esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overall System Design
### Performance Requirements  
To meet the above-mentioned simulation requirements, the system developed in this study aims to generate high-consistency, tunable, and reconfigurable solar background radiation within the communication band, while supporting spatial co-propagation with weak signal beams. The design focuses on achieving three key performance objectives: (1) Spectral consistency: A multilayer optical filter is optimized to compensate for the nonlinear power-wavelength behavior of the laser source, maintaining stable spectral output within the communication window, with a target spectral matching degree of no less than 75%. (2) Far-field irradiance uniformity: A hybrid beam shaping structure, combining a homogenizing fiber and a beam truncation element, is employed to produce a flat-top beam with over far-field 85% irradiance uniformity across a 200 mm aperture. (3) Continuously tunable output power: A programmable attenuation system enables smooth power modulation from 10% to 100% (max = 130 mW), covering the full range of solar irradiance levels observed before, during, and after Sun-transit events. (4) Stray light suppression capability: Due to the co-propagation of solar background and communication signals within a shared optical path, multiple internal reflection and scattering events--especially from coated surfaces--can introduce stray light that degrades signal recognition. Therefore, the system is required to incorporate a theoretical model for stray light path analysis and implement spatial extinction structures in the design to limit background energy. The target performance is to maintain a signal-to-background ratio (SNR) greater than 10:1 under a 10\({}^{-10}\) W signal output condition.  
Additionally, a precision rotation stage allows for \(\pm 3^{\circ}\) incident angle simulation, enabling reconstruction of dynamic alignment scenarios. The entire system is designed to support the physical simulation of co-aligned solar interference environments, with a focus on high spectral and spatial fidelity, wide dynamic output range, and low-background signal recognition capabilities.
## 3 Results and Discussion
### _RUL Estimation for Dataset-2_  
The prediction results for the delamination Data Set 2 using Paris model is plotted in Figure 7a-c. As mentioned earlier, the proposed prognostic framework requires a  
Figure 5: Comparison plot for root mean square error values between the **Paris** model and **Paris**-**Paris** model for all the three delamination of Sample S1.  
Figure 6: The damage growth area for sample S1 subjected to low-velocity impacts till the damage eventually led to fiber breakage.  
minimum of two measurement datapoints available for prediction. For the Paris model, the model parameters for Region 1 (\(m_{1},C_{1}\)) are used in the damage propagation model, and the prediction results show that the predictions follow an exponentially increasing trend completely diverging from the actual degradation behavior. Even when there are a greater number of observations available to learn from the failure trend, the predictions at 50 J and 80 J fail to capture the actual degradation trend as shown in Figure 7b,c. It is evident from the results here that the Paris model is unable to capture the degradation behavior of the GFRP S1 laminate.  
The prediction results for the optimized Paris-Paris model is shown in Figure 8a-c. The predictions at 20 J are similar to that of the Paris model shown in Figure 8a. However, after the jump energy is detected at 25 J, the prediction accuracy improves as model parameters for Region 2 (\(m_{2},C_{2}\)) are plugged into the damage propagation model. The results for 90 J shown in Figure 8b clearly show that the model manages to capture the degradation trend. However, the prediction accuracy starts decreasing with a prediction error of about 20% after 250 J as shown in Figure 8c.  
Figure 7: _Cont._  
Figure 8: _Cont._  
Figure 7: The prediction results for Delamination Dataset 2 using the **Paris** model with failure threshold assumed to be at 1200 mm\({}^{2}\). The grey lines represent the particle trajectories for 1000 particles.  
