esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate preoperative distinction between in situ and invasive Breast Cancer (BC) is critical for clinical decision-making and treatment planning. Radiomics and Machine Learning (ML) have shown promise in enhancing diagnostic performance from breast MRI, yet their application to this specific task remains underexplored. The aim of this study was to evaluate the performance of several ML classifiers, trained on radiomic features extracted from DCE-MRI and supported by basic clinical information, for the classification of in situ versus invasive BC lesions. In this study, we retrospectively analysed 71 post-contrast DCE-MRI scans (24 in situ, 47 invasive cases). Radiomic features were extracted from manually segmented tumour regions using the PyRadiomics library, and a limited set of basic clinical variables was also included. Several ML classifiers were evaluated in a Leave-One-Out Cross-Validation (LOOCV) scheme. Feature selection was performed using two different strategies: Minimum Redundancy Maximum Relevance (MRMR), mutual information. Axial 3D rotation was used for data augmentation. Support Vector Machine (SVM), K Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) were the best-performing models, with an Area Under the Curve (AUC) ranging from 0.77 to 0.81. Notably, KNN achieved the best balance between sensitivity and specificity without the need for data augmentation. Our findings confirm that radiomic features extracted from DCE-MRI, combined with well-validated ML models, can effectively support the differentiation of in situ vs. invasive breast cancer. This approach is quite robust even in small datasets and may aid in improving preoperative planning. Further validation on larger cohorts and integration with additional imaging or clinical data are recommended.  
**Keywords:** breast cancer; radiomics; machine learning; artificial intelligence
# Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints
## 3 Main Result
### A Predict Method Compensating Delay Dynamic of Fin Stabilizers
The order \(p\) of AR model is unknown, so it is necessary to determine \(p\) and fit a more reasonable AR model to predict the roll angles accurately for the participation in the controller design. There is no strict criterion to confirm the order \(p\) of the model. Increasing the order can reduce the mean square deviation between the model and the sample data, making the model closer to the sample data, but too high order will introduce the disturbance, which does not mean that it can better describe the real sequence. There are some criteria for the selection of order, and the more famous one is Akaike criterion (AIC) [35]. It is generally more appropriate to describe the motion of the ship on the wave above order 4 and below order 8 [36].
## 5 Conclusions  
Based on the conducted research and statistical modeling, it was found that:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **X\({}_{2}\)** & **X\({}_{5}\)** & **MAPE** & **Prediction** & **Reality** \\  & **[-]** & **[-]** & **[\%]** & **[km]** & **[km]** \\ \hline \hline \multicolumn{5}{c}{Linear model} \\ \hline K1 & 250 & 158 & \(-\)0.39\% & 67.476 & 67.214 \\ K2 & 487 & 165 & 2.38\% & 79.545 & 81.481 \\ K3 & 307 & 168 & 5.21\% & 72.326 & 76.303 \\ K4 & 386 & 152 & 7.03\% & 72.056 & 77.507 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Brake pad wear prediction results for sample trucks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Linear Model** & **Power Model** \\ \hline R\({}^{2}\epsilon\) & 0.037 & 0.142 \\ \(\chi^{2}\alpha\) & 1.089 & 4.130 \\ \(\chi^{2}\alpha\) & 5.99 & 3.84 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Breusch–Pagan test results.  
The paper presents three models based on the multiple regression method. Only the linear model met all criteria. The inverse linear and power-law models did not meet the criteria.  
To predict the wear of brake pads of vehicles carrying oversized loads, a linear model can be used based on the variables X\({}_{2}\)--number of brake applications in the range of 0.15-0.24 [MPa] and X\({}_{5}\)--vehicle load range <20% GVW.  
Model validation showed that MAPE forecast errors ranged from \(-\)0.39% to 7.03%.  
The work shows how important it is to perform full verification of regression models. Verification should be based on meeting all Gauss-Markov assumptions.  
Additional achievement of the work--The methodology for building regression models presented in the work, based on statistical foundations, ensures correct verification and determination of the quality of the models. It should be used in all kinds of regression model creation tasks in engineering problems.  
In further work, it is planned to expand the selection of data for modeling to include long-distance transport trucks. Time will tell whether it will be possible to create a generalized model of brake pad wear in oversized and long-haul trucks.  
