esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed System Concept Architecture
### Turbulence Compensation
#### 3.2.1 Adaptive Optics
Using the updated structure function \(C_{n}\), we compute the Rytov variance, \(\sigma_{R}^{2}\). Finally, by applying the new Rytov variance into Equation (8), the scintillation index is derived. In this work, we focus on removing the first seven low-order Zernike modes, namely piston \((n=0,m=0)\), tilt \((n=1,m=\pm 1)\), defocus \((n=2,m=0)\), astigmatism \((n=2,m=\pm 2)\), coma \((n=3,m=\pm 1)\), trefoil \((n=3,m=\pm 3)\), and spherical \((n=4,m=0)\). Because each pair \(\pm m\) merges into a single term after integration in \(\phi\), these seven modes correspond to a total of 11 distinct Zernike polynomials. When summing only the modes listed above, the \(\phi\)-integration yields a closed-form expression. Defining \(r=\gamma\kappa D/2\), the integral becomes  
\[ \begin{split}\int_{0}^{2\pi}\Bigl{[}1\ -\ F_{\text{piston}}\ -\ F_{\text{tilt}\pm}\ -\ \ldots\Bigr{]}\,d\phi&=2\pi\ -\ 2\pi\biggl{[}\frac{l_{1}(r)}{r}\biggr{]}^{2}\ -\ 8\pi\biggl{[}\frac{l_{2}(r)}{r}\biggr{]}^{2}\\ &\ -\ 18\pi\biggl{[}\frac{l_{3}(r)}{r}\biggr{]}^{2}\ -\ 32\pi\biggl{[}\frac{l_{4}(r)}{r}\biggr{]}^{2}\ -\ 10\pi\biggl{[}\frac{l_{5}(r)}{r}\biggr{]}^{2}.\end{split}  \tag{17}\]  
The scintillation indices for varying ratios of temperature to salinity fluctuation before and after compensation with adaptive optics are shown in Figure 4. It demonstrates the effectiveness of the scintillation index reduction.
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 6 Parametric Analysis Model of Ultrasonic P-wave Velocity
### Verification of the Synthetic Forecasting Model  
Verification of Formula (2) involved preparing slurries and samples with two distinct mix proportions, as listed in Table 14, and measuring their ultrasonic p-wave velocities. Predictions from the model were then compared to actual test values documented in Table 15.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Specimen Number** & **P\({}_{\mathbf{a}}\). Coal Particle** & **Ps. High-Water Material** & **Pc. Cement** & **Pb. Water** \\
**Number** & **Size/mm** & **Content/\%** & **Content/\%** & **Content/\%** \\ \hline C1 & 0–6 & 3.80 & 23 & 29 \\ C2 & 0–4 & 5.20 & 18 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Design of verification schemes.  
Figure 12: For the wave velocity fitting curves of different factors. (**a**) Particle size of coal grains. (**b**) High-water material. (**c**) Cement. (**d**) Water content.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**The Specimen Number** & **Materials** & \multicolumn{3}{c}{**Wave Velocity/km s\({}^{-1}\)**} \\
**1** & **2** & **3** \\ \hline \multirow{3}{*}{C1} & Predicted values & 2.287 & 2.305 & 2.323 \\  & Experimental values & 2.354 & 2.033 & 2.276 \\  & Average error/\% & 4.250\% & \\ \hline \multirow{3}{*}{C2} & Predicted values & 1.831 & 1.855 & 1.879 \\  & Experimental values & 1.672 & 1.877 & 1.722 \\ \cline{1-1}  & Average error/\% & & 5.580\% & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Analysis of verification results.  
The analysis confirmed the model's high predictive accuracy, with relative errors for various indices spanning from 4.250% to 5.580% and an average error of 4.915%.