The damage model proposed fails to capture the damage trend towards the end of life of the sample where the damage dynamics seems to have a more concave gradual increase in damage area, unlike the rapid exponential damage growth in the earlier stages of delamination. Thus, the model to be adapted has to be improvised further to improve the prediction accuracy in the final stages of delamination growth, which is out of the scope of this study here. If a more appropriate three-stage model for crack nucleation, growth, and resulting delamination could be proposed, our framework in Figure 2 can be easily extended to do prognosis for such scenarios with two thresholds instead of one.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Plasma Irradiation
#### 2.7.3 Active Hydrogen H\({}_{2}^{*}\)  
[PERSON] et al. [129] exposed CVD-grown MoS\({}_{2}\) mono-layers in hydrogen plasma at different temperatures (400-700 \({}^{\circ}\)C), as shown in Figure 26. The MoS\({}_{2}\) mono-layer were treated to expose more active sites in the basal plane of MoS\({}_{2}\) and to improve catalytic activity. No significant changes were observed on the MoS\({}_{2}\) when the annealing temperature was 400 \({}^{\circ}\)C. The MoS\({}_{2}\) was then H\({}_{2}\)-treated at 500 \({}^{\circ}\)C and small triangular holes with sizes around 1-4 \(\upmu\)m appeared. High-density holes with sizes around 10-20 nm were omnipresent in the basal plane of MoS\({}_{2}\). High-density triangular-shaped holes became the prominent part in the original mono-layer MoS\({}_{2}\) at 600 \({}^{\circ}\)C. 700 \({}^{\circ}\)C H\({}_{2}\)-treatments led to severe MoS\({}_{2}\) decomposition. The hydrogen plasma treatments decreased the peak intensities of Raman scattering and PL spectra, indicating that hydrogen treatments could increase the defects and edges in mono-layer MoS\({}_{2}\).  
Figure 25: **(a)** Optical images, **(b)** confocal PL spectral intensity mapping images, and **(c)** spectral peak mapping images of pristine and treated MoS\({}_{2}\) mono-layers [131]. The color represents PL intensity in **(b)** and wavelength of PL peak in **c**. Reprinted from Reference [131]. Copyright © 2015, with permission from Elsevier.
###### Abstract  
As an important method for seismic data processing, reverse time migration (RTM) has high precision but involves high-intensity calculations. The calculation an RTM surface offset (shot-receiver distance) domain gathers provides intermediary data for an iterative calculation of migration and its velocity building. How to generate such data efficiently is of great significance to the industrial application of RTM. We propose a method for the calculation of surface offset gathers (SOGs) based on attribute migration, wherein, using migration calculations performed twice, the attribute profile of the surface offsets can be obtained, thus the image results can be sorted into offset gathers. Aiming at the problem of high-intensity computations required for RTM, we put forth a multi-graphic processing unit (GPU) calculative strategy, i.e., by distributing image computational domains to different GPUs for computation and by using the method of multi-stream calculations to conceal data transmission between GPUs. Ultimately, the computing original efficiency was higher relative to a single GPU, and more GPUs were used linearly. The test with a model showed that the attributive migration methods can correctly output SOGs, while the GPU parallel computation can effectively improve the computing efficiency. Therefore, it is of practical importance for this method to be expanded and applied in industries.  
reverse time migration (RTM); surface offset gathers (SOGs); parallel computation; graphic processing unit (GPU) +
Footnote †: journal: Applied Sciences  
1
## 7 Future Work
### HAP Drone Localization  
The positions of HAP drones are assumed to be stationary and known in our paper. However, the positions of HAP drones will constantly change due to jitter. Only by knowing the exact location of HAP drones can we accurately calculate the distance between HAP drone and LEO satellite, the remaining visible time, and the channel capacity. Therefore, the exact location of HAP drone is essential for making the user access and traffic backhaul strategy of the space-air-ground integrated network. In the future, the HAP drone localization problem needs to be solved. Other positioning systems can be added to estimate the exact location of HAP drone. For example, reinforcement learning-based algorithms can be used to regularly predict the exact location of HAP drone by inputting atmospheric data such as wind speed.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Spaceborne LiDAR, Terrestrial Carbon Biomass  
From the Google Earth Engine repository, we downloaded global tree canopy height data (circa 2005) produced from the Geoscience Laser Altimeter System (GLAS) spaceborne LiDAR aboard ICESat (Ice, Cloud, and land Elevation Satellite) [76] for the lenti zone. GLAS transmits laser pulses at 1024 nm with footprints of \(\sim\)65 m diameter and records the reflected waveform. The data were available at a spatial resolution of 30 arc seconds.  