Conceptualization, G.B.; methodology, G.B.; software, S.F.; validation, S.F., G.B. and M.H.; investigation, G.B.; data curation, M.H. and N.P.; writing--original draft preparation, G.B.; writing--review and editing, S.F., G.B., M.H. and N.P.; visualization, N.P. All authors have read and agreed to the published version of the manuscript.  
Article processing charges were financed from the subsidy of the Ministry of Education and Science for the Agricultural University of Hugo Kollataja in Krakow for the year 2024.  
Not applicable.  
The data presented in this study are available within the article.  
The authors would like to thank [PERSON] and [PERSON] for providing the trucks for research.  
The authors declare no conflicts of interest.
# AG1478 Elicits a Novel Anti-Influenza Function via an EGFR-Independent, GBF1-Dependent Pathway
## 2 Results  
### A Small-Molecule EGFR Inhibitor, AG1478, Inhibited IAV Production Independent of EGFR Blockade  
To understand the effect of EGFR on IAV production, we initially screened various commercially available EGFR inhibitors (i.e., Gefitinib, BIBX1382, and AG1478) for their antiviral effects. Gefitinib is an FDA-approved TKI for lung cancer. BIBX1382 and AG1478 were discontinued for clinical trials and primarily used as chemical probes for studying EGFR function in the laboratory setting. To our surprise, only AG1478 demonstrated a potent antiviral effect in three different MOIs ranging from a repression by 8-fold at MOI = 0.1 to 21-fold at MOI = 1 (Figure 1A). We also checked a time course of AG1478 activity. At MOI = 0.1, daily dosing of AG1478 demonstrated a long-term anti-IAV activity up to at least 72 h post infection (Supplemental Figure S1). In contrast, neither of the other two EGFR inhibitors (Figure 1B,C) had any effect on IAV production. The lack of antiviral activity was certainly not due to the lack of EGFR inhibition as all three inhibitors significantly repressed EGFR autophosphorylation, an indicator of EGFR activation (Figure 1D). To verify this finding, we knocked down EGFR using EGFR-specific siRNA (siEGFR) (Figure 1E). siEGFR had no significant effect on IAV production as compared to the control siRNA (SiC) (Figure 1F). Thus, EGFR inhibition or knockdown appeared to have no antiviral effect.
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 4 Discussion  
### Monitoring Vegetation Phenology  
Monitoring vegetation remotely (e.g., satellite, phenocam, UAV) is key to understanding phenological controls on carbon fluxes. The two phenological datasets employed in this study contrasted in their spatial, temporal, and spectral resolutions. The main strengths and weaknesses of each are outlined below, alongside a discussion of some key problems to consider when comparing across phenology datasets.
# The Time Machine: Future Scenario Generation Through Generative AI Tools
## 1 Introduction  
### Scenario Planning  
Since the latter half of the 20 th century, organisations have increasingly sought methods to anticipate and navigate uncertainty. Scenario planning emerged as a pivotal strategic tool during this period, with companies like Shell pioneering its use to prepare for energy crises and unexpected geopolitical disruptions [1]. In a world where change remains the only constant, scenario planning has become an indispensable methodology for organisations striving to anticipate future developments and seize emerging opportunities. Constructing plausible future narratives grounded in current trends enables informed decision-making withoutresorting to overly deterministic predictions [1]. Beyond visualising diverse potential futures, scenario planning facilitates the development of adaptive strategies, equipping organisations to respond effectively and swiftly to dynamic challenges [2].  
Moreover, scenario planning extends beyond predictive functions. It serves as a reflective tool that enables organisations to examine their core values and aspirations through narratives of what might happen and what the company might do, fostering a socially and ethically responsible direction [3; 4]. This reflective dimension promotes decision-making that considers long-term consequences and emphasises social responsibility [5]. Identifying and evaluating trends, such as the rapid advancement of digital technologies, is critical in constructing robust and adaptive scenarios. Understanding these forces positions organisations to better prepare for future complexities [6]. Additionally, the collaborative nature of scenario planning strengthens organisational learning by creating a shared vision among participants and stakeholders [4; 7]. Such collective engagement enhances the understanding of change dynamics, enabling adaptation to varied circumstances and fostering more informed decision-making [8].  