# Investigation of Quadrate Fresnel Zone Plates Fabricated by Femtosecond Laser Direct Writing
## 2 Theoretical and Experimental Methods
### Experimental Methods  
FZPs were fabricated in sapphire (Al\({}_{2}\)O\({}_{3}\)) via femtosecond laser direct writing. The femtosecond laser has an ultra-short pulse and ultra-high energy peak, which can realize non-hot melt cold processing and high-precision processing, and has a wide range of applications in micro and nano processing. The schematic diagram of the femtosecond laser direct writing experimental system is shown in Figure 3. A Ti-sapphire femtosecond laser with a pulse width of 120 fs, central wavelength of 800 nm, and repetition rate of 1 kHz was used for laser ablation, and it was focused with a 20\(\times\) objective lens (Nikon,  
Figure 1: The relationship between the half sidelength of S-FZP and the radius of the circular FZP.  
Figure 2: Schematic diagrams of quadrate FZPs: (**a**) L-FZP; (**b**) O-FZP; (**c**) S-FZP; (**d**) PL-FZP; (**e**) PO-FZP; (**f**) PS-FZP.  
NA = 0.4). The sapphire samples (\(10\text{ mm}\times 10\text{ mm}\times 1\text{ mm}\)) were placed on the 3-DOF movement platform. After processing, the morphology of surfaces was obtained by an optical microscope. The diffractive performance of FZPs was tested by a CCD and a diffraction performance test system [13].
# Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar Data
###### Abstract  
Fracture zones in front of tunnel faces can easily cause falling blocks and landslides during the construction process. Using seismic waves and ground-penetrating radar (GPR) data, we extracted the features of fracture zones and achieved the advanced prediction of tunnel fracture zones. The energy variation in the reflected waves propagated by seismic waves at interfaces with different impedances of contact waves was found to manifest as positive and negative reflections, and the amplitude of reflected signals within the fracture zone areas thus increased. We designed a superimposed velocity spectrum, divided the areas of variation in wave velocity, and constructed the three-dimensional spatial distribution of the tunnel fracture zones. Based on the phase change, increase in amplitude, and increase in the center-frequency characteristics of the one-dimensional time waveform of the electromagnetic waves in the fault zone area (A-scan), we located the characteristic points of the fracture zones and observed the occurrence of in-phase axis misalignment in two-dimensional scanning (B-scan). We then implemented the identification of fracture zones. This method predicted the fractured area in the rock surrounding the Liangwangshan Tunnel, and during the tunnel excavation, the fracture zones appeared in the recognition area.  
fracture zones; recognition; tunnel; seismic waves; ground-penetrating radar Article Recognition of Tunnel Fracture Zones in Seismic Waves and Ground-Penetrating Radar  
1 Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 6650500, China 1  
[PERSON]  
2 Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd., Kunming 6650200, China 2
## 3 Results and Discussion
### Summary  
The results obtained from the tensile testing of different oriented samples draw positive conclusions that the composites with parallel fiber orientation with respect to the injection flow exhibit higher values tensile strength. Maximum tensile strength (66.17 MPa) and a maximum TMOE (4.6 GPa) was seen was seen in 20% and 25% cellulose pulp-PA6 composite associating to 24% and 25% increase in values respectively when compared to neat PA6. Similarly, maximum flexural resistance (113.6 MPa) and maximum flexural MOE (3.84 GPa) was attained for 25% cellulose pulp-PA6 composites associating to 57% and 8% higher values than those of neat PA6 respectively.  
Previously conducted research on PA6-cellulose materials depicts no major increments in the mechanical properties of the final composite [50; 52]. The tensile and flexural values obtained in this study are without the use of any lubricants or process stabilizers and are comparable to the results of those in the bibliography [7; 10; 53]. Different cellulosic materials effect the tensile and flexural properties of cellulose based composites due to various factors such as particle size, particle shape, distribution, thermal stability, particle surface morphology and particle surface energy [54; 55]. Lower strain values with the increase in cellulose composition indicate the prominent reinforcing effect of cellulose due to strong interfacial bonding between fibers and PA6 matrix [14; 29]. The adhesion between pure cellulose fibers and PA6 matrix is further demonstrated through SEM analysis. Lack of defects indicate that the compounding method has been effective. Thanks to extreme short processing times, the thermal stresses subjected on the cellulose fibers have negligible degrading effect. This single step, quick compounding process constitutes to high fiber length when compared to studies which involve multiple thermal compounding steps/cycles [25; 49]. This aspect is boon on an industrial level where the promotion of renewable materials is hindered due to complex nature procedures involved in manufacturing biocomposites.