Harmonized above and below-ground terrestrial carbon (MgC/ha) estimates (circa 2010) [77,78] were downloaded from the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC). The aboveground C data represent a compilation of pre-existing, publicly available ecosystem data. For below-ground C, land cover specific root-to-shoot relationships had been used to generate maps for each of the input above-ground C maps prior to harmonization (300 m resolution) (see [77] for details). Both above and below ground biomass C stock densities are limited to living plant tissues. We summarized the above and below ground C for the pre-inundated forest area within high water reservoir boundaries.
###### Abstract  
It is accepted that there exists two kinds of atmospheric turbulence in the Earth's aerospace--Kolmogorov and non-Kolmogorov turbulence; therefore, it is important to research their combined impacts on laser-satellite communications. In this paper, the exponential power spectra of refractive-index fluctuations for non-Kolmogorov turbulence in the free troposphere and stratosphere are proposed, respectively. Based on these two spectra, using the Markov approximation, beam wander displacement variances of a Gaussian-beam wave are derived, respectively, which are valid under weak turbulent fluctuations condition. On this basis, using a three-layer altitude-dependent turbulent spectrum model for vertical/slant path, the combined influence of a three-layer atmospheric turbulence on wander of a Gaussian-beam wave as the carrier wave in laser-satellite communication is studied. This three-layer spectrum is more accurate than a two-layer model. Moreover, the variations of beam wander displacement with beam radius, zenith angles, and nominal value of the refractive-index structure parameter on the ground are estimated. The theory of optical wave propagation through non-Kolmogorov atmospheric turbulence is further enriched and a theoretical model of a three-layer atmospheric turbulence beam wander for a satellite-ground laser communication uplink is established.  
satellite laser communication; atmospheric optics; non-Kolmogorov turbulence; Kolmogorov turbulence; beam wander
## Nomenclature  
Abbreviations and Symbols  
\begin{tabular}{l l} \multicolumn{2}{l}{IMC} & Internal model control \\ FOPDT & First-Order Plus Dead-Time model \\ PSO & Particle swarm optimization \\ SOPDT & Second-Order Plus Dead-Time model \\ \(D\)(s) & Polynomial of a denominator \\ \(e\) & Error between the reference signal and the actual output value. \\ \(G_{\rm c}\) & Closed-loop transfer function \\ \(G_{\rm dc}\) & Desired closed-loop transfer function \\ \(G_{\rm o}\) & Open-loop transfer function \\ \(G_{\rm p}\) & Transfer function of the plant \\ \(G_{\rm r}\) & Controller transfer function \\ \(K\) & Static gain of the plant \\ \(K_{\rm p}\) & Controller parameter, proportional gain \\ \(N\)(s) & Polynomial of a numerator \\ \(r\) & Reference signal \\ \(t_{\rm s}\) & Settling time \\ \(T_{\rm r}\) \(T_{\rm i}\), \(T_{\rm 2}\) & Time constants \\ \(T_{\rm d}\) & Derivative time constant \\ \(T_{\rm i}\) & Integral time constant \\ \(u\) & Control signal \\ \(w_{1}\), \(w_{2}\) & Weight factors \\ \(\Delta\) & Error band \\ \(\chi\) & Overshoot \\ \(\theta\) & Dead time \\ \(\tau_{\rm c}\) & Closed-loop time constant \\ \end{tabular}
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## 6 Algorithm Analyses
### Differential Attack Analysis  
The differential attack is used to check the plaintext sensitivity for an image encryption algorithm [22]. Therefore, if we make a slight change to the plain image, a desirable encryption algorithm can spread this influence over the whole encryption process. To evaluate the ability to resist the differential attack, the Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) are defined by:  
