esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 5 Simulation Analysis of Seepage-Stress Coupling  
### Permeability Evolution Model  
The permeability evolution model reflects the coupling relationship between the stress-strain field and the seepage field. Through the permeability test of mudstone, [PERSON] [30] obtained the evolution relationship between the stress-strain state, volume strain and permeability under pressure. In the process of whole stress-strain, the change in permeability is a complex process that first slightly decreases, then slowly increases, then suddenly increases and finally tends to be stable. [PERSON] [31] analyzed the process combined with volume strain and divided the whole process into five stages. However, in fact, the critical point of each stage is difficult to accurately define. From a physical point of view, the permeability evolution is related to the closure, development and penetration of internal pores and fissures, and the change in pores and fissures is closely related to the damage and plastic deformation of materials. Considering that the change in permeability in the compression stage of mudstone is much smaller than that in the expansion stage, and the latter stage is mainly used in oil and gas exploitation engineering, this paper defines the evolution of permeability as a function of the damage variable and simplifies it into two stages: \(D=0\) and \(D\geq 0\). Further, referring to the research results [32; 33], the permeability evolution model is defined as:  
\[k=\begin{cases}\begin{array}{c}k_{0},\;\;D=0\\ k_{0}\Big{[}1+\xi\big{(}1-e^{-nD}\big{)}^{2}\Big{]}\;,D\geq 0\end{array}\end{cases} \tag{35}\]  
where \(k_{0}\) is the initial permeability coefficient, and n and \(\xi\) are the model parameters. They can be obtained by fitting the permeability coefficient curve. \(\xi\) is the magnitude increasing coefficient, which can describe the sharp increase in the permeability coefficient in the damage-fracture process of mudstone.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
## 1 Introduction
[19] analyzed the dynamic response of the world's longest steel box tied-arch bridge under moving trains by field measurement. They found that the poor road surface roughness can greatly increase the IM, and the train braking can also produce a larger IM. [PERSON] and [PERSON] [20] studied the actual dynamic response of a long-span corrugated steel culvert railway bridge by measuring the strain and displacement under moving trains. In their study, the dynamic displacement were found to be as much as 20% larger than the corresponding static response, and the dynamic amplification factor (DAF) obtained from the moments reached a high value of 1.45. [PERSON] et al. [21] conducted field tests of a prestressed concrete simply supported beam bridge subjected to two electric-multiple-unit rail cars to determine the IMs, and the IMs were found to be insensitive to train speed and smaller than investigated code provisions. So far, much experimental research has been conducted to study the dynamic impact of railway arch bridges or simply supported bridges. However, fewer experimental studies have focused on the dynamic impact effect of moving trains on railway continuous beam bridges, especially for large-span railway continuous beam bridges with a main span of more than 100 m.  
In this study, the static and dynamic load tests of the Yexihe Bridge, which is the longest continuous-girder, high-speed railway bridge in China, were carried out to investigate the dynamic responses of the bridge under design moving loads. In the field test, one or two freight trains passed through the bridge at constant speeds. The impact factors for the deflections and strains of the bridge were measured in different loading scenarios and compared. A finite element model was established to analyze the influence of the train's speed and type, and track irregularity.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 6 Results and Analysis
### Experimental Setup  
Before presenting the results, we provide details of the experimental setup. We used three state-of-the-art network traffic classification models as the baseline. These models were trained on a labeled dataset that contained both benign and two families of DoS malicious network flows. We split the dataset into training and test sets, in a 70:30 ratio. The entire test set was then used to generate AEs. This approach enables us to assess the resilience of the proposed method against adversarial perturbations. Table 3 outlines the hyper-parameters settings for the studied models and attack method.
