esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### NMR Analysis of Carbon Sources  
The \({}^{1}\)H-NMR results of the original PS0 sample are shown in Figure 4a-c and are consistent with documented PS spectra in the literature [44]. The strong signals observed between 6.0 and 7.3 ppm  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(M_{\mathrm{w}}\) & \(M_{\mathrm{n}}\) & \(D\) (\(M_{\mathrm{w}}/M_{\mathrm{n}}\)) \\ \hline PS0 & 65,000 & 22,000 & 3.0 \\ PS1 & 57,000 & 19,000 & 3.0 \\ PS2 & 48,000 & 17,000 & 2.8 \\ PS3 & 42,000 & 14,000 & 3.0 \\ PS4 & 30,000 & 8000 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average molar mass (\(M_{\mathrm{n}}\)), average molecular weight (\(M_{\mathrm{w}}\)), and dispersity (\(D\)) of the PS0–4 samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Name** & \(T_{\mathrm{max}}\) (\({}^{\circ}\)C) & **Resolute (\%)** \\ \hline PS0 & 408/443 & 2.1 \\ PS1 & 411/441 & 3.9 \\ PS2 & 411/441 & 4.2 \\ PS3 & 411/439 & 4.3 \\ PS4 & 411 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The thermogravimetric parameters for PS1–4 samples.  
are attributed to pendant aromatic rings. The absence of any signals between 2.5 to 6.0 ppm indicates that no ester, alcohol, or halogen or vinyl functional groups are present. Signals at 1.4 and 1.8 ppm are the signals for the methylene and methane polymer backbone protons, respectively (assignments are shown in Figure 4a,c).  
The \({}^{13}\)C-NMR spectra (see Figure 5) show the carbon sources (PS0-4) are atactic polystyrene; this is clearly visible from inspection of both the methine (Figure 5b) and _ipso_ (Figure 5c) carbon regions of the spectrum. Assignments of the carbon signals are displayed on the spectra and are in agreement with the assignments of [PERSON] et al. [45] for the _ipso_ region and [PERSON] and [PERSON] [46] for the methylene region. The effect of thermo-oxidative degradation on the polymeric microstructure of the degraded samples (PS1-4) appears to be minor, with no major differences observable in either the proton or \({}^{13}\)C spectra from that of PS0. No signals are observed in the carbonyl region of the \({}^{13}\)C-NMR spectra of any of the samples (PS0-4). Additionally, there are no observable correlations within this region in the HMDC spectra. This is consistent with the relatively low acid number obtained from KOH titration (see Section 3.1).  
Figure 4: Stacked \({}^{1}\)H-NMR spectra results of the PS0 (original) sample and PS1–4 (thermally treated) samples. Labels, a–e, represent the positions of hydrogen atoms as shown on the PS diagram featured. Graph (**a**) shows the overall spectra; (**b**) displays 7.5–6.0 ppm; (**c**) details 3.0–0.5 ppm.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## Mining Closed Frequent Itemsets with Map-Reduce
### Reduce Step
#### 3.2.5 Soundness  
The mapper phase makes sure that the input to the reducer is a key which is a subset of items \(p\), and a set of all transactions that contain \(p\), denoted by definition \(T=g(p)\). The reducer first checks that \(sup(p)\geq minSup*|\mathcal{D}|\) by checking \(|T|\geq minSup*|\mathcal{D}|\) and then performs an intersection of all the transactions in \(T\), which by definition is the result of the function \(f(T)\), and outputs the result. So, by definition, all outputs are the result of \(f\circ g\), which is a closed frequent itemset.
###### Abstract  
Reducing the size of ambient magnetic flux trapping during cooldown in superconducting radio-frequency niobium cavities is essential to reaching the lowest power dissipation as required for continuous wave application. Here, it is suggested that applying an alternating magnetic field superimposed to the external DC field can potentially reduce the size of trapped flux by supporting flux line movement. This hypothesis is tested for the first time systematically on a buffered chemically polished (BCP) niobium sample before and after high temperature annealing, a procedure which is known to reduce flux pinning. External low-frequency (Hz-range) magnetic fields were applied to the samples during their superconducting transition and the effect of varying their amplitude, frequency and offset was investigated. A few results can be highlighted: The influence of the frequency and magnitude of the AC fields on the flux trapping in the untreated Nb sample cannot be neglected. The trapped flux seems to be homogeneously distributed, unlike the flux trapping in, e.g., lead (Pb), which is a type I superconductor. After annealing, the Nb sample shows practically no dependency of flux trapping on external AC fields. The trapped magnetic flux was measured by polarized neutron imaging, and calculations of trapped fields show good agreement with experimental results.  