\[NPCR=\frac{\sum_{i=1}^{\text{m}}\sum_{j=1}^{\text{n}}f(i,j)}{\text{m}\times \text{n}}\times 100\% \tag{16}\]  
\[UACI=\frac{\sum_{i=1}^{\text{m}}\sum_{j=1}^{\text{n}}\lvert I^{\prime}(i,\,j)- I^{\prime\prime}(i,\,j)\rvert}{255\times m\times n}\times 100\% \tag{17}\]  
where \(I^{\prime}(i,\,j)\) is the encryption image of the plain image, \(I^{\prime\prime}(i,\,j)\) is the encryption image of the modified plain image, and \(f(i,\,j)\) is defined by:  
\[f(i,j){=}\left\{\begin{array}{ll}0&I^{\prime}(i,j)=I^{\prime\prime}(i,j)\\ 1&I^{\prime}(i,\,j)\
eq I^{\prime\prime}(i,j)\end{array}\right. \tag{18}\]  
To evaluate the performance of the plaintext sensitivity, the simulation changes the pixel value \(I(1,1)\) of the plain image into 200. For the proposed algorithm, [PERSON]'s algorithm, [PERSON]'s algorithm, and [PERSON]'s algorithm, have their NPCR and UACI results of Figure 4a listed in Table 4. For the proposed algorithm and [PERSON]'s algorithm, NPCR and UACI results are very large. Therefore, their plaintext sensitivity is very strong. However, for [PERSON]'s algorithm and [PERSON]'s algorithm, NPCR and UACI results are 0. The reason is that both the proposed algorithm and [PERSON]'s algorithm use the mode of cipher block chaining to encrypt Figure 4a. Meanwhile, for the proposed algorithm, the variable \(x_{0}\) and \(q\) of PWLCM are generated by the SHA-256 value of Figure 4a. Even if two images are very similar, but only one bit is different. Their hash values of SHA-256 are completely different [21]. The results imply that both the proposed algorithm and [PERSON]'s algorithm are strong to withstand the differential attack.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithms** & **NPCR** & **UACI** \\ \hline The proposed algorithm & 99.62\% & 33.38\% \\ [PERSON]’s algorithm & 99.36\% & 33.14\% \\ [PERSON]’s algorithm & 0 & 0 \\ [PERSON]’s algorithm & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) results for Figure 4a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Directions** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Plain image & 0.9402 & 0.9423 & 0.9032 \\ The proposed algorithm & \(-\)0.0047 & 0.0025 & 0.0014 \\ [PERSON]’s algorithm & \(-\)0.0039 & 0.0022 & \(-\)0.0020 \\ [PERSON]’s algorithm & \(-\)0.0012 & 0.0007 & 0.0034 \\ [PERSON]’s algorithm & \(-\)0.0026 & \(-\)0.0009 & 0.0007 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation coefficients.
# Design Criteria for High-Gradient Radio-Frequency Linacs
## 3 RF Breakdown
### Reproducibility of High-Power Test Results  
One of the most important results of high-power testing of multiple X-band TW and SW cavities is the reproducibility of _breakdown rate maps_. I found that the breakdown rate maps are remarkably reproducible for cavities of the same shape made from the same materials. This observation has been confirmed for cavities made by various manufacturers and for a range of initial metal surface conditions. As an example, I show in Figure 2 the breakdown rate maps for three cavities of the same shape, two made in KEK, Japan, and one made at the INFN-LNF, Italy. Note that these are stationary breakdown rate maps obtained after initial conditioning.  
This reproducibility has several important practical implications. Firstly, the breakdown performance could be predicted for cavities of new geometries. Understanding this led to the development of empirical quantities that have become a practical tool for designing accelerating structures [31, 56, 72, 73]. However, extrapolation of data outside of a well-studied parameter space should be performed with caution.  
Secondly, a physical model capable of predicting the rf breakdown characteristics of accelerating cavities should have the shape of the structure and the material as its main parameters. At the time of publication, such a model is still far away.