However, traditional scenario planning methods often demand significant time and resources as they involve analysing multiple external forces and trends. These approaches may struggle to address contemporary complexities in rapidly evolving contexts and can prove excessively costly for smaller organisations and public administrations [9; 10]. To this end, emerging technologies present new opportunities to overcome these limitations, offering innovative solutions that could make scenario planning more accessible and efficient for resource-constrained entities.
## 4 Results and Discussion
### XRD Tests  
XRD is a powerful nondestructive method for symbolizing crystalline materials. It offers information on the structures, stages, preferred crystal locations (texture), and other structural factors. XRD peaks are formed by the productive interference of a monochromatic beam of X-rays distributed at definite angles from each set of lattice planes in an  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multicolumn{5}{c}{**Results**} \\ \cline{3-6}  & & **Natural** & **Conaminated** & **Stabilized with CEM II** & **Stabilized with OPC** \\ \hline pH & & 7.7 & 7.9 & 7.1 & 12.74 \\ Electrical conductivity & \(\mu\)mho/cm & 703 & 599 & 3160 & 4710 \\ Alkalinity & mg/L & 39 & 87 & 106 & 87 \\ Carbonate & mg/L & 0 & 0 & 0 & 87 \\ bicarbonate & mg/L & 39 & 87 & 106 & 0 \\ Sulfate & mg/L & 104 & 1B52 & 1320 & 20 \\ Chloride & mg/L & 132 & 104 & 65 & 52 \\ Total organic carbon & \% & 0.43 & 11.7 & 0.8 & 8.2 \\ Oil and Grease & \% & 0.52 & 14 & 0.96 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Chemical test results of the clean, contaminated, and tread soil samples.  
Figure 8: SEM photograph of 2 \(\mu\)m: (**a**) natural soil; (**b**) contaminated soil; (**c**) soil stabilized with CEM II; and (**d**) soil stabilized with OPC.  
illustration. The highest strengths are found using the atomic positions within the lattice planes. Accordingly, the XRD pattern is the print of periodic atomic arrangements in a specified material [52].  
XRD test results for the soil samples are illustrated in Table 8 and Figure 9. The pollutants changed the shape of Figure 9b, when compared with Figure 9a; silicon oxide and calcium carbonate values were increased in the polluted soil sample, when compared with the normal soil sample (Table 8). Furthermore, calcium aluminum silicate also increased, while silicon oxide and calcium carbonate decreased after both treatment methods. Additionally, treatment using CEM II is shown in Figure 9c. Using OPC for the treatment of the polluted soil sample affected the soil components, as shown in Figure 9d. Values of aluminum calcium silicon, magnesium aluminum silicate, iron silicate hydroxide, magnesium dialuminium disilicide-U1, and sodium aluminum silicate hydrate increased in the treated samples (Table 8). The obtained results shown in Table 7 are in coincidence with the illustration of XRD results. The variation of values in Figure 9a-d agrees with the obtained results in Table 8. The present results agree with the published work of [PERSON] [53].
## 2 Materials and Methods
### Data Analysis  
Statistical analyses in this study were conducted using two different software programs. Initially, the distribution characteristics of the variables were assessed using the Kolmogorov-Smirnov test via GraphPad Prism (version 9.5.1; GraphPad Software, San Diego, CA, USA). Descriptive statistics, including maximum, minimum, mean, and standard deviation values, were also calculated for all variables. Since the data did not follow a normal distribution, the relationships among the variables were examined using the non-parametric [PERSON]'s correlation analysis. A significance level of \(p<0.05\) was adopted for all statistical tests. Regression analyses were conducted using IBM SPSS Statistics software (version 26.0; IBM Corp., Armonk, NY, USA). Given the non-normal distribution of certain variables, logarithmic transformations were applied where necessary to meet the assumptions of normality, linearity, and multicollinearity. Variables that met the regression assumptions were included in multiple regression analyses, and the results were reported accordingly. The statistical significance was set at \(p<0.05\), and 95% confidence intervals (CI) were reported.
## 4 Experimental Evaluation
### Cora Dataset  
Figure 7 illustrates the accuracy of all embedding methods and similarity measures with the Cora dataset and Table 6 shows the precision values for all methods.  