## 3 Materials and Methods  
### Dataset Collection  
In this study, two distinct datasets--time-series and spatial datasets--were constructed to validate the effectiveness of the proposed model. These datasets reflect the characteristics and requirements of time-series and spatial data in various real-world applications. The process of dataset collection and preparation was a critical component of the experimental workflow, involving meticulous efforts in data source selection, collection methods, cleaning, and preprocessing to ensure high data quality and broad coverage. The construction of the time-series dataset primarily focused on financial scenarios. Representative stock markets, including the Shanghai Stock Exchange Composite Index (SSE), NASDAQ, and S and P 500, were selected to gather stock prices and related indicators. These data were sourced from multiple authoritative financial platforms, such as Yahoo Finance, Sina Finance, and Google Finance. Automated web scraping scripts were developed to periodically retrieve data, utilizing multithreaded crawling techniques to enhance efficiency and ensure real-time data availability. During the collection process, particular attention was given to the following aspects:  
First, data completeness was ensured by covering as long a historical period as possible for each stock (2013-2023), avoiding data gaps caused by network issues. Second, data diversity was emphasized, as shown in Table 1. Beyond core metrics such as stock prices, auxiliary indicators were collected, including stock volatility, market sentiment indices, trading volumes, and turnover rates. These supplementary indicators provided robust support for multidimensional analyses.  
For the spatial dataset, the CelebA [36] dataset was chosen as the primary data source, as shown in Table 2. CelebA is a high-quality public facial image dataset containing over 200,000 images, each annotated with detailed metadata, such as facial landmarks, expression categories, and whether glasses are worn. These annotations provided a rich foundation for in-depth analysis in the research tasks. The collection method for spatial data combined the direct download of publicly available datasets with careful compliance checks. During the download process, the licensing agreements for the datasets were thoroughly reviewed to ensure adherence to ethical standards and legal requirements for research.  
\begin{table}
\begin{tabular}{l c} \hline
**Data Type** & **Number of Entries** \\ \hline Stock Prices & 50,923 \\ Stock Volatility & 37,010 \\ Market Sentiment Index & 29,741 \\ Trading Volume & 40,532 \\ Turnover Rate & 25,994 \\ \hline \end{tabular}
\end{table}
Table 1: Categories and counts of time-series data.  
The datasets collected represent diverse and comprehensive data sources, ensuring their applicability across various research scenarios. The time-series dataset captures critical temporal dynamics in financial markets, while the spatial dataset provides rich facial image annotations for detailed analysis. These datasets lay a solid foundation for validating the proposed model's performance across multimodal tasks.
# Coloured Petri Nets-Based Modeling and Validation of Insulin Infusion Pump Systems
## 8 Discussion
This finding helps insulin infusion pump systems manufacturers deal with increased development time and project costs when applying formal methods. The questionnaire responses for the two experiments reflect the favorable opinion of the interviewed modelers concerning the re-use of the reference model. All modelers reported an outstanding or optimum achievement of the training phase, showing proper knowledge about the study (approximately 91.7%) and the CPN (approximately 83.3%). The results indicate a positive response to the main RQ. However, the modelers' performance was low when conducting modifications in the reference model, presenting a usability problem. A hypothesis of a solution to address this problem is the usage of GUI to assist modelers in understanding and modifying model components as a learning mechanism. Such a hypothesis motivated the web-based application's implementation to assist the simulation-based training of modelers. Implementing the application using the Access/CPN framework to conduct the background execution of CPN models enabled the improvement of \(MBA/CPN\) because it does not require knowledge about all model components to conduct simulations. The application provides feedback about the meaning of such model components to improve the understanding of modelers. Besides, during the implementation, we were also able to provide an API RESTful (Figure 14) that can be re-used by other developers of safety-critical systems (e.g., to perform other simulation-based training). The simulation-based training using the software application may also be relevant to improving systems' GUI design. As stated in the introduction, one of the problems faced by insulin infusion pump systems developers is the provision of inadequate GUI [10]. However, there are some limitations to validity. Two refinements of the reference model enabled the model checking and validation by simulations. With the generation of the strongly connected components graph, this approach prevented the state space explosion problem instead of using a more robust technique to reduce the more detailed version's state space, e.g., equivalence method. This may have made the model harder to handle, considering that manufacturers need to split the verification and validation activities into different specification versions. The empirical evaluation showed that modelers reported the model as an easy-to-understand project artifact. In addition, we used two properties of insulin infusion pump systems to conduct the model checking. This may have limited the verification step because other property regulatory agencies may consider relevant in the certification process. The simulations of the model by hypothetical insulin dosages may also limit the validation compared to using real data from a medical prescription. However, we used verification and validation to illustrate quality assessment scenarios rather than fully validate an instantiated system's behaviors.