# Arsenic: A Review on a Great Health Issue Worldwide
## 4 Arsenic Sources and Exposure  
Arsenic is a natural element found in freshwater and foods; it is also found in ocean, lakes, rivers, soil, and air. In humans, the main sources of arsenic exposure can be divided in natural and anthropogenic sources. The most important contribution of arsenic in nature is attributed to geological formation, including the deposition of this element in sedimentary rocks, volcanic rocks and soil in the form of sulfide in over 200 mineral species containing a mixture of many different metals. Arsenic levels in the crust fluctuate between 1.5 and 5 mg/kg [45]. Other significant sources of inorganic arsenic are geothermal activity and geothermal water, coal and volcanic activity. In addition to volcanic activity, leaching of arsenic from soil to groundwater through natural erosion releases this metal into the air, water, and soil [46]. Arsenic contamination in groundwater has been found in about 100 countries (including China, USA and India) with more than 200 million people worldwide due to anthropogenic activities through mining and petroleum extraction. Burning of petroleum and As-rich coal, use of pesticides, herbicides, insecticides containing arsenic, phosphate fertilizers contaminated with arsenic, act as major anthropogenic sources of arsenic. Human activities may trigger and contribute to the arsenic contamination such as the use of arsenic-contaminated irrigation water for agriculture, mining and smelting activities, fossil fuel combustion, and wood preservatives [47]. Some of the major sources of arsenic exposure in humans are represented by drinking water, arsenic-contaminated foods and agricultural products (rice, wheat, potato, and vegetables) [48; 49].
# Human Behavior Analysis: A Survey on Action Recognition
## 3 Datasets
### Frame-Level Benchmarks  
**UCF-101**[95] consists of 13,320 realistic videos widely collected from Youtube, containing 101 action classes with a wide diversity in intra-class and inter-class, and large variations of camera motion, object scale, object appearance, cluttered backgrounds, viewpoints, different illuminations. This dataset provides the frame-level ground-truth of the actions from all videos and is one of the most popular benchmarks among the action recognition methods at the frame-level.  
Figure 7: A spatiotemporal graph of a skeleton sequence. Light green dots represent the body joints (graph nodes). Light blue edges illustrate the intra-body edges. The spatial graph convolution receives as input a skeleton graph with its corresponding adjacency matrix to control the intra-frame (spatial) convolution (red dotted line) from the root node (red joint) neighbourhood. Then, 1-dimensional convolution is performed on the same positional joints across consecutive frames, resulting in the temporal (inter-frame) convolution.  
**HMDB-51**[96] with 51 action categories, is composed of 7000 clips from Youtube videos to digitised movies, where each class contains at least 101 videos, providing a great diversity between action classes. This dataset provides the frame-level ground-truth of the actions from all videos and is also one of the most popular datasets for evaluation at the frame-level.  
**Kinetics-400**[97] has 400 human action classes, where each action has at least 400 video samples from Youtube, and each video clip has a duration of 10 seconds. With great heterogeneity, this dataset provides the frame-level ground-truth of all videos' actions. Being a well-known dataset, the authors released two extended versions, the Kinetics-600 [98] with 600 action categories, where each action has at least 600 clips, and very recently, the Kinetics-700 [99] with 700 action classes, where each class has at least 700 videos. However, the 400 version is currently the most popular one of these three versions.  
The **Sports-1M** dataset [19] is currently the largest video dataset composed of 1,133,158 videos, which have been annotated automatically with 487 action categories at the video-level, presenting an extreme diversity of sports videos. However, its availability is only provided through individual video URLs, making it difficult to access the videos.  
**THUMOS'14**[100] consists of approximately 18,000 videos widely collected containing 101 action classes, providing its ground-truth labels at the frame-level. This dataset has the peculiarity of providing only trimmed videos for the training phase, and methods should be evaluated on untrimmed data over the validation and test set.
## 5 Discussion and Implications
### Implications and Recommendations
#### 5.4.2 Research  
Future studies should move beyond predictive accuracy and assess actual learning outcomes through longitudinal and mixed methods designs. Aligning model architectures with learning theories, such as SRL, motivation, and engagement, can improve pedagogical relevance. Additionally, the instructional impact of GenAI tools on reflection, autonomy, and knowledge construction warrants deeper exploration.