esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Thermomechanical Properties  
The thermal degradation behavior is shown in Figure 10. The TGA curves showed the weight loss as a function of temperature under N\({}_{2}\) atmosphere. All the samples had the same structural units and almost the same degradation pattern. The degradation temperature was slightly higher for WPU\(-\)g\(-\)BTA\(-\)C. The thermograms showed two-step degradation; first, there was a decomposition stage (around 230-270 \({}^{\circ}\)C) due to the urethane bond degradation of hard segment; second, there was a degradation of the soft segment  
Figure 8: BODE plots for raw CRS electrode and CRS electrode coated with WPU\(-\)g\(-\)BTA\(-\)A, WPU\(-\)g\(-\)BTA\(-\)B, and WPU\(-\)g\(-\)BTA\(-\)C in 3.5 wt% NaCl(\({}_{\text{aq}}\)).  
polyol. For WPU\(-\)g\(-\)BTA\(-\)C, 5 wt% loss occurred in the range of 222-261.19 \({}^{\circ}\)C followed by 215-258 \({}^{\circ}\)C for WPU\(-\)g\(-\)BTA\(-\)A and WPU\(-\)g\(-\)BTA\(-\)B (Table 1).  
The mechanical properties of the WPU\(-\)g\(-\)BTA coatings with different cross-linking densities were determined (Table 1). DMA demonstrated the effect of temperature on various anticorrosive coatings. The storage modulus of all the samples is shown in Figure 11.  
The modulus of WPU-g-BTA-C was 171.4 MPa, which is higher than those of WPU-g-BTA-A and WPU-g-BTA-B at room temperature (20 \({}^{\circ}\)C). The hardening mechanism may be attributed to the various cross-linking density in the given samples. The as-prepared coatings showed a sharply decreased modulus. However, the WPU-g-BTA-C coating had a slightly reduced decreasing tendency of storage modulus, providing a good thermomechanical stability up to 50 \({}^{\circ}\)C. As shown in Table 1, the 100% modulus, stress at  
Figure 10: TGA curve of (black) WPU\(-\)g\(-\)BTA\(-\)A, (red) WPU\(-\)g\(-\)BTA\(-\)B, and (blue) WPU\(-\)g\(-\)BTA\(-\)C.  
Figure 9: O\({}_{2}\) permeability for CRS electrode coated with (red) WPU\(-\)g\(-\)BTA\(-\)A, (green) WPU\(-\)g\(-\)BTA\(-\)B, and (blue) WPU\(-\)g\(-\)BTA\(-\)B.  
break, and elongation at break of the WPU-g-BTA films increased with increasing chain length of the extenders. The tensile-strain behavior showed a stress of 51.2-47.56 kgf/cm\({}^{2}\), elongation at break of 178.85%-283.41%, and tensile modulus of 42.2-46.5 kgf/cm\({}^{2}\). This increase in properties may be associated with the increased cross-linking density due to the WPU-g-BTA chain end reaction with A, B, and C.
## 4 Experiments
### Evaluation Metrics
#### 4.3.2 Ablation Study  
To assess the contribution of each modality and the effectiveness of the proposed fusion mechanism, we conducted an ablation study using the UCF Crowd and Simulated Panic Behavior Datasets. The experiments include single-modality models (crowd density, trajectory patterns, and semantic cues), dual-modality combinations (density + trajectory, density + semantic, and trajectory + semantic), and the full multimodal system. In addition, to evaluate the impact of the fusion strategy, we compared the proposed attention-enhanced MLP fusion with a naive early concatenation baseline.  
Table 6 summarizes the results of the ablation study, highlighting the impact of different modality configurations and fusion strategies. The results demonstrate that the full model achieves the highest performance, with 91.7% accuracy, 88.2% F1-score, and 40 FPS inference speed, validating the benefit of multimodal integration. Removing any modality causes a significant performance drop, and semantic-only models suffer the most due to noise sensitivity. Among the dual-modality configurations, density + trajectory achieves the best trade-off, with 81.6% accuracy and 83.4% F1-score, highlighting the complementary nature of spatial and temporal cues.  
Furthermore, replacing attention-enhanced fusion with naive concatenation reduces the F1-score from 88.2% to 81.1%, confirming that attention-weighted feature aggregation substantially improves fusion effectiveness under heterogeneous input conditions.
## 4 Proposed Power Consumption Modes  
We have proposed a three state model, where the BSs of the network can be in any of the following three states:  
1. **Active Mode:** In active mode, the BS is in fully operational mode and consumes maximum power.
