  
In addition, recently studied techniques such as capsule networks [30; 31], aspect-oriented sentiment analysis [32; 33; 34; 35], hybrid approaches that combine deep learning with rule-based approaches [36], and the neuro-symbolic concept learner approach [37] can be used for more accurate feature extraction and buzz analysis.  
Conceptualization, K.M.; Data curation, R.A.; Funding acquisition, K.M. and M.Y.; Methodology, K.M.; Supervision, K.K.; Validation, M.Y.; Visualization, K.M.; Writing--original draft, R.A.; Writing--review & editing, K.M., M.Y. and K.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2021 SCAT Research Grant and JSPS KAKENHI Grant Number JP20K12027, JP21K12141.  
Not applicable.  
The authors declare no conflict of interest.
# Heterogeneous Image Matching via a Novel Feature Describing Model
## 2 Fundamentals  
### Scale Space and Difference Space and Principal Component Analysis  
Scale space theory [40] has been introduced in many models and it can help to distinguish the features in different levels. After a scale parameter added to the related model, a multi-scale representation sequence can be computed following the changing of the parameter. Many tasks can be achieved by analyzing or processing the sequence such as extracting features.  
The classical linear scale space can be generated by a diffusion equation as shown in Equation (1), and it can be regarded as the observation of the image at different distance/scale:  
\[\left\{\begin{array}{ll}\frac{\partial I}{\partial t}(x,t)=c\
abla^{2}I(x,t ),&(x,t)\in\Omega\times\Sigma,\\ I(x,0)=I_{0}(x),&x\in\Omega,\end{array}\right. \tag{1}\]  
where \(t\) denotes the scale parameter and \(c\) denotes the diffusion constant. \(I(x,t)\) means the a scale space and \(\Omega\subset R^{2}\) is the image domain. This partial differential equation can be solved by some classical techniques such as Fourier transform and many related models have been presented for different image processing problems [41; 42; 43]:  
\[I(x,t)=I(x)*\frac{1}{4\pi t^{2}}\exp(-\frac{|x|^{2}}{4t^{2}}). \tag{2}\]  
It means that the scale space can be denoted as the convolution of image function \(I(x)\) and a Gaussian kernel function \(g_{\sqrt{2t}}\) with different \(t\), so-called Gaussian scale space. Then, the discrete difference of Gaussian space can be denoted as  
\[d\ I(x,t)=I(x,t+dt)-I(x,t). \tag{3}\]  
Here, \(dt\) can be set according to the image data. Figure 1 shows two Gaussian images and a difference image of Lena.  
Principal Component Analysis (PCA) [44] is a common technique for dimensionality reduction and has been widely applied to many computer vision problems such as feature selection, object recognition, and so on. The main ideas of PCA can be summarized as follows. Assume \(n\times d\) matrix  
Figure 1: Gaussian scale space of Lena image.  
is the normalized observation data of random variables \(Y_{1},Y_{2},\cdot,Y_{d}\). Then, the \(d\)-dimensional data can be mapped into an orthogonal space for a maximal variation in each new dimension. The orthogonal space can be computed by applying matrix eigen value theory on the covariance of \(Y\):  
\[Y^{T}Yv_{i}=\lambda_{i}v_{i},\ i=1,2,\cdots,d. \tag{4}\]  
The components can be denoted as \(V_{i}=Yv_{i}\) with the assumption \(\lambda_{1}>\lambda_{2}>\cdots>\lambda_{d}\) and \(v_{i}\) means the direction with maximal projection variation of original data. Then, the first \(k\) components can be used for dimension reduction of the original data, and \(k\) can be determined by specific needs.  
Though PCA suffers from some restriction and shortcomings, it is still popular due to its simplicity. [PERSON] et al. introduce PCA to unsupervised scale-invariant learning for object recognition [PERSON] (2000). This technique is also applied to represent keypoint patches and improves SIFT's matching performance [PERSON] (2000), named PCA-SIFT.