# Laboratory Validation of Surface-Initiated Transverse Cracking of Asphalt Pavement
## 2 Methodology
### Scale Down of Sample Size  
It should be noted that the specimen thickness used in the laboratory may not be equivalent to field pavement thickness directly. The reduced specimen size has been widely used to simulate field pavement conditions in the laboratory. A commonly used reduced-scale accelerated trafficking device is the one-third scale Model Mobile Load Simulator (MMLS3) [18]. Studies have shown that the application of MMLS3 loading creates stresses and strains in the pavement slab trafficked that are similar to those created in pavements subjected to field traffic if conditions of similitude are met [19].  
Such scaled-down is based on dimensional analysis [20; 21], which shows that if N is the scaling factor applied between the prototype and model, to get the same strains and stress in the model for inertial effect, it is necessary to scale down the length by 1:N and load magnitude by 1:N\({}^{2}\), while keeping a 1:1 scale for material properties and stress on the surface. For the MMLS3, these conditions entail scaling down of the pavement layer thickness and traffic velocity to one-third (N = 3) and applied load to one-ninth of those experienced in the field, respectively. The tire inflation pressure of MMLS3 is kept as a constant of 600 kPa.  
The scale-down factor N is not a fixed number and typically is determined by users [20]. In this study, the same scale-down factor as MMLS3 is applied to HWTD (N = 3) to take into account the mold geometry (thickness) size of the existing HWTD test without disturbing the rubber mold and specimen diameter (5.9 in). Assuming the scale factor as 3 means that when the authors evaluated overlay from 1 to 2.4 inches thickness, the full-scale overlay thickness was 3 to 7.2 inches. The HWTD wheel is tracked across the sample using a 445 N load onto a pneumatic linear hose pressurized to 0.69 MPa.  
Figure 1: Hamburg wheel-tracking device.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### Risk Assessment by BIA Guidelines by Asset
#### 5.2.10 Measuring Point C3 (MPC3)  
In the case of a Category C3, continuous measurement with remote communication, the MP can be read remotely, and in the event of communication failure, a field worker is sent to the site. Of the Type C metering, this is one of the most numerous groups of electricity meters. _Legal and contractual obligations_: If there is an MPC3 failure, it will not be possible to remotely read the MP. If this happens at the wrong time for many sites, there may be a violation of Decree No. 359/2020 Coll., and No. 540/2005 Coll., as well as Act No. 458/2000 Coll. and Decree No. 408/2015 Coll. Therefore, the risk is set as high. _Management and operation of the organization_: A large-scale communication failure could have a major impact on the management and operation of the organization, as tens of thousands of sites could be affected because the company would not be able to remediate the situation on the grounds. Depending on the nature of the outage and the length, in extreme cases, there could be a substantial impact on operations. _Loss of credibility_: Regarding this aspect, a possible communication outage with a large number of meters at MP could have a medium impact. A few tens of thousands of customers could have their bills delayed, meaning that it would not have such a heavy impact on the company's reputation. _Financial loss/Disruption to operations_: Major financial loss could only occur if communication with tens of thousands of sites is interrupted at the time of billing, when billing for consumption would be required and data must be sent to EMO. Unfortunately, the amount cannot be precisely quantified  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Legal and Contractual Obligations** & **Management and Operation of the Organization** & **Loss of Credibility** & \
\begin{tabular}{c} **Financial Loss/Disruption** \\ **of Activities** \\ \end{tabular} &
\begin{tabular}{c} **Provision of** \\ **Essential Services** \\ \end{tabular} & **MTPD** & **MIDP** & **MTDL** \\ \hline High & High & High & High & High & 1W & 2D & BM \\ \hline \hline \end{tabular}
\end{table}
Table 21: Assets in measuring point C2 risk rating.  