2. **Stand-by Mode:** In stand-by mode, the BS is in low power consumption non-operational mode, where it consumes a small amount of power but requires negligible wake-up time to go back to the active mode. Researchers from Ericsson company have reported that a BS consumes approximately 10 W power in stand-by mode and takes approximately 30 us to go into the active mode [5].
3. **Sleep Mode:** In sleep mode, the BS is totally switched off so that it consumes almost zero power, however takes longer time to wake up. The authors in [6] have reported that small cells can take tens of seconds to couple of minutes to wake up from sleep mode, where a macro BS takes 10-15 min of wake up time from sleep mode. Please note that there is some non-zero ultra-low power consumption during sleep mode, however this ultra-low power consumption is assumed to be negligible compared to the power consumed in active mode in this work. Hence sleep mode is treated as zero power consumption mode in this work.  
Table 3 outlines the delay required for a BS to remain in active mode or become active from stand-by and sleep mode.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Carrier frequency, \(f_{c}\) & 1800 MHz \\ Path Loss Exponent, \(\alpha\) & 2.5 \\ BS Transmit Power & 40 W \\ Shadowing Mean & 0 \\ Variance, \(\sigma\) & 7 \\ Total BSs, \(N\) & 3 \\ Antenna Gain & 10 dB and 2 dB \\ User arrival rate, \(\lambda\) & 0.01 \\ User death rate, \(\mu\) & 0.005 \\ \(\mathbb{T}_{max}\) & 40 W \\ Minimum data rate & 300 kbps \\ \(d\mathbb{I}\) & 4.7 \\ \(\mathbb{T}_{std}\) & 10 W \\ \(\mathbb{T}_{sLip}\) & 0 W \\ \(BWT_{T}\) & 6 MHz \\ \(b_{max}\) & 6 MHz \\ \(b_{min}\) & 2 MHz \\ BW per BS & 2 MHz \\ \(dt\) & 2 s \\ \(d_{0}\) & 1 km \\ \(c\) & \(3\times 10^{8}\) ms\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: BS’s parameters for the power consumption model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**BS Become Active** & **Required Time** \\ \hline From active mode & 0 s (delay group-1) \\ From stand-by mode & 30 us (delay group-2) \\ From sleep mode & 40 s (delay group-3) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Required time for a BS to become active (Hence approximate delay experienced by a new user).
# GAN-Based Map Generation Technique of Aerial Image Using Residual Blocks and Canny Edge Detector
## 2 Related Works
### Image-to-Image Translation
Similar to the purpose of the hourglass structure in maintaining detailed information, the proposed method leverages residual block structures to ensure the preservation of critical features in map generation. Thus, while StarGAN [8], CoMoGAN [9], [PERSON] et al. [10], and Hourglass Block-GAN [11] are related to the proposed method under the broader theme of image-to-image translation, their specific goals differ. The proposed method distinguishes itself by advancing beyond traditional image restoration and translation techniques, specifically targeting the generation of detailed and accurate maps, thereby presenting a novel approach that emphasizes detail preservation for geographic information.
## 4 Discussions  
Slope stability analysis is one of the most challenging fields in geotechnical engineering. Landsliding causes huge losses to human lives as well as the economy of a country. Normally slope stability analysis is performed in conventional ways, but as soil behavior is not homogenous, it is strongly recommended to analyze soil slopes using theoretical as well as practical approaches. Without the collection of samples and testing in a laboratory, it will not give accurate results. This research strongly recommends collecting soil samples from the required site, testing in a laboratory to compute the soil properties and then running the analyses. Furthermore, study the slope in all aspects, such as analyze it in a saturated condition, an unsaturated condition and apply dynamic loads as well. Moreover, other soil types also need to be considered in future work as fissured clays have a different response compared to non-fissured clays [36].  
Using Equations (9) and (10), the shear strength for any saturated and unsaturated slopes, respectively, can be computed, provided that the material properties are in the range of Table 1. For example, if the slope angle is 74 degrees and the shear stress is 49 kPa, then the shear strength for this specific case in a saturated condition will be approximately 32.22 kPa, and in an unsaturated condition, it will be 51.20 kPa. The factor of safety can be calculated using Equation (11) as follows:  
\[\text{Factor of safety}=\text{Shear strength}/\text{Shear stress} \tag{11}\]  
\[\text{Factor of safety in a saturated condition}=32.22/49=0.66,\]  
\[\text{Factor of safety in an unsaturated condition}=51.20/49=1.04,\]  
This equation can be extended to find the correlation of the same parameters while considering the horizontal and vertical seismic coefficients together. Although the vertical seismic effect in most of the cases is negligible, it affects the slope in some cases. Normally shear strength is computed using [PERSON]'s equation:  
\[\tau=\text{c}+\sigma\text{ tan }\Phi \tag{12}\]  
Combining Equation (12) with 9 and 10, a new correlation is developed as follows.  