# Multiple Kernel Stein Spatial Patterns for the Multiclass Discrimination of Motor Imagery Tasks
## 5 Discussion and Concluding Remarks
Therefore, the proposed methodology avoids the computation of unnecessary frequency components for discriminating MI tasks. Further, the weight distribution may predict the subject BCI performance. Regarding the spatial representation, Figure 9 presents the resulting Stein spatial patterns for subjects S07, S05, and S02. Only the patterns corresponding to the least (top) and most (bottom) weighted bands are displayed. On the one hand, the least weighted bands yield wider activity sources without neurophysiological interpretability. On the other hand, MKSSP mostly highlights bands with spatial patterns physiologically related to MI tasks. For instance, the contralateral activities, typical of left and right-hand movement, emerge from the first two patterns of subjects S07 and S05. In addition, the third pattern for all subjects finds activity close to the central vertex related to the foot movement. Consequently, MKSSP correctly decodes spatially interpretable patterns while spectrally highlighting the bands containing them. Future work considers two research directions. Firstly, we will work on the development of a BCI ability estimator based on the MKSSP methodology to evaluate the subject efficiency, since up to 30% of people underdevelop the coordination skills after training, hampering the spreading of this kind of system [38]. Secondly, we will consider a transfer learning scheme for training the MKSSP-based representation to achieve subject-independent BCI systems with reduced training times. Conceptualization, D.C.-P. and S.G.-N.; methodology, D.C.-P.; software, S.G.-N.; validation, D.C.-P., A.O.-G. and S.G.-N.; formal analysis, D.C.-P. and S.G.-N.; investigation, S.G.-N.; resources, A.O.-G.; data curation, D.C.-P.; writing--original draft preparation, S.G.-N.; writing--review and editing, D.C.-P. and A.O.-G.; supervision, D.C.-P. and A.O.-G.; project administration, A.O.-G.; funding acquisition, A.O.-G. All authors have read and agreed to the published version of the manuscript. This research was funded by the project with code 111080763051 by MinCiencias and Universidad Tecnologica de Pereira and by the Vice-rectory for research from Universidad Tecnologica de Pereira by the project with code E6-20-3. This work was supported by the research project \"Herramienta de apoyo al diagnostico del TDAH en nios a partir de maltiples caracteristicas de actividad electrica cerebral desde registros EEG\" with code number 111080763051 and funded by MinCiencias. Authors also thank to the Vice-rectory for research from Universidad Tecnologica de Pereira for supporting the development of this work. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} BCI & Brain-Computer Interface \\ EEG & Electroencephalography \\ MI & Motor Imagery \\ CSP & Common Spatial Pattern \\ SPD & Symmetric Positive Definite \\ CKA & Centered Kernel Alignment \\ MKL & Multiple Kernel Learning \\ MKSSP & Multi-Kernel Stein Spatial Patterns \\ \end{tabular}
# Temperature Prediction of Chinese Cities Based on GCN-BiLSTM
###### Abstract
We consider the spatial-temporal features of meteorological data and integrate the GCN-BiLSTM model to propose a novel temperature prediction method that improves the accuracy of temperature prediction from the perspective of temporal and spatial distribution. 2. The spatial relationship between the monitoring stations is used to learn the features of the meteorological data from the surrounding stations and to explore the correlation between the meteorological factors of the surrounding stations and the temperature of the target stations. 3. The research results show that the average RMSE of this prediction method is 2.671 \({}^{\circ}\)C and the average MAE is 3.024 \({}^{\circ}\)C for the future 14-day temperature prediction; for the multi-area temperature prediction, the average RMSE is 1.906 \({}^{\circ}\)C, and the average MAE is 2.583 \({}^{\circ}\)C. The model's performance in this study is significantly better than other baseline models, and it has higher accuracy in cities with stable temperature fluctuations and small temperature differences. The rest of this paper is organized as follows. In Section 2, we systematically introduce a series of related works on modeling approaches for meteorological forecasting learning. Section 3.1 describes the data source and the data preprocessing workflow. Section 3.2 details our proposed temperature prediction model, GCN-BiLSTM. In Section 4, extensive experiments are designed and conducted to verify the effectiveness of the GCN-BiLSTM model. We describe the experiments of multi-day and multi-regional temperature prediction, including the data description, baseline methods, evaluation metrics, and experimental settings. For further illustration, we present the experimental results of various baseline models while comparing the performance with some common assessment criteria in Section 4.3. Finally, in Section 5, we summarize and discuss our research work.