SRF; superconductivity; niobium; meissner effect; trapped flux; polarized neutron imaging +
Footnote †: journal: Applied Physics Letters
# On a Novel Approach to Correcting Temperature Dependencies in Magnetic Observatory Data
## 3 Temperature Dependence in Data from the Saint Petersburg Observatory
### Typical Temperature Dependence in February 2021  
Temperature dependencies most often occur when the behavior of the temperature plot during a month does not have a more or less constant downward or upward trend, but rather has a pronounced minimum or maximum--in other words, has a non-monotonous character during the month. In 2021, such a temperature trend is the most evident in February 2021, as shown in Figure 2.  
In the example above, the G record clearly correlated with the temperature plot; as a result, a long-period component was added to the significant short-period G variations of small amplitude in this month. The situation looked especially critical at the beginning of the month when, due to the indicated effect, G values reached 0.6 nT, which exceeded the value of 0.5 nT adopted in observatory practice. The vertical lines in the figure mark the moments of fulfillment of absolute measurements. Still, applying these measurements through the observed baseline values did not lower G at the beginning of the month. The reason for this was that the daily baseline values were calculated by interpolating the observed baseline values, which were generated about once a week, with a smoothing spline. This function did not account for non-monotonous temperature variations between absolute measurements, which in turn led to undesirable distortions in the G graph.  
Figure 1: Observed baseline values of the three components (top three plots) and variometer sensor temperature (hourly means, bottom plot) at the time of absolute measurements at the Saint Petersburg Observatory (SPG) in 2021.  
Figure 2: Plots of G (blue) and variometer sensor temperature (orange) based on 1-min data in February 2021. Vertical lines indicate moments of absolute measurements.
## 1 Introduction
For initial training of the sub-network used for the edge pixel extraction, edge pixels are considered as positive samples, with the pixels at other locations being treated as negative samples. The inner pixels are then designated as positive samples, with the pixels at other locations as negative samples, for training the sub-network used for the inner pixel extraction. After the successful completion of the training, the neural network is able to extract the winter wheat field from the GF-2 images accurately. 3. Finally, a GF-2 image is segmented by the trained model. Because SegNet and DeepLab are classic semantic segmentation models of images, and, the working principles of these two models are very similar to our work, we choose these two models as the comparison model, and a comparison is performed with them to evaluate the accuracy of the segmentation results.
# Synchronization and Application of a Novel Hyperchaotic System Based on Adaptive Observers
## 4 Adaptive Observer Based Synchronization of the Proposed Hyperchaotic System
Equation (24) is then obtained:  
\[\dot{e}=(A-LC_{1})e+B(f(x)\ -f(\hat{x}))+B(g(x)\theta-g(\hat{x})\hat{\theta})- \frac{1}{2}\overline{k}B(y_{2}-C_{2}\hat{x})+B\eta-\alpha \tag{14}\]5 \[\dot{e}=(A-LC_{1})e+B(f(x)\ -f(\hat{x}))+B(g(x)\theta-g(\hat{x})\hat{\theta})- \frac{1}{2}\overline{k}B(y_{2}-C_{2}\hat{x})+B\eta-\alpha \tag{14}\]6 \[\dot{e}=(A-LC_{1})e+B(f(x)\ -f(\hat{x}))+B(g(x)\theta-g(\hat{x})\hat{\theta})- \frac{1}{2}\overline{k}B(y_{2}-C_{2}\hat{x})+B\eta-\alpha \tag{14}\]7  
If we modify (24) with the adaptation law given in (15) and the control law given in (17), we obtain (25):  
\[\dot{e}=(A-LC_{1})e+B(f(x)\ -f(\hat{x}))+B(g(x)\theta-g(\hat{x})\hat{\theta})- \frac{1}{2}\overline{k}B(y_{2}-C_{2}\hat{x})+B\eta-\alpha \tag{14}\]8  
From the inequality provided in (25), it can be deduced that the independent parameters \(\varepsilon_{f}\) and \(\varepsilon_{g}\) can be chosen to be sufficiently small, ensuring \(\dot{V}<0\) for all non-zero states. Consequently, the error dynamics presented in (14) can achieve asymptotic stability. Inother words, the drive system given in (10) and the response system given in (13) can be synchronized. In (24), not only the unknown Lipschitz constants \(k_{f}\) and \(k_{g}\), but also the unknown limits \(\delta_{\theta}\) and \(\delta_{\eta}\) can be encompassed within the constant \(k\), which can be adaptively tuned by the adaptation law provided in (16). Moreover, if the constants \(\varepsilon_{f}\) and \(\varepsilon_{g}\) are chosen to be sufficiently small, the stability of the error dynamics in (14) can be guaranteed. The initial concept of the control law can be traced back to the work by [PERSON] and [PERSON] [39]. Evidently, as the error \(y-C\hat{x}\) approaches zero, the magnitude of \(\alpha(t)\) will escalate indefinitely and render computational feasibility unattainable. In practical applications, we can substitute the control law \(r(t)\) with (26):  