but could exceed several million crowns in the event of litigation. Therefore, the risk of impact is assessed as medium. _Provision of essential services_: This is the second largest type of metering in terms of the number of points of consumption, so only a medium impact on the company in terms of unavailability of services to customers can be expected in the event of a major communication failure. A comprehensive assessment of the risks and parameters of data availability and maturity is presented in Table 22.
## 4 EEG Normal Rhythms and Modification in Diabetes
[97] reported the changes in EEG rhythms in T2D and also T1D. Studies on T2D patients demonstrated decreased alpha band power in the posteriors temporal brain region and also beta band power in the temporal brain area. Theta and delta power was reported to have increased activity over the frontal and parieto-occipital brain region. In the posterior and central brain areas have been observed also the increase of P300 latency and decrease of N100 amplitude. In T1D patients was observed a decreasing band power of alpha, beta and gamma rhythms in the temporal and posterior temporal brain region and an increasing of theta and delta band power in the frontal and parieto-occipital brain area. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Data and Methods** & **EEG Modification** & **Paper** \\ \hline Signal acquisition for 27 healthy subjects used as control and 24 patients with longstanding diabetes mellitus (DM) and signs of autonomic dysfunction. The power distribution based on wavelet analysis & Decreased EEG power in the delta (1–4 Hz) and gamma (30–45 Hz) bands on DM patients. & [95] \\ \hline EEG recorded for 28 patients with T2D: 16 amnesic mild cognitive impairment (aMCI) patients and 12 controls. Phase lag index (PLI) used for frequency band decomposition and grouped into long distance & Significant decrease of global mean PLI in lower alpha, upper alpha and beta bands observed on aMCI subjects. & [98] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant studies on EEG modification for T2D patients.
# Analysing the Performance of a Trust-Based AODV in the Presence of a Flooding Attack
## 7 Simulation and Evaluation
### Throughput Performance Varying the Flooding Rate  
The simulation was run varying the flooding rate from 50 to 100 packets per second with a constant number of 200 nodes and 5 malicious nodes.  
Figure 6 shows that increasing the flooding rate from 50 to 100 packets per second reduces the throughput of AODV from 0.27 Mbps to less than 0.15 Mbps. In TAODV, the throughput looks more stable, with a slight decrease.  
Figure 4: Throughput of TAODV vs. AODV (95% CI).  
Figure 5: Throughput of TAODV vs. AODV under flooding attack (95% CI).
# Hair Removal Combining Saliency, Shape and Color
## 3 Experimental Results
### Quantitative Evaluation
#### 3.3.1 Quantitative Evaluation Based on Quality Measures
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Img** & **Met.** & **MSE** & **PSNR** & **MSE3** & **PSNR3** & **SSIM** & **MSSIM** & **VSNR** & **VIFP** & **UQI** & **NQM** & **WSNR** \\ \hline IMD061 & [PERSON] & 199.214 & 25.138 & 197.816 & 25.168 & 0.918 & 0.934 & 15.841 & 0.705 & 0.881 & 14.097 & 24.753 \\  & [PERSON] & **13.371** & **36.869** & **18.984** & **35.347** & **0.994** & 0.973 & **25.671** & **0.915** & **0.962** & **21.197** & **32.801** \\  & [PERSON] & 231.503 & 24.485 & 238.370 & 24.358 & 0.869 & 0.848 & 17.243 & 0.568 & 0.811 & 12.035 & 22.788 \\  & [PERSON] & 92.491 & 28.470 & 92.932 & 28.449 & 0.965 & **0.979** & 19.645 & 0.871 & 0.939 & 17.979 & 28.910 \\  & [PERSON] & 230.406 & 24.506 & 237.676 & 24.371 & 0.853 & 0.839 & 17.117 & 0.526 & 0.778 & 11.960 & 22.729 \\  & [PERSON] & 125.200 & 27.155 & 127.409 & 27.079 & 0.919 & 0.937 & 18.203 & 0.551 & 0.875 & 15.407 & 26.464 \\  & HR-SSC & 120.609 & 27.317 & 126.001 & 27.127 & 0.760 & 0.895 & 21.122 & 0.254 & 0.535 & 19.579 & 28.808 \\ \hline IMD063 & [PERSON] & 62.239 & 30.190 & 60.750 & 30.295 & 0.984 & 0.977 & 16.040 & 0.902 & 0.959 & 18.381 & 27.253 \\  & [PERSON] & **6.407** & **40.064** & **9.620** & **38.299** & **0.996** & **0.982** & **27.252** & **0.931** & **0.968** & **22.772** & **33.216** \\  & [PERSON] & 88.233 & 28.674 & 89.376 & 28.619 & 0.961 & 0.937 & 15.611 & 0.820 & 0.927 & 14.729 & 24.462 \\  & [PERSON] & 67.139 & 29.861 & 65.432 & 29.973 & 0.975 & 0.975 & 15.731 & 0.871 & 0.937 & 16.992 & 26.398 \\  & [PERSON] & 94.791 & 28.363 & 95.625 & 28.325 & 0.954 & 0.934 & 15.164 & 0.784 & 0.909 & 14.397 & 24.068 \\  & [PERSON] & 63.394 & 30.110 & 63.387 & 30.111 & 0.972 & 0.977 & 16.033 & 0.778 & 0.937 & 18.206 & 27.103 \\  & HR-SSC & 37.731 & 32.364 & 41.061 & 31.997 & 0.880 & 0.963 & 25.120 & 0.395 & 0.671 & 20.784 & 30.350 \\ \hline IMD075 & [PERSON] & 84.834 & 28.845 & 86.173 & 28.777 & 0.979 & 0.983 & 16.386 & 0.880 & 0.951 & 15.894 & 27.734 \\  & [PERSON] & **4.125** & **41.977** & **5.986** & **40.359** & **0.999** & **0.986** & **27.081** & **0.932** & **0.969** & **23.102** & **37.372** \\  & [PERSON] & 123.562 & 27.212 & 123.997 & 27.197 & 0.951 & 0.917 & 14.234 & 0.803 & 0.920 & 11.946 & 24.537 \\  & [PERSON] & 92.738 & 28.458 & 93.963 & 28.401 & 0.970 & 0.977 & 15.607 & 0.851 & 0.934 & 14.744 & 26.916 \\  & [PERSON] & 131.867 & 26.929 & 132.950 & 26.894 & 0.942 & 0.913 & 13.898 & 0.762 & 0.897 & 11.703 & 24.251 \\  & Bibiloni & 77.560 & 29.234 & 79.014 & 29.154 & 0.976 & 0.982 & 16.953 & 0.825 & 0.949 & 16.659 & 28.034 \\  & HR-SSC & 42.993 & 31.797 & 46.676 & 31.440 & 0.829 & 0.944 & 21.012 & 0.286 & 0.539 & 19.158 & 32.117 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Img** & **Met.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
### Current Challenges in Malware Detection
#### 1.1.5 The Need for Advanced Visualization and Deep Learning  
These challenges highlight the necessity for innovative approaches that can effectively bridge the gap between traditional binary analysis and modern machine learning techniques. The transformation of assembly code into visual representations offers a promising avenue for leveraging computer vision advances in malware detection. By converting opcode sequences into RGB images and employing hybrid convolutional neural networks, we can potentially overcome many of the limitations associated with traditional feature extraction methods while maintaining scalability and detection accuracy.
# Integrated Photonics for IoT, RoF, and Distributed Fog-Cloud Computing: A Comprehensive Review
## 8 Future Trends
When deployed in hybrid Fog-Cloud systems, photonic quantum processors and neuromorphic modules could support edge intelligence with unprecedented speed, energy-efficiency, and data security. These capabilities are particularly relevant to autonomous systems, environmental sensing, and real-time AI-enabled services, suggesting a profound role for PICs in shaping future distributed computing landscapes. Integrating optical neural networks into real-time, edge-deployable systems remains an open area, with several efforts focusing on simulating optical neural networks for deep learning workloads in edge computing environments [120]. Advances in photonic packaging and co-design with electronics are critical--as demonstrated by NTT's Photonics-Electronics Convergence (PEC) roadmap, which places integrated transmitters and receivers progressively closer to CPU/ASIC chips, moving toward the use of die-to-die optical interconnects by 2032 [121]. Furthermore, hybrid architectures combining classical, neuromorphic, and quantum photonics require unified design frameworks: recent research into hybrid quantum-classical photonic neural networks outlines pathways to enhance computational capacity without expanding network size [122]. Addressing these gaps is essential for transitioning from isolated PIC modules to holistic, intelligent photonic computing platforms that bridge the edge-to-cloud continuum.
# Discrimination of Aroma Characteristics for Cubeb Berries by Sensomics Approach with Chemometrics
## 4 Conclusions  
In this study, the aroma compounds of two ambiguous cubeb berries were isolated by HD, SDE, and SPME pretreatment methods in order to fully obtain the complex aroma profiles of the cubeb berries and were analyzed by GC-MS-O combined with PCA. By GC-MS-O analysis, a total of 12 aroma-activecompounds were found to play a key role in the characteristic flavor of the cubeb berries. The PCA results clearly indicated that the two ambiguous cubeb berries could be discriminated by the aroma profiles. _Litsea cubeba_ was characterized by higher level of d-limonene, citral and dodecanoic acid; _Piper cubeba_ was marked with \(\alpha\)-cubebene, higher camphor, and linalool. Therefore, using the volatile profile combined with PCA is an appropriate method to discriminate the cubeb berries and assure the related product quality.  
Methodology, J.C.; Software, D.W.; Formal Analysis, S.C.; Writing--Original Draft Preparation, H.C.; Writing--Review & Editing, P.J.W., and D.L.; Supervision, X.Y.  
This research was funded by the Special Science and Technology Program of Xinjiang Uygur Autonomous Region (grant no. 2016A03008-02), Natural Science Foundation of Zhejiang province, China (grant no. LY17C200013).  
The authors declare no conflict of interest.
## 4 The Test Phenomena and the Result Analysis
### The Load--Displacement Curve  
Figure 16 displays the load--displacement curve of the beam end. Due to the loading on the manual control pump and the fluctuations of the display on the monitor, asymmetry occurred when loading occurred from both ends. It can be seen from the diagram that the load--displacement curve of all specimens basically fits, which means that both ends are fundamentally symmetrical while loading.  
In the early stage of loading, the load--displacement relation curve of S2 is steeper than that of S1. That is mainly because the diagonal stiffeners of S2 are larger in size. During the loading period, the deformation of S2 developed more slowly compared to S1 under the same load. When the specimen S2 was damaged, the load values on the beam ends of both sides were 149 kN and 151 KN, both of which are greater than the failure load of S1, namely 105 N and 120 kN. Poor weld quality is the main reason for the early damage of the right side of S1. Therefore, the weld quality seems an important factor in ensuring a certain capacity of loading.  
Specimen S2 has an obvious plastic phase. The equivalent elastic and plastic yield methods are adopted to judge the yield point. The yield load of S2 is approximately 105 KN, and the yield displacement is approximately 26 mm. Specim 1 shows less of a plastic phase; however, judging by the curve and the stress at every point, it began to yield. The yield load is approximately 90 kN and the yield displacement is approximately 24.7 mm. Comparing the data of S1 and S2, it can be concluded that the ultimate load increased by approximately 25% and the yield load increased by approximately 17%, after the sizes of the diagonal stiffeners were changed.