# Impact of a Robot Manipulation on the Dimensional Measurements in an SPC-Based Robot Cell
## 4 Results  
In the experiments, we used comparative measurements (see Section 3), with the main goal to show how any additional robot manipulation influences the measurement process's capability (process variation). Centering was of interest, so only the precision index \(C_{p}\) was used in our study.  
We confirmed the normal distribution of the measured data for all measured characteristic codes for both measured objects using Anderson Darling normality test [39] with 95% confidence level. Anderson Darling normality test rejects the hypothesis of normality when the p-value is less than or equal to \(p_{min}=0.05\). Corresponding statistical p-values for all characteristic codes for scenario 1 and scenario 9 are greater than \(p_{min}\) and are gathered in Table 3. Additionally, Figure 6 shows the normal probability plot, respectively, of characteristic codes 20 and 30 for a Magnet object, and Figure 7 shows the normal probability plot  
The normal distribution of the measured data allows the use of the capability index \(C_{p}\).  
Results provide the first selected graphs for the Magnet object. The following are selected graphs for the PKR object. For each repetition of the scenario, an MSA type-1 analysis was performed for each characteristic code. The selected statistical characteristics obtained from the MSA study are the capability factor \(C_{p}\), the range of measurements \(R\), and \(6\sigma\) (standard deviation) of the individual repetitions. The relationship between \(C_{p}\) and \(6\sigma\) is described in (2), and the range of measurements \(R\) has no mathematical relationship  
Figure 6: Normal probability plot for measurement codes 20 and 30 of Magnet object for measurements of one repetition for measurement scenario 1 (n = 25). Calculated p-values using Anderson Darling normality test are, respectively, 0.910 and 0.754.  
Figure 7: Normal probability plot for measurement codes 10, 100, 130, and 140 of PKR object for measurements of one repetition for measurement scenario 1 (n = 25). Calculated p-values using Anderson Darling normality test are, respectively, 0.743, 0.226, 0.588, and 0.968.  
with the \(C_{p}\) factor and \(6\sigma\) but, in many cases, suggests a similar trend. Additionally, outliers, which are not seen on \(\sigma\), can be seen on the \(R\) graphs.
# Signature-Based Security Analysis and Detection of IoT Threats in Advanced Message Queuing Protocol
## 5 Attack Description and Explanation
### Explanation of Man-in-the-Middle (MiTM)
#### 5.3.2 Traits and Signature  
Several features were identified as indicators of an AMQP MiTM attack. These include the following:  
* **Severity Warnings**: Triggered by ARP protocol warnings due to duplicate IP and MAC addresses.
* **Echo Requests and Replies**: Used by the attacker to establish communication and monitor responses.
* **Frame Length**: Consistently measured at 60 bytes.
* **Syslog Messages**: Alerts about changes to AMQP IP address states.  
These features are often used in attack signatures to detect potential MiTM attacks, as they reflect suspicious or malicious network behavior. Additional indicators, such as anomalies in routing tables or unexpected changes in MAC address tables, further support the identification of MiTM activity.  
Table 3 summarizes the abnormal traffic features observed during the AMQP MiTM attack, forming the basis for its signature.
## 4 Tropospheric profiling with neural networks  
As previously described, to solve the atmospheric profiling problem of GPS LEO occultations overcoming the need of external information, we have considered different neural network approaches for both desert and vegetation databases. For each database and each approach, we have trained three neural networks where predictors are the total refractivity profiles \(N\) computed from RO data using eq. (1), and the targets are the corresponding dry \(N_{d}\) and wet \(N_{w}\) refractivity profiles and the dry pressure profiles \(P_{d}\) computed from ECMWF data.  
Over desert area between Tropics, the neural network training and the following independent test were performed by using 88 profiles for the training and the remaining 10 for the independent test, that represent 90% and 10% of the entire desert data set.  
Over vegetation area between Tropics, we have proceeded in the same way by choosing 312 profiles for the training and the remaining 35 for the independent test.  
First, before the training phase, we have pre-processed input and target features, with columns representing atmospheric parameter profiles, standardizing each row's means to 0 and standard deviations to 1 [11]. After standardization, we have exploited different approaches for the neural networks learning, as described in the following section.