As observed in the figure, with the Cora dataset, although DeepWalk and Line show the _best_ accuracy among all embedding methods, their accuracies are _not_ tangible; Line outperforms DeepWalk in terms of recall, PRES, and F-score, while DeepWalk outperforms Line in terms of MAP and precision. NetMF and BoostNE show the worst accuracy among embedding methods in terms of all metrics. In the case of similarity measures, JPRank shows the _best_ accuracy and SimRank again shows the worst one in terms of MAP, precision, recall, PRES, and F-score. Now, by comparing the accuracy of Line with that of JPRank, it is observed that JPRank slightly _outperforms_ Line by 4.05%, 5.73%, 2.28%, 2.98%, and 3.05% in terms of MAP, precision, recall, PRES, and F-score, respectively; Table 7 shows the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **graphGAN** & **Line** & **NERD** & **NetMF** & **node2 vec** & **JacSim** & **SimRank*** & **SimRank** \\ \hline
0.085 & 0.045 & 0.161 & 0.101 & 0.047 & 0.100 & 0.049 & 0.170 & 0.116 & 0.090 & 0.125 & 0.069 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Precision values with the BlogCatalog dataset.  
Figure 6: Accuracy of embedding methods and similarity measures with the BlogCatalog dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **ATP** & **BoostNE** & **DeepWalk** & **DWNS** & **graphGAN** & **Line** & **NERD** & **node2 vec** & **JacSim** & **SimRank*** & **SimRank** \\ \hline MAP & 209.77 & 1010.00 & 14.83 & 154.20 & 1068.42 & 163.24 & 838.03 & 121.26 & 179.83 & 73.44 & 365.73 \\ precision & 100.72 & 280.94 & 5.22 & 68.06 & 286.49 & 69.75 & 246.76 & 46.55 & 89.36 & 35.68 & 147.22 \\ recall & 162.84 & 456.31 & 8.73 & 99.65 & 519.46 & 114.61 & 293.81 & 88.18 & 125.15 & 42.89 & 245.18 \\ PRES & 168.22 & 617.50 & 9.68 & 112.59 & 682.73 & 128.99 & 431.48 & 101.17 & 145.30 & 50.00 & 286.10 \\ F-score & 147.01 & 418.26 & 9.88 & 106.51 & 466.07 & 113.95 & 344.39 & 88.32 & 135.98 & 47.10 & 222.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy improvements (%) by NetMF over other methods with the BlogCatalog dataset.  
percentage of improvements in accuracy obtained by JPRank over all other methods with the Cora dataset.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 6 Performance Evaluation
### Behaviour with Higher Initial Energy  
Due to the excessive simulation time when using a realistic battery budget and network lifetime that increases almost linearly [26], we predict the lifetime improvement for TMON and CIT-1 using the linear interpolation method. For TMON, we simulate using 0.5 and 1 Joules of initial energy. For CIT-1, we use 2.5 and 5 Joules. Based on the simulation results with these small amount of energy, we  
Figure 14: CIT-1’s percentage of dead nodes over time.  
Figure 15: Increased lifetime for deployments with multiple rooms.  
predict the lifetime of the network using the real energy storage, _i.e._, around 10,000 Joules for two AA batteries.  
The increased lifetime for the two deployments is illustrated in Figure 16. The text next to each bar in Figure 16 shows how much the lifetime was extended in time units. While the one-hop TMON deployment achieves up to around 9\(\times\) for greedy-maintain with reading correlation, the CIT-1 deployment only results in around a 1.5\(\times\) lifetime improvement. This happens because CIT-1 has smaller coverage redundancy among the nodes, and there are two bridge nodes, _i.e._, Nodes 13 and 14 in Figure 5, that can disconnect the left part of the network when they fail. The lesson learned from this experiment is that it is necessary to identify connectivity critical nodes by taking message paths into account. After identifying the critical nodes, such as Nodes 13 and 14 in Figure 5, one can deploy additional nodes as backups. Therefore, when critical nodes die, the network is still connected [25]. WSN-Maintain can be extended to incorporate this issue. It can then be used to re-deploy sensor nodes to achieve better coverage and better lifetime, given a coverage threshold and an expected lifetime.
## 2 Materials and Methods
### Cell Culture and Treatment  
The BJ cell line (CRL-2522(tm)) and the CACO-2 cell line (HTB-37(tm)) were provided by the American Type Culture Collection (ATCC, distributor: LGC Standards, Lomianski, Poland). The CACO-2 cells were cultured in MEM without phenol red with 10% of FBS containing 2.5 mM of L-glutamine, 1.2 g/L of sodium bicarbonate, and 0.1% of antibiotics.  