\[\text{c}+\sigma\text{tan}\Phi=30.482-0.385\beta+0.617\sigma=49.637-0.606\beta+ 0.947\sigma \tag{13}\]  
All these developed correlations are not generalized equations that can be applied in all scenarios. These correlations can be applied only in specific conditions; the material properties must be in the range of Table 1.  
All other discussions regarding this research are mentioned in the result section, with each step of the study.
## 2 Simulation Method
The detailed shape of the angular scattering pattern is obtained by solving the scattering phase functions that describe the scattered field, \(E_{S}\). The scattered far field in spherical coordinates (\(E_{S\theta}\), \(E_{S\phi}\)) is given by  
\[E_{S\theta}=\frac{e^{ikr}}{-ikr}\cos\phi\cdot S_{2}(\cos\theta) \tag{11}\]  
\[E_{S\phi}=\frac{e^{ikr}}{ikr}\sin\phi\cdot S_{1}(\cos\theta) \tag{12}\]  
where \(E_{S\theta}\) is the far-field component in the scattering plane defined by the incident and scattered directions and \(E_{S\phi}\) is the orthogonal component. The angle \(\phi\) is the angle between the incident electric field and the scattering plane. \(S_{1}(\cos\theta)\) and \(S_{2}(\cos\theta)\) are the scattering phase functions that describe the angular scattering patterns and hence the polar distribution of Mle scattering by pure water, contaminated water, and black carbon. The scattering amplitudes are given by  
\[S_{1}(cos\theta)=\sum_{n=1}^{\infty}\frac{2n+1}{n(n+1)}(a_{n}\tau_{n}+b_{n} \tau_{n}) \tag{13}\]  
\[S_{2}(cos\theta)=\sum_{n=1}^{\infty}\frac{2n+1}{n(n+1)}(a_{n}\tau_{n}+b_{n} \tau_{n}) \tag{14}\]  
It is the functions \(\pi_{n}\) and \(\tau_{n}\) in these spherical harmonics that describe the angular scattering patterns of the incident light upon encountering the particulate matter. The functions \(\pi_{n}\) and \(\tau_{n}\) follow from the recurrence relations  
\[\pi_{n}=\frac{2n-1}{n-1}cos\theta\cdot\ \pi_{n-1}-\frac{n}{n-1}\pi_{n-2} \tag{15}\]  
\[\tau_{n}=n\cdot cos\theta\cdot\ \pi_{n}-(n+1)\pi_{n-1} \tag{16}\]  
The angular intensity distribution obtained from Equations (13)-(16) and the backscattering efficiency obtained from Equation (10) were plotted using Matlab. The calculations using the Mie theory as described above take in the complex refractive index, particle size and wavelength of excitation light as input parameters. The particles in the calculations are assumed to be independent single scatterers with spherical shapes excited by a homogeneous plane wave. In future work, the Mie theory can be extended to coated [37] and multilayer particles such as droplets with surface and/or internal contaminants [38].