\[\dot{e}=(A-LC_{1})e+B(f(x)\ -f(\hat{x}))+B(g(x)\theta-g(\hat{x})\hat{\theta})- \frac{1}{2}\overline{k}B(y_{2}-C_{2}\hat{x})+B\eta-\alpha \tag{14}\]9 \[\dot{\hat{\theta}}=l_{\theta}g(\hat{x})^{T}(y_{2}-C_{2}\hat{x}) \tag{15}\]0  
Here, \(\varepsilon\) is a sufficiently small positive constant [39]. Consequently, the state error will remain within a neighborhood of the origin. [PERSON] et al. [40] first proposed the idea of adaptation for unknown Lipschitz constants in order to manage chaotic systems. We expand on this idea in this thesis to synchronize a larger class of hyperchaotic systems that have external disturbances and parameter uncertainty. A control rule can be used to achieve synchronization if the hyperchaotic systems meet the rigorous positive real condition.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 2 Single-Model Nonlinear Filtering Algorithms  
Many real-world filtering problems are nonlinear due to the nonlinearities in the dynamic and measurement models. Measurements of the sonar, radar, electro-optical, GMTI radar, AMTI radar, synthetic aperture radar, over-the-horizon radar, passive radar, video, IRST sensor, unattended ground sensor, and 2D air traffic control radar are nonlinear functions of the target state [20; 40; 48]. The NCT model with an unknown turn rate and dynamic models involving atmospheric drag, as well as the gravitational forces of the earth, moon, sun, and other planets, are nonlinear in the target state [49]. The sensor measurement models are always discrete, as the data are gathered at specific intervals. A dynamic model can be continuous or discrete in time. The dynamic models for an earth-orbiting satellite [49], ballistic missile, and ballistic projectile are continuous in time and are described by stochastic differential equations [50]. These filtering problems are known as continuous-discrete filtering problems. Most filtering problems in target tracking are discrete in nature, where the dynamic and measurement models are both discrete. In this study, we consider discrete filtering problems. Next, we discuss the general single-model filtering.
# Planning-Level Optimisation of Headway Regularity
## 5 Sensitivity Analysis
For instance, if a primary disruption of 3 min occurs after optimisation, a \(-0.326\) reduction in PRDM is achieved in CASE 6, while smaller reductions are attained in the remaining four cases (\(-0.280\) in CASE 7, \(-0.262\) in CASE 8, \(-0.225\) in CASE 9 and \(-0.182\) in CASE 10). After applying the optimisation procedure, increasing the headway for the same values of the primary headway disturbance results in a worse headway regularity. That is, on lines with longer headway, the effects of the optimisation procedure, as measured by PRDM, are worse. \begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Headway (min)** \\ \hline
6 & 6 \\
7 & 7 \\
8 & 8 \\
9 & 9 \\
10 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Headway duration per case. Figure 10: Sensitivity of passenger travel time to changes in transport demand. As shown in Table 8 and Figure 12, changes in headway also exert a significant influence on passenger travel time. Using the earlier example, if a primary disruption of 3 min occurs after optimisation, passengers will experience \(-1.31\), \(-1.16\), \(-1.08\), \(-0.85\) and \(-0.56\) reductions in travel time in CASEs 6-10. Therefore, after applying the optimisation procedure, increasing the headway for the same values of the primary headway disturbance will result in a shorter passenger travel time. However, on lines with longer headways, the optimisation procedure will lead to smaller travel time savings. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variant**} & \multirow{2}{*}{**Case**} & \multicolumn{5}{c}{**Primary Headway Disturbance (min)**} \\ \cline{3-8}  & & **0** & **1** & **2** & **3** & **4** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Variant** \\  
Based on the results presented above, it can be surmised that there is a negative correlation between the headway values and the optimisation procedure effects. With respect to headway regularity, it is evident that increasing the primary headway disturbance increases the sensitivity of the optimisation procedure to the headway value, but not on passenger travel time.