Figure 2: Synthesis of the Les-4368 and Les-4370 derivatives. Structures of the potential PPARy-agonist rosigiltazone and PPARy-antagonist (GW9662) used in the experiments.  
The BJ cells were cultured in DMEM without phenol red supplemented with 2 mM of L-glutamine, 10% of FBS, and 0.1% of penicillin/streptomycin. Both cell lines were maintained at a constant concentration of 5% CO\({}_{2}\) at 37 \({}^{\circ}\)C. The cells were seeded at a density of 4.5 \(\times\) 10\({}^{5}\) cells per well in 96-well plates (for the resazurin reduction assay, the H\({}_{2}\)DCF-DA assay, the LDH-release assay), 1.1 \(\times\) 10\({}^{5}\) cells per well in 12-well plates (for qPCR), and 1.2 \(\times\) 10\({}^{5}\) cells per well in 6-well plates (for Western blot). The BJ and CACO-2 cells were treated with increasing concentrations of Les-4368 and Les-4370 in the range between 1 nM and 100 uM for 24 h or 48 h (for assessment of metabolic activity, ROS production, and LDH release levels). In the case of the qPCR method, both tested cell lines were treated with an efficient but non-lethal concentration of Les-4368 (10 uM), Les-4370 (10 uM), and 1 uM of rosiglitzone for 6 h. To assess the engagement of PPAR\(\gamma\) in the biological effect of the tested 4-TZD derivatives at the protein level, the BJ and CACO-2 cells were exposed to 10 uM of Les-4368, 10 uM of Les-4370, 1 uM of GW9662, or 1 uM of rosiglitzone alone and in co-treatment with Les-4368 or Les-4370. Cells treated with DMSO served as controls; the amount of DMSO in the experiments was always the same and did not exceed 0.1%.
# Microscopic Multiple Fatigue Crack Simulation and Macroscopic Damage Evolution of Concrete Beam
## 3 Fatigue Modeling of Three-Point Bending Beams
### Fatigue Failure Analysis  
In order to perform fatigue failure analysis, it shall be defined that the ultimate fatigue load is reached once the nominal stiffness of the beam is only 5% left. The so-called macroscopic mechanical behaviors are substantially influenced by microscopic fatigue crack growth. A cycle-jump technique is employed to model load cycles such that the computational cost can be saved. In the simulations of regular cyclic loading scenarios with the same loading range, a cycle can be adopted to reduce the computational cost. As an example, the results at the stress level \(S=0.4\) are shown here. The three-point bending concrete beam is ruptured after 42,000 load cycles. Figure 9 shows crack propagation diagrams of the whole failure process, where \(N_{f}\) is the fatigue life of this specimen.  
There are naturally a large number of distributed microscopic cracks in concrete, usually occurring on the mortar-aggregate interfaces during cement hydration process. Under the fatigue load, microcracks in tensile zone of the beam start to stably grow at a medium rate (Stage II) according to Paris law, as shown in Figure 9a,b. As the number of fatigue load increases, crack coalescence initially takes place at the lower left corner of the specimen. Localized damage subsequently appears.  
Figure 8: Stress contour of micro-scale area of concrete beam.  
It shall be pointed out that, crack coalescence does not necessarily occur in the designated area. The localized damage at the lower left corner of the specimen here is a reflection of random distribution of microcracks in concrete. To be able to describe this stochastic behavior is the strongest point of our algorithm. Once macrorack appears, fatigue crack growth start to accelerate (but still in a stable fashion), gradually entering into Stage III, as shown in Figure 9c,d. The formation of macrocrack can be firstly seen in Figure 9c. Its further propagation will lead to final beam rupture. It should be noted that at this stage, cracks in other areas still grow at a low or medium rate. At later period of fatigue rupture, the neutral axis gradually moves up, the macrocrack on the left side propagates at a high rate, and the localized damage (on the upper left side) increases dramatically, which eventually leads to the rupture of beam, as shown in Figure 9e.  
It is noteworthy that the unsymmetrical pattern of crack formation and propagation is clearly displayed in the middle area of concrete beam. It is reflective of the microscopically stochastic phenomenon of crack distribution. This is the strong point that our proposed model is able to achieve.