## 2 Experimental Section
### Procedure for Wet Chemistry Isolation of NaI\({}^{211}\)At]At
A 5 mL quantity of DIPE-saturated 8 M HCl was added to the DIPE in the scintillation vial, and that biphasic mixture was stirred for 5 min. Following agitation, the aqueous HCl layer was again removed via pipet and counted in a dose calibrator. The preceding two steps were repeated an additional three times (for a total of 4 washes). The DIPE solution in the scintillation vial was counted in a dose calibrator to determine the \({}^{211}\)At activity remaining. Step 5: Back-Extraction into NaOH. A 600-700 \(\mu\)L quantity of 4 M NaOH (ThermoFisher, Waltham, MA, USA) was added to the DIPE in the scintillation vial, and the vial was stirred for 10 min. The pH of the basic aqueous \({}^{211}\)At solution was checked to confirm that it was strongly basic (pH \(>\) 13). The aqueous layer (containing the \({}^{211}\)At) was then removed and both layers were counted separately in the dose calibrator to determine the total amount of \({}^{211}\)At isolated. If the \({}^{211}\)At was to be used without a further purification, the isolated NaOH solution was neutralized (brought to pH 6.8-7.0) by addition of aliquots of HCl or NaOH solutions (4 M, 1 M, 0.5 M and 0.1 M) as required. Step 6: Distillation under reductive conditions. To a 25 mL round bottom flask containing 9.19 mL DI-water, 0.75 M ferrous sulfate heptahydrate (Sigma-Aldrich, St. Louis, MO, USA), in 1.5 M \(\mathrm{H_{2}SO_{4}}\) (ThermoFisher, Waltham, MA, USA) was added the basic \({}^{211}\)At solution. The resultant \({}^{211}\)At solution was stirred to re-dissolve the characteristic cloudy greenish precipitate, and the flask was connected to a cold-water jacketed distillation head. To the receiving side of the apparatus was attached a 10 mL round bottom flask pre-charged with 100 \(\upmu\)L of 1 M NaOH. The distillation assembly was lowered into a 25 mL round bottom aluminum heating block (and an ice bath for the receiving flask), the distilling flask and neck of the distillation head were covered with aluminum foil, and the \({}^{211}\)At solution was distilled at 200 \({}^{\circ}\)C (hot plate temperature) with gentle stirring. The collected \({}^{211}\)At, free of bismuth metal and other contaminants from the wet chemistry procedure, was used for radiolabeling experiments. In some studies, the aqueous solution was reduced in volume while heating (e.g., 80 \({}^{\circ}\)C) under an argon stream to provide a higher concentration of \({}^{211}\)At.
## 1 Introduction
### Physical Background  
In general, near-field communication is based on the principle of inductive coupling, where a sinusoidal current of amplitude \(I_{1}\) and angular frequency \(\omega\) is injected into a primary coil and thus generates a magnetic field of flux density \(B_{1}\). In a secondary coil spanning the area \(S_{2}\), \(B_{1}\) creates a magnetic flux (see Figure 1a)  
\[\Phi_{2}=\iint_{S_{2}}\vec{B}_{1}(\vec{r})\;d\vec{S}_{2}, \tag{1}\]  
which induces a voltage \(U_{2}\) according to [PERSON]'s law:  
\[U_{2}=-\frac{d\Phi_{2}}{dt}=-\iint_{S_{2}}j\omega\vec{B}_{1}(\vec{r})\;d\vec{ S}_{2}=j\omega M_{12}I_{1}. \tag{2}\]  
Here, the ratio of magnetic flux \(\Phi_{2}\) and primary current \(I_{1}\) is defined as the mutual inductance  
\[M_{12}=\frac{\Phi_{2}}{I_{1}} \tag{3}\]  
and is a measure of the coupling strength of primary and secondary coil. Moreover, the system is also characterized by the inductances \(L_{i}\) and the loss resistances \(R_{i}\) of the coils (with index \(i\in\{1,2\}\) denoting the coil number). The primary coil is driven by a voltage source with source impedance \(Z_{\text{S}}\), the secondary coil is loaded with a circuit of impedance \(Z_{\text{L}}\), resulting in the equivalent circuit of Figure 1b. In order to present all physical aspects being relevant to near-field data transfer systems, we need to account for the loaded input impedance, which is present at the terminals of the primary coil and is composed of the primary's self-impedance \(Z_{\text{self}}\) and the reflected impedance \(Z_{\text{r}}\) representing the power transfer to the secondary coil:  
\[Z_{\text{in}}=Z_{\text{self}}+Z_{\text{r}}=R_{1}+j\omega L_{1}+\frac{\omega^{ 2}M_{12}^{2}}{j\omega L_{2}+R_{2}+Z_{\text{L}}}. \tag{4}\]  
With the given physical background, two data transfer mechanisms can be established: The first category is classified as _load modulation_ or _load shift keying_ (LSK): The primary device continuously provides RF energy by a sinusoidal input voltage \(U_{0}\) with constant amplitude, while the secondary device transmits binary data by changing its load impedance \(Z_{\text{L}}\): By enabling or disabling additional resistive or capacitive branches, binary information ('0' or '1') is encoded. Consequently, the input impedance is modified according to (4), so that a change in the logic level reflects to a change in the envelope signal of \(U_{1}\). The second category of data transmission requires that every participating communication device _actively generates a modulated carrier signal_ by modifying the properties of its source voltage \(U_{0}\) (either by amplitude, phase or frequency modulation), translating to a change in the secondary's output voltage \(U_{2}\) (also modulated w.r.t. amplitude, phase or frequency) which is provided to the corresponding demodulation stage.