## 3 Results
### Histomorphometric Findings
#### 3.4.2 Residual Graft  
The highest number of residual grafts in all defect types in comparison of groups was observed in group L, followed by group C and group H groups, respectively. The assessment of graft applications indicated a fairly close number of residual grafts in all three vitamin D levels, as shown in Table 5. Regarding the differences, only group H in the AuG group was observed to have a statistically significantly low number of residual grafts compared to the XG group (AuG 0.39 \(\pm\) 0.04, XG 0.42 \(\pm\) 0.04; \(p\) = 0.044).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**New Bone Formation (mm2)**} & \multicolumn{2}{c}{**High Vit D (H)**} & \multicolumn{2}{c}{**Normal Vit D (C)**} & \multicolumn{2}{c}{**Low Vit D (L)**} & \\  & & **(n = 12)** & **(n = 12)** & **(n = 12)** & **(n = 12)** & **\(p\)** \\ \hline \({}^{1}\) Empty Defect (ED) & \begin{tabular}{c} Avg \(\pm\) Sd \\ Median (Q1–Q3) \\ \end{tabular} & 0.96 \(\pm\) 0.08 & 0.49 \(\pm\) 0.07 & 0.33 \(\pm\) 0.09 & \({}^{\text{a}}\) 0.001 ** \\ \({}^{2}\) Autegonous Graft (AuG) & \begin{tabular}{c} Avg \(\pm\) Sd \\ Median (Q1–Q3) \\ \end{tabular} & 1.33 \(\pm\) 0.08 & 0.53 \(\pm\) 0.14 & 0.42 \(\pm\) 0.09 & \({}^{\text{a}}\) 0.001 ** \\ \({}^{2}\) Autegonous Graft (AuG) & \begin{tabular}{c} Median (Q1–Q3) \\ \end{tabular} & 1.3 (1.3–1.4) & 0.5 (0.5–0.5) & 0.4 (0.3–0.5) & \({}^{\text{H}}\) \textgreater{} C.L \\ \hline \({}^{3}\) Xenogreft (XG) & \begin{tabular}{c} Avg \(\pm\) Sd \\ Median (Q1–Q3) \\ \end{tabular} & 1.46 \(\pm\) 0.08 & 0.61 \(\pm\) 0.19 & 0.39 \(\pm\) 0.08 & \({}^{\text{a}}\) 0.001 ** \\ \({}^{3}\) Xenogreft (XG) & \begin{tabular}{c} Median (Q1–Q3) \\ \end{tabular} & 1.5 (1.4–1.5) & 0.5 (0.5–0.6) & 0.4 (0.3–0.5) & \({}^{\text{H}}\) \textgreater{} C.L \\ \hline \({}^{4}\) Allograft (AlG) & \
\begin{tabular}{c} Avg \(\pm\) Sd \\ Median (Q1–Q3) \\ \end{tabular} & 1.48 \(\pm\) 0.07 & 0.66 \(\pm\) 0.19 & 0.40 \(\pm\) 0.06 & \({}^{\text{a}}\) 0.001 ** \\ \({}^{4}\) Allograft (AlG) &
\begin{tabular}{c} Median (Q1–Q3) \\ \end{tabular} & 1.5 (1.4–1.5) & 0.6 (0.6–0.7) & 0.4 (0.4–0.4) & \({}^{\text{H}}\) \textgreater{} C.L \\ \hline \({}^{\text{b}}\) p & 0.001 ** & 0.001 ** & 0.022 \({}^{\text{a}}\) & \\ \hline \({}^{\text{bb}}\) Post Hoc Test & & & & \\ \hline ED-AuG & 0.018 \({}^{\text{a}}\) & 0.429 & 0.007 ** & \\ \hline ED-XG & 0.002 ** & 0.013 * & 0.268 & \\ \hline ED-AlG & 0.001 ** & 0.003 ** & 0.027 * & \\ AuG-XG & 0.069 & 0.268 & 0.114 & \\ AuG-AlG & 0.014 * & 0.009 ** & 0.635 & \\ \hline XG-AlG & 0.527 & 0.040 * & 0.268 & \\ \hline \hline \end{tabular}  
* Mann–Whitney U test; \({}^{\text{b}}\) [PERSON] test and \({}^{\text{bb}}\) post hoc comparison [PERSON] test; \({}^{\text{a}}\)\(p\) < 0.05, ** \(p\) < 0.01.  
\end{table}
Table 4: Number of new bone formations in the groups.