esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Results and Discussion  
The signal was operated with FFT, and its spectrum was adaptively divided into 50 initial components. We re-divided the spectrum via the boundary optimization method, as shown in Figure 10. We constructed a filter bank to obtain a new set of components, as shown in Figure 11.  
Figure 11: New components.  
Figure 10: \(M\)I between the initial components and the original signal.  
Figure 9: Defect time-domain signal.  
The \(MI\) and \(K\) of each component after re-dividing the spectrum are shown in Figure 12. First, according to \(Ml_{m}\), the components \(f_{1}(t)\), \(f_{3}(t)\), and \(f_{6}(t)\) were selected. And then, according to \(K\), \(f_{1}(t)\) and \(f_{3}(t)\) were finally selected. The two components were used to reconstruct the signal according to Equations (2) and (3). Figure 13 shows the reconstructed signal.  
From Figure 13, obviously, after filtering, the defect signal component was retained and noise interference was suppressed compared to Figure 9. To verify the advantage of the improved EWT in minor-defect MFL signal filtering, CEEMD and WT were used to filter the defect signal, and the filtering effects of the three methods were compared.  
(1) CEEMD  
The IMF components and residual component obtained by decomposing the defect signal using CEEMD are shown in Figure 14. We selected the suitable components according to the method in Figure 6, and the reconstructed signal is shown in Figure 15.  
Figure 12: \(Ml\) and \(K\) of new components.  
Figure 13: Result of the improved EWT.  
(2) WT  
There have been studies on the filtering of the MFL signal using WT, including the aspects of wavelet basis and decomposition layers. According to Ref. [21], we selected sym6 as the wavelet basis, set the number of layers for wavelet decomposition to 10, and adopted the soft threshold processing method. Figure 16 shows the WT filtering result.  
Figure 16: Results of the WT.  
Figure 14: Components of CEEMD.  
Figure 15: Results for CEEMD.  
The common evaluation indicators of filtering quality for the defect signal include SNR and root-mean-square error (RMSE), etc. [21]. The filtering effects of improved EWT, CEEMD, and WT were compared via SNR and RMSE. The results are shown in Table 2. Because of the strong background noise and weak defect signal, the power of the useful signal is lower than that of the noise, resulting in a negative SNR. From Table 2, it can be seen that after being processed via the improved EWT, the SNR is increased by 15.54 dB compared to that of the original signal, and is 3.46 dB and 7.46 dB higher than that of CEEMD and WT, respectively. The RMSE has decreased by 60% compared to that of the original signal. It can be concluded that the improved EWT proposed in this article can preserve defect signal components while suppressing background noise, and has a better performance compared to CEEMD and WT.
# Probabilistic Modeling of Dust-Induced FSO Attenuation for 5G/6G Backhaul in Arid Regions
## 4 Conclusions  
Based on controlled dust chamber experiments, this paper presents a probabilistic model for FSO signal attenuation under dust storm conditions. Several statistical distributions were tested across dense, moderate, and light dust visibility ranges. Among these, the Johnson SB distribution demonstrated the best overall performance, with RMSE and MAE values close to zero and a consistently high \(R^{2}\geq 0.95\) for all visibility levels. These results indicate strong agreement between the model and empirical data. The proposed model supports robust, risk-aware FSO link design for current and future 5G/6G networks by accounting for the variability in attenuation caused by dusty environments.  
Future work will focus on validating the proposed model through outdoor measurements and evaluating the performance of communication systems using practical metrics such as bit error rate and outage probability. Furthermore, the chemical composition and  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Visibility Condition** & \(\gamma\) & \(\delta\) & \(\lambda\) & \(\xi\) \\ \hline Dense Dust & 2.30 & 0.78 & 10371 & 245.25 \\ Moderate Dust & 0.59 & 0.75 & 279 & 74.20 \\ Light Dust & 0.67 & 2.15 & 187 & \(-\)19.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated parameters of the Johnson SB distribution under different dust conditions.  
Figure 3: Comparison of fitted PDFs over the full visibility range using **(a)** the Johnson SB distribution model and **(b)** the negative exponential distribution model.  
concentration of dust particles, which could influence optical signal scattering, were not considered in this study due to equipment limitations. Future efforts will incorporate these parameters to enhance the model's generality across various dust types and atmospheric conditions. Additionally, ambient factors such as temperature, humidity, and air pressure will be taken into account to provide a more comprehensive assessment of their impact on FSO link performance.  
This article is derived from a research grant funded by the Research, Development, and Innovation Authority (