## 2 Literature Review
### The Software Packages  
The subspace method has been used in many structural monitoring and modal analysis software programs. In this subsection, the software packages that used subspace system identification for modal identification and SHM are further investigated. ARteMIS is a self-stand tool suite that utilized CC-SSI for operational modal analysis [132]. [PERSON] and [PERSON] [58] developed MACEC for modal analysis in TD and FD. SSI-COV, SSI-DATA, combined deterministic-stochastic subspace identification (CSI), and their reference-based generalization (SSI-data/ref, SSI/ref and CSI/ref) are adopted in the software package. MACEC 3.2 is the latest version of the software [133]. ModalVIEW [134] software was developed under LabVIEW which used subspace system identification algorithm for modal analysis. [PERSON] et al. [135] presented structural modal identification (SMI) and continuous structural modal identification (CSMI) for modal analysis within the LabVIEW environment. [PERSON] and [PERSON] [136] proposed COSMAD toolbox in Scilab, for in-operation damage identification that used SSI-COV as the basic identification tool in the software. [PERSON] et al. [137] introduced structural modal identification toolsuite (SMIT) to study the modal parameters of natural frequency, mode shapes, and damping ratio.  
Operational modal analysis (OMA) [138] is another software program that uses subspace system identification for the dynamic identification of structure and it has been used for the modal identification of several structures such as Berta Bridge [139] and Berke Arch Dam [140]. LMS Cada-X [141] is another software program employing subspace algorithm. The software is developed by LMS International in Leuven, Belgium. TestLab [142] is another software by LMS that was used extensively for modal analysis. The software also used a subspace algorithm for parameter identification. Automated operational modal analysis (AOMA) [143] utilized a strong identification and stabilization diagram. The algorithm uses one user-defined parameter.
## 4 Datasets
### Hollywood2  
Hollywood2 [56] action dataset was created by INRIA (Institut National de Recherche en Informatique et en Automatique), France in 2009. This dataset consists of 12 actions (get out of car, answer phone, kiss, hug, handshake, sit down, stand up, sit up, run, eat, fight, and drive car) with dynamic background features. This dataset is very challenging, consists of short unconstrained movies with multiple persons, cluttered background, camera motion, and large intra-class variations. This dataset is meant for evaluation of HAR algorithms in real life scenarios. Many researchers have evaluated their algorithms on this dataset, the best accuracy achieved so far is 75.2% in [169] using rank pooling and CNN. Some example frames from Hollywood2 dataset are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Year** & **No. of Actions** & **Method** & **Highest Accuracy** \\ \hline KTH & 2004 & 6 & [36] & 98.2\% \\ Weizmann & 2005 & 9 & [87] & 100\% \\ KMAS & 2006 & 13 & [120] & 100\% \\ UCF Sports & 2008 & 10 & [36] & 95.0\% \\ Hollywood2 & 2009 & 12 & [169] & 75.2\% \\ YouTube & 2009 & 11 & [52] & 93.38\% \\ HDMB-S1 & 2011 & 51 & [157] & 74.7\% \\ UCF-101 & 2012 & 101 & [157] & 91.6 \\ ActivityNet (Untrimmed) & 2015 & 200 & [176] & 42.2 (baseline) \\ ActivityNet (Trimmed) & 2015 & 200 & [176] & 50.2 (baseline) \\ \hline \end{tabular}
\end{table}
Table 4: Well-known public datasets for human activity recognition.  
Figure 14: (**a**) Exemplar frames for action 1–28 from HMDB (Human Motion Database)-51 action dataset; (**b**) Exemplar frames for action 29–51 from HMDB-51 action dataset.
## 3 Methodology
### Frequency Domain Feature Extraction Module
#### 3.3.2 Frequency Domain Feature Extraction Based on Transformer
The residual connection helps mitigate the vanishing gradient problem and facilitates information propagation in deep networks. The purpose of layer normalization is to normalize all features of each sample, enhancing numerical stability and accelerating training convergence. The calculation process is as follows:  
\[H_{1}=LN\Big{(}X^{T}+X\Big{)} \tag{17}\]  
\[H_{2}=FFN(H_{1}) \tag{18}\]  
\[\tau=LN(H_{1}+H_{2}) \tag{19}\]  
where: \(X\) and \(X^{T}\) represent the input sample and the output of the multi-head attention layer, respectively, the \(LN\) denotes residual connectivity and layer normalization and the \(FFN\) denotes feedforward network, \(\tau\) represents the feature representation obtained after processing through multi-head self-attention, feed-forward network, residual connection, and layer normalization.