## 1 Introduction
This method combines photoelectric scanning angle measurement sensing technology with the principle of spatial resection in photogrammetry in conjunction with an external receiver array [5; 19]. Accordingly, the proposed calibration method avoids the limitations associated with a multi-station layout. Positioning information is obtained by solving the unknown parameters of the model according to a nonlinear optimization approach using the Levenberg-Marquardt least-squares fitting algorithm [20; 21]. The remainder of this article is organized as follows. Section 2 presents the working principles and mathematical model of the wMPS. Section 3 presents a single station calibration model based on a combination of photoelectric scanning angle measurements and single image space resection in conjunction with an external receiver array, and the objective function solution process is discussed. The proposed approach is verified experimentally in Section 4. Finally, a brief overview of the work, concluding remarks, and a discussion of future research are presented in Section 5.
## 3 Optical Sensing with NDs
### Magnetic Sensing
The NV\({}^{-}\) center is suitable for intra-tissue imaging because it emits fluorescence in the near-infrared. Nearly 70% of its light emission lies in the near-infrared (NIR) window from 650 to 1350 nm (Figure 13) [87], where light has the maximum penetration depth in biological tissue [88]. Fluorescent commercial NDs where observed with optically detected magnetic resonance (ODMR) imaging through 1.5 to 5 mm thick sections of breast tissue of chicken. The idea behind this imaging scheme is to detect the 0-magnetic-field fluorescence signal, slicing the biological tissue with B gradient, as in normal magnetic resonance imaging, but collecting the red NV B-dependent fluorescence, see Figure 14. It is clear that the main advantage of fluorescent NDs with respect to a normal MRI is that the first one pumps and collects optical waves (with a potential resolution close to the wavelength of  
Figure 12: **(a)** Fluorescence spectrum of NDs in water under 532 nm Continuous-Wave (CW)-laser pumping. **(b)** Temporal evolution of the fluorescence intensity at 687 nm under 2 Hz magnetic modulation. The inset shows Fourier transformed frequency spectrum. **(c)** Normalized fluorescence spectrum of NDs in water (black) and its Fast Fourier Transform (FFT) spectrum (red) after demodulation (NDs concentration in water 0.1 \(\upmu\)g mL\({}^{-1}\)). The asterisk indicates Raman peaks of water. Reproduced with permission from [85], Springer, 2017. Figure 13: Fluorescence spectrum (red curve) of NDs superimposed on the near-infrared window of biological tissues. The black and gray curves are the absorption spectra of water (H\({}_{2}\)O), oxygen bound hemoglobin (HbO\({}_{2}\)), and hemoglobin (Hb), respectively. Reprinted with permission from [87], Elsevier, 2012.  
observation, i.e., sub-micrometric resolution) whereas in the usual MRI, the RF pumping and collection limits the resolution to millimeters. In the case of tumor diagnosis/imaging, this can mean earlier observation of the insurgence of the illness. Other magnetic detection schemes with NDs have been demonstrated, and comprehensive lists can be found in References [89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99].
# Artificial Intelligence (ChatGPT) and Bloom's Taxonomy in Theoretical Computer Science Education
## 4 Research and Methods
### Quantitative Analysis--Phase 2  
The standard data analysis of Experimental Test 2 was performed using the JASP tool including, the ANOVA analysis with additional post hoc tests.  
**Hypothesis 1**.: The overall results for the educational performance of AI and students are equal.  
ANOVA of overall results in ET 2 can be seen in Table 9. Descriptives of overall results in ET 2 are reported in Table 10.  
Descriptives plots of overall results in ET 2 are shown in Figure 27.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**typeAgg** & **N** & **Mean** & **SD** & **SE** & **Coefficient of Variation** \\ \hline GPT35 & 10 & 12.600 & 4.115 & 1.301 & 0.327 \\ GPT40 & 10 & 20.300 & 2.312 & 0.731 & 0.114 \\ HU & 105 & 21.524 & 8.899 & 0.868 & 0.413 \\ \hline \hline \end{tabular} Note. typeAgg factor has the following values: HU—human subject, GPT35 and GPT40, ChatGPT ver. 3.5 and 4. Descriptive characteristics of ET2 score.  
\end{table}
Table 10: Descriptives of overall results in ET 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subject** & **Number of Cases** \\ \hline GPT35 & 10 \\ \hline GPT40 & 10 \\ \hline HU & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Subjects sample—phase 2.  
Figure 27: Descriptive plots of overall results in the Experimental Test 2. (**a**) Box plot, (**b**) rain-cloud plot.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cases** & **Sum of Squares** & **Df** & **Mean Square** & **F** & \(p\) & \(\eta^{2}\) \\ \hline typeAgg & 728.942 & 2 & 364.471 & 5.270 & 0.006 & 0.080 \\ Residuals & 8436.690 & 122 & 69.153 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: ANOVA—overall results in ET 2.  
