esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Potential Challenges
### Mobility  
Users' mobility in NOMA networks becomes a crucial issue that has to be addressed in additional studies, particularly for vehicular systems. Numerous studies primarily consider static surroundings, rendering these models unsuitable for use in wireless or mobile situations. Mobility impacts the maximum user pairing because it results in constant changes in users' channel gains. Due to mobility, the semi-static NOMA channel requires to be further reprocessed to determine the maximal decoding order. Mobility also impacts user pairing in terms of the variety and quantity of components accessing a resource block. To make wireless NOMA work with the static frameworks that are currently available, new techniques are needed.
## 3 Satellite-Borne GPS Data Quality Analysis  
LEO satellites move at high velocities and operate in complex environmental conditions. The quality of satellite observation data plays a pivotal role in achieving precise orbit determination. Consequently, it is imperative to conduct a thorough analysis of observation data quality, as it directly reflects the receiver's performance. This analysis primarily relies on indicators such as the number of visible satellites, multipath error, data integrity rate, and other relevant metrics.  
In this regard, we meticulously examine the quality of satellite-borne GPS data, aiming to assess the transmission capabilities and operational status of the \"HY2_Receiver\". This assessment forms a crucial foundation for the ongoing development of indigenous GPS receivers, offering valuable scientific insights. To delve into these indicators in a comprehensive manner, we employ TEQC [24] as our analytical tool.  
In this study, the on-board GPS data from the HY-2B and HY-2C satellites (2020 DOY349-355) and the HY-2D satellite (2021 DOY263-269) were used for experiments. While the data acquisition periods differ, Figure 3 shows that the solar F10.7 index exhibits a similar overall trend during both time periods. Although there are some variations, such as a peak in September 2021 compared to a relatively low level in December 2020, these differences are negligible and have an almost identical impact on the precise orbit determination of the HY-2B/2C/2D satellites [25].  
Figure 2: Attitude variation features of HY-2B/2C/2D satellites.
# Analysis of Seaweeds from South West England as a Biorefinery Feedstock
## 3 Results and Discussion
### Pigments
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & **Chl-c** & **Chl-c** & **Chl-b** & **Chl-a** & **Focusanthin** & **a Castorne** & **b Castorne** & **b Castorne** & **Lutetia** & **Zexaurthin** & **Vocaurthin** & **Vocaurthin** & **Anthersanthin** & **Total Pigment** \\ \hline _[PERSON]_ & 0.0 & 110.6 & 150.0 & 2.3 & 0.8 & 8.4 & 340.0 & 1.5 & 80.2 & 17.3 & 0.0 & 422 \\ [PERSON] & 0.7 & 98.7 & 154.9 & 2.9 & 0.0 & 10.6 & 27.7 & 3.9 & 7.4 & 11.1 & 0.0 & 334 \\ [PERSON] & 0.7 & 2.8 & 76.6 & 3.6 & 4.1 & 2.8 & 15.2 & 0.8 & 0.0 & 0.4 & 0.0 & 129 \\ [PERSON] & 0.0 & 2.6 & 119.7 & 4.1 & 7.7 & 1.0 & 19.4 & 0.8 & 0.0 & 0.0 & 0.0 & 173 \\ [PERSON] & 0.0 & 1.6 & 26.3 & 0.9 & 0.6 & 1.4 & 5.0 & 0.8 & 0.0 & 0.0 & 0.0 & 37 \\ [PERSON] & 0.0 & 2.0 & 213.1 & 6.2 & 12.1 & 11.0 & 56.4 & 1.1 & 0.0 & 1.5 & 0.0 & 315 \\ [PERSON] & 5.7 & 4.0 & 179.0 & 22.6 & 5.7 & 7.9 & 29.4 & 2.0 & 0.0 & 1.4 & 2.5 & 270 \\ [PERSON] & 0.0 & 10.1 & 133.6 & 1.9 & 11.6 & 5.9 & 33.9 & 0.0 & 1.2 & 0.7 & 0.0 & 271 \\ Callbright spp. & 0.9 & 5.9 & 197.5 & 4.6 & 9.1 & 96.4 & 4.1 & 0.8 & 0.0 & 0.0 & 0.0 & 283 \\ [PERSON] & 99.2 & 2.1 & 473.5 & 292.7 & 0.0 & 35.7 & 0.7 & 10.2 & 0.0 & 80.2 & 38.6 & 1045 \\ [PERSON] & 52.8 & 0.0 & 379.5 & 195.7 & 0.0 & 27.0 & 0.3 & 3.1 & 0.0 & 64.9 & 21.0 & 789 \\ [PERSON] & 32.1 & 0.0 & 62.5 & 131.8 & 0.0 & 13.4 & 0.4 & 1.5 & 0.0 & 53.7 & 14.9 & 313 \\ [PERSON] & 65.4 & 0.0 & 387.1 & 220.8 & 0.0 & 20.6 & 0.3 & 2.7 & 0.0 & 57.4 & 28.9 & 801 \\ [PERSON] & 73.0 & 0.0 & 462.6 & 289.2 & 0.0 & 17.7 & 0.5 & 7.7 & 0.0 & 36.9 & 34.3 & 945 \\ [PERSON] & 135.0 & 0.0 & 612.7 & 304.0 & 0.0 & 46.8 & 0.5 & 6.8 & 0.0 & 106.3 & 54.1 & 3139 \\ C.-proprpritan & 8.5 & 1.0 & 70.7 & 38.8 & 0.0 & 1.9 & 0.9 & 1.0 & 0.0 & 6.6 & 4.7 & 135 \\ G. Gustav & 1.8 & 0.0 & 337.0 & 7.6 & 6.4 & 27.1 & 71.3 & 11.8 & 0.0 & 0.0 & 1.3 & 484 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Primary pigments of South West seaweed samples (ug/g dry biomass). Chlorophyll(chl)-c is given as the total of C-1, 2 and 3 subtypes. Antheraxanthin, a keto-carotenoid and yellow colorant, has a range of benefits to human and animal health. Due to the fact of its high demand in the pharmaceutical, nutraceutical, food and feed industries, there are major efforts to improve antheraanthin production from biological sources instead of synthetic ones [31]. None of the green and only one of the red seaweeds (_Rhodomela conferovides_) contained antheraxanthin and this was at a very low level of just 2.5 \(\upmu\)g/g. All of the brown seaweeds analysed contained antheraxanthin, but the content varied from 1.3 \(\upmu\)g/g in _Gastroclonium oovatum_ to 54.1 \(\upmu\)g/g in _Punctaria latifolia_. Overall, from a commercial perspective, _Punctaria latifolia_ has good levels of all the important pigments (with the exception of lutein) making it an ideal candidate for multi-pigment isolation. Lutein production would be best achieved with _Palmaria palmata_ which would also generate chlorophyll-a as a secondary pigment product.
## 3 Joint System Implementation
### Joint Signal Design
The two orthogonal FBMC chirp waveforms are generated by zero interleaving and shifting a single chirp spectrum as the input sequence. The proposed joint signal technique exploits the orthogonality of discrete frequency components, i.e., subcarriers. Assume the input sequence's spectrum is \(X(k)\) with \(N\) discrete spectral components, separated by \(2\Delta f\) (see Figure 4). The input sequence \(X(k)\) is interleaved by \(N\) zeros, which is \(X_{jam}(k)\) in Figure 4; then, the interleaved input sequence is shifted by \(\Delta f\) for the second data sequence \(X_{com}(k)\). These data sequences are transformed into the time domain by the \(2N\)-point inverse discrete Fourier transform (IDFT). Finally, two waveforms modulated by the quadrature subcarrier set intershifted by \(\Delta f\) are obtained, and the demodulation process is realized by \(2N\)-point DFT. Note that both sets contain \(2N\) subcarriers, but only \(N\) subcarriers are used to carry the input data. Figure 1 shows the model considered in this paper. The joint system is ready for data communication based on achieving jamming to the incoming object. The functional trade-off is reflected in energy distribution for the joint signal designed with the orthogonal waveform regime. If the total transmit power remains the same, an increase in the energy of one of the signals will inevitably lead to a loss of energy of the other functional signal. In the joint waveform design, the communication signal energy mainly affects the BER, and the jamming signal energy mainly affects the jamming distance and jamming signal ratio (JSR). In the design, if the communication function is given priority, the energy resources can be more favorable to the communication signal; if the jamming effect is more important, more energy should be allocated to the jamming function.
## 4 Theoretical Analysis of Rigid Rotor Vibrations Amplitude in Rolling Bearings
### Radial Displacement of the Rotor Center Due to the Contact Deformations
#### 4.3.1 BSO Position of Support (Figure 4a)  
The dependence between the bearing deflection (\(w_{o}\)) and external radial load (\(Q\)) for the BSO position according to [22] can be obtained from the following equation (Figure 4a):  
\[Q=K\cdot\left[w_{o}^{n}+2\cdot\sum_{i=1}^{\frac{q-1}{2}}\left(w_{o}-a_{2i+1} \right)^{n}\cdot\cos^{n+1}(i\gamma)\right], \tag{12}\]  
where \(i\) is index of rolling element, \(K\) is effective coefficient of stiffness, \(w_{o}\) is bearing deflection, \(q\) is total number of rolling elements on which inner ring is supported, \(a_{2i+1}\) is boundary deflection of bearing when an \(i\)-\(th\) rolling element enters into the contact with the bearing rings, \(\gamma\) is angular spacing between rolling elements.  
Figure 4: The influence of the external load and contact deformations on the relative displacement between the bearing rings, (**a**) BSO position of support, (**b**) BSE position of support.  
The bearing deflection that is necessary for entering of the \(i\)-\(th\) rolling element into the contact with the bearing rings in a BSO position can be calculated using the equation [21]:  
\[a_{q,o}=\frac{e}{2}\cdot\left(\frac{1}{\cos\frac{q-1}{2}\gamma}-1\right)=t_{q,o} \cdot\frac{e}{2}, \tag{13}\]  
where \(t_{q,o}\) is the coefficient of the boundary deflection of bearing in the BSO position of inner ring support. The detailed derivation of the above equation and the value of coefficient \(t_{q,o}\), can be found in the reference [19,21].
## 5 Experimental Results and Analysis
### Experimental Dataset
#### 5.1.1 SCBehavior Dataset  
This dataset comprises 1346 high-resolution images of classroom scenes, each with varying distributions of student behaviors. The SCBehavior dataset encompasses seven different types of student behaviors: read, write, lookup, raise_hand, turn_head, stand, and discuss. These categories essentially cover the typical behaviors observed in daily classroom activities. The diverse distribution of these behaviors across different scenes enhances the dataset's robustness and applicability in real-world scenarios.  
In order to maintain the integrity and applicability of the SCBehavior dataset, we conducted a series of rigorous experimental studies on it. Our goal was to unambiguously affirm its value as a powerful educational resource that can significantly contribute to the development of automated systems for student behavior analysis, thereby improving teaching strategies and educational outcomes. Below are some example images from the SCBehavior dataset, illustrating the various student behaviors it captures, as shown in Figure 6.  
More specifically, the number of labels in our dataset and the division of the dataset into training, testing, and validation sets are shown in Table 2 below.
## 2 Our Approach
Then we first calculate the minimum integer \(n_{0}\) such that \(r_{0}\geq\frac{n_{0}+2}{n_{0}}\cdot C_{n_{0}+1},\) where \(C_{n_{0}+1}\) is the value given in the statement of Lemma 1. Next we construct a set \(S\) such that for any two numbers \(a\) and \(b\) with \(1\leq a\leq n_{0}\) and \(b\leq a+1\), there exist two triangles \(\Delta_{1}=\Delta(1,a_{1},b_{1})\) and \(\Delta_{2}=\Delta(1,a_{2},b_{2})\) in \(S\) such that the following conditions are met:  
(i) \(a_{2}<a\leq a_{1}\) and \(b_{2}<b\leq b_{1}\),  
(ii) there exists an algorithm for \(\Delta_{1}\) whose competitive ratio is \(r_{1}\), and  
(iii) \(r_{1}\cdot\max(\frac{a_{1}}{a_{2}},\frac{b_{1}}{b_{2}})\leq r_{0}\). We call such a set an \(r_{0}\)-_approx_ set. **Lemma 3**: _If one can construct an \(r_{0}\)-approx set \(S\), then there is an online algorithm for the \(2\)-server problem on three points, whose competitive ratio is at most \(r_{0}\)._  
_Proof._ Consider the following algorithm \(\mathcal{A}(a,b)\) which takes the values \(a\) and \(b\) of the triangle \(\Delta(1,a,b)\). Note that \(\mathcal{A}(a,b)\) is an infinite set of different algorithms from which we select one due to the values of \(a\) and \(b\). If \(a\geq n_{0}\), then we select the maximum integer \(n\) such that \(a\geq n\). Then \(\mathcal{A}(a,b)\) uses the algorithm for \(\Delta(1,n+1,n+2)\) of Lemma 1. Clearly we have \(a\leq n+1\) and \(b\leq n+2\). Therefore, by Lemma 2, the competitive ratio of this algorithm for \(\Delta(1,a,b)\) is at most (recall that \(C_{n+1}\) is the competitive ratio of this algorithm for \(\Delta(1,n+1,n+2)\) given in Lemma 1)  
\[\max\left(\frac{n+1}{a},\frac{n+2}{b}\right)\cdot C_{n+1}\leq\frac{n+2}{n} \cdot C_{n+1}\leq\frac{n_{0}+2}{n_{0}}\cdot C_{n_{0}+1}\leq r_{0}.\]  
By a simple calculation we have that \(\frac{n+2}{n}\cdot C_{n+1}=\frac{n+2}{n}\cdot\frac{\left(1+\frac{1}{n}\right)^ {n}-\frac{1}{n+1}}{\left(1+\frac{1}{n}\right)^{n}-1}\) monotonically decreases, which implies the inequality second to last. If \(a<n_{0}\), then we have the two triangles \(\Delta_{1}\) and \(\Delta_{2}\) satisfying the conditions (i) to (iii) above. Then we use the algorithm for \(\Delta_{1}\) guaranteed by condition (ii). Its competitive ratio for \(\Delta(1,a,b)\) is obviously at most \(r_{0}\) by Lemma 2. \(\Box\)
## 4 Analysis of Influential Factors
### Residual Ultimate Bearing Capacity  
To analyze the influence of the heating temperature on the residual bearing capacity of specimens, the heating temperature influence coefficient \(k_{2}(T)\) of bearing capacity was introduced:  
\[k_{2}(T)=\frac{N_{\text{u}}(T)}{N_{\text{u}}} \tag{1}\]  
where \(N_{\text{u}}(T)\) and \(N_{\text{u}}\) are the ultimate bearing capacity of the specimens subjected to high temperature and ambient temperature, respectively. Figure 8 shows the variation in the heating temperature influence coefficient of bearing capacity.  
As shown in Figure 8a, the influence of the heating temperature on the ultimate bearing capacity of the specimens was very obvious. As the heating temperature increased, the ultimate bearing capacity of specimens decreased continuously. Compared with the specimen under ambient temperature, the \(k_{2}(T)\) curve decreased slowly and not significantly (about 16.1-17.3%) within the range of 200 \({}^{\circ}\)C \(\leq T\leq\) 400 \({}^{\circ}\)C. Thereafter, the \(k_{2}(T)\) curve dropped sharply and its decline varied from 27.6% to 48.8%. If the reduction in residual bearing capacity is regarded as the approximately linear variation with the heating temperature, the average rate of decrease was 6.256%-100 \({}^{\circ}\)C\({}^{-1}\).  
Figure 8b reflects the influence of steel tube diameter (\(d\)) on the ultimate bearing capacity of the specimens. It can be seen that under both ambient temperature and high temperature (\(T\) = 600 \({}^{\circ}\)C), the ultimate bearing capacity increased with the increase in steel tube diameter. Compared with specimens with \(d\) = 89 mm, the ultimate bearing capacity of the specimens with \(d\) = 114 mm and 138 mm increased by 4.68% and 7.82%, respectively at ambient temperature, while it rose by 1.54% and 3.32%, respectively, under exposure to high temperature (\(T\) = 600 \({}^{\circ}\)C).  
Figure 8b indicates that the residual bearing capacity of the specimens after high temperature (\(T\) = 600 \({}^{\circ}\)C) exposure decreased with the increase in concrete cover thickness (\(a\)). The residual bearing capacity of specimens with \(a\) = 20 mm and 30 mm decreased by 7.65% and 11.82% compared with that of specimens with \(a\) = 10 mm. The main reason is that the concrete cover thickness basically failed at high temperature. When the concrete cover thickness was larger, the stirrup inner confining concrete was smaller, which reduced the bearing capacity of the outer RC and led to a reduction in overall bearing capacity of the specimens.  
Figure 8: Change laws of residual bearing capacity of specimens. (**a**) Influence of \(T\). (**b**) Influence of \(d\) and \(a\).
# A Compact Wearable Textile Antenna for NB-IoT and ISM Band Patient Tracking Applications
## 2 Antenna Design Methodology
### Specific Absorption Ratio (SAR) Analysis  
Since the antenna is designed for patient tracking applications, it must be safe for long-term on-body operation. Therefore, evaluated SAR values are evaluated for 1 gm and 10 gm cubic tissue of the proposed wearable antenna on the multi-layer human body phantom from the standpoint of safety justification at the 1.8 GHz, 2.45 GHz, and 5.8 GHz frequencies, as shown in Figure 21. Human safety standards like ICNIRP guidelines and FCC [26] propose that the maximum average SAR (ASAR) value for 1 gm/10 gm of the cube of human tissue needs to be lower than the 1.6/2.0 W/kg. At 2.45 GHz and 5.8 GHz frequencies, the 1 gm and 10 gm ASAR (in W/kg) of the proposed antenna is shown in Table 5 (at 1 W of input power). At 1.8 GHz, 2.45 GHz, and 5.8 GHz, the 1 gm/10 gm average SAR values are 0.0796/0.0759, 0.0575/0.0552, 0.0226/0.0204, respectively. All the values of SAR are smaller than the standard values of 1.6 W/kg and 2.0 W/kg. In this case, 1 W of the input power is taken, but practically, all the wearable antennas need operating power in the mW range [35]. In that case, this antenna will have much smaller SAR values, which make it safe for body-worn applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency (GHz)** & **At 45 mm** & **At 35 mm** & **At 25 mm** \\ \hline
1.8 & 2.1 & 2.0 & 0.2 \\
2.45 & 4.5 & 4.6 & 3.2 \\
5.8 & 8.2 & 8.1 & 7.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boresight peak gain values (dBi) at different bending radii: 45 mm, 35 mm, and 25 mm.  
Figure 20: Simulated vs. measured efficiency of the proposed wearable antenna.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Frequency** & **Simulation** & **Measured** \\
**(GHz)** & **(Chest Phantom)** & **(on Human Chest)** \\ \hline
1.8 & 3.7 & 2.8 \\
2.45 & 5.3 & 4.6 \\
5.8 & 9.6 & 8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Boresight peak gain values (dBi).
## 4 Integrated Reflectance and Transmittance Imaging
### Image Acquisition and Pre-Processing
The correction for transmittance can be made, assuming the sample has a circular cross-section shape in the direction of light transmitting, using the following equation:  
\[T_{c}=T_{r}\times\left(d/\overline{d}\right)^{s} \tag{6}\]  
where \(T_{c}\) is the diameter-corrected transmittance, \(T_{r}\) is the uncorrected transmittance obtained from the in-line light calibration, \(d\) is the diameter of a sample, \(\overline{d}\) is the mean diameter of all tested samples and s is adjustable scaling factor. A typical \(s\) value is 1.3 for cucumbers [86]. However, no diameter correction is needed for the reflectance measurement. Figure 14: Examples of integrated hyperspectral reflectance and transmittance images and their spatial and spectral profiles (left panel: no sample on the conveyor belts; right panel: cucumbers on the conveyor belts): (**a**) hyperspectral images; (**b**) spatial profiles corresponding to the horizontal dashed lines (**a**); (**c**) spectral profiles corresponding to vertical dashed lines in (**a**). Reproduced with permission from [82], Copyright Springer, 2008.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
###### Abstract  
Kalmogorov-Arnold Networks (KANs) are a novel class of neural network architectures based on the Kolmogorov-Arnold representation theorem, which has demonstrated potential advantages in accuracy and interpretability over Multilayer Perceptron (MLP) models. This paper comprehensively evaluates the robustness of various KAN architectures--including KAN, KAN-Mixer, KANConv_KAN, and KANConv_MLP--against adversarial attacks, which constitute a critical aspect that has been underexplored in current research. We compare these models with MLP-based architectures such as MLP, MLP-Mixer, and ConvNet_MLP across three traffic sign classification datasets: GTSRB, BTSD, and CTSD. The models were subjected to various adversarial attacks (FGSM, PGD, CW, and BIM) with varying perturbation levels and were trained under different strategies, including standard training, adversarial training, and Randomized Smoothing. Our experimental results demonstrate that KAN-based models, particularly the KAN-Mixer, exhibit superior robustness to adversarial attacks compared to their MLP counterparts. Specifically, the KAN-Mixer consistently achieved lower Success Attack Rates (SARs) and Degrees of Change (DoCs) across most attack types and datasets while maintaining high accuracy on clean data. For instance, under FGSM attacks with \(\epsilon=0.01\), the KAN-Mixer outperformed the MLP-Mixer by maintaining higher accuracy and lower SARs. Adversarial training and Randomized Smoothing further enhanced the robustness of KAN-based models, with t-SNE visualizations revealing more stable latent space representations under adversarial perturbations. These findings underscore the potential of KAN architectures to improve neural network security and reliability in adversarial settings.  
adversarial attacks; robustness; Kolmogorov-Arnold networks (KANs); multilayer perceptrons (MLPs); convolutional-KANs; KAN-Mixer Article
## 2 Materials and Methods
### Surface Characterization  
The surface chemical structures after the chemical surface modification was firstly investigated by a TENSOR 27 (Bruker, Karlsruhe, Germany) ATR-FTIR with a grazing angle reflector and the high-sensitivity detector. The experiment was carried out under atmospheric pressure and at room temperature. The infrared spectra between 4000-650 cm\({}^{-1}\) was plotted.  
In order to further examine the surface chemical structures of the pristine magnesium alloy and the chemical conversion layers, X-ray photoelectron spectroscopy (XPS, VG Scientific, East Grinstead, UK) was utilized to detect the surface atomic concentrations of the different samples. The focused monochromatic Mg K\({}_{\alpha}\) X-ray source was used for excitation. The experiment was carried at 14 kV and the measurement pressure was \(8\times 10^{-8}\) Pa. The take-off angle was kept at 30\({}^{\circ}\) and the atomic information between 0 and 1200 eV was recorded. The surface atomic concentrations were calculated through the software provided by the XPS supplier (Avantage XPS).  
The water contact angle was used to characterize the surface hydrophilicity and the experiments were carried out on a DSA25 contact angle goniometer (Kruss GmbH, Nurnberg, Germany) at room temperature. A sessile drop method was applied to measure the water contact angle. After cleaning and drying the samples, 10 \(\upmu\)L of distilled water was dropped on the substrate and the liquid drop image was immediately taken. The water contact angle was obtained by the software provided by Kruss GmbH (Drop Shape Analysis, DSA Version 1.92, Kruss GmbH, Nurnberg, Germany). Five parallel samples were measured and the values were averaged and expressed as mean \(\pm\) standard deviation (SD).