Based on the ANOVA and post hoc tests, we can reject the main hypothesis 1. The overall results in ET 2 in general categories (HU, GPT35, GPT40) differ. The post hoc tests show statistically significant differences, especially between group **HU and GPT35** bot. The plots also reveal that the overall results are better between humans and both GPT35 and GPT40. The results generally conform to the Stage 1 results, but there is no statistically significant difference between humans and GPT40 (but we have no option to differentiate students by performance in previous studies). In Stage 1, there were only significant differences between HUA and GPT40 and HUA, HUB and GPT35.  
**Assumptions checks and non-parametric tests**  
Since [PERSON]'s Equality of Variance test showed rejections of the hypothesis regarding equal variance (\(p<0.001\)), we also performed a Kruskal-Wallis test and non-parametric post hoc tests (Table 12). However, the resulting analysis showed similar results to standard tests after corrections, as seen in pbonf and pholm values (Bonferroni and Holm corrections).
## 2 Material and Methodology
### Linear Cutting Machine (LCM) Test
 \\ \hline Granite \#1 & 4 & 40 & 122.0 & 5.8 & 4.17 & 30.50 \\ Granite \#1 & 6 & 60 & 184.2 & 10.1 & 3.76 & 30.70 \\ Granite \#1 & 8 & 60 & 212.5 & 17.2 & 3.50 & 26.56 \\ Granite \#2 & 4 & 48 & 74.0 & 5.6 & 3.20 & 18.50 \\ Granite \#3 & 4 & 40 & 90.8 & 5.5 & 3.46 & 22.70 \\ Granite \#4 & 5 & 70 & 171.3 & 2.6 & 0.74 & 34.26 \\ Granite \#5 & 4 & 72 & 32.9 & 4.2 & 1.46 & 8.23 \\ Granite \#5 & 6 & 108 & 55 & 6.9 & 1.06 & 9.17 \\ Granite \#5 & 8 & 144 & 62.4 & 8.0 & 0.69 & 7.80 \\ Granite \#6 & 3 & 75 & 75.1 & 3.7 & 1.64 & 25.03 \\ Granite \#6 & 5 & 75 & 85.7 & 2.6 & 0.69 & 17.14 \\ Granite \#6 & 7 & 75 & 94.8 & 8.4 & 1.60 & 13.54 \\ Diorite & 5 & 70 & 126.2 & 14.1 & 4.03 & 25.24 \\ Diorite & 7 & 70 & 129.8 & 17.7 & 3.61 & 18.54 \\ Feliste & 5 & 70 & 94.6 & 9.2 & 2.63 & 18.92 \\ Feliste & 7 & 90 & 180.6 & 21.4 & 3.40 & 25.80 \\ Gneiss \#1 & 5 & 70 & 119.7 & 11.1 & 3.17 & 23.94 \\ Gneiss \#2 & 4 & 48 & 89.7 & 7.8 & 4.08 & 2
# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 4 Results
### Training and Validation Loss Curves  
The training and validation loss curves illustrated in Figure 12 show how each model learns and generalizes over time. Deep learning models like ResNet50 and VGG16 show a steep decline in both training and validation loss, with curves that closely align--indicating efficient learning and minimal overfitting. ResNet50, in particular, exhibits the lowest loss values across all epochs, converging quickly and maintaining stability, which reflects strong feature learning and generalization. In contrast, traditional models such as Logistic Regression and SVM demonstrate slower loss reduction and higher validation losses, suggesting limited ability to capture the complex patterns in image-based spam detection. Ensemble models, XGBoost and LightGBM, show consistent performance with gradual loss reduction, though LightGBM displays a slightly wider gap between training and validation loss, hinting at some overfitting. In terms of overall performance, based on the loss curves, ResNet50 ranks the highest, demonstrating superior convergence and minimal generalization error. XGBoost follows closely, with low and stable losses and  
Figure 11: ROC curve comparison.  
strong consistency between training and validation. VGG16 ranks third, performing well but slightly more sensitive to validation fluctuations. LightGBM comes next, showing good learning but with signs of mild overfitting. Traditional models trail behind, with Logistic Regression outperforming SVM; however, both showing limited adaptability to the image classification task. This ranking aligns well with the quantitative results reported in this paper, including AUC scores and confidence intervals, further validating ResNet50 and XGBoost as the most robust models for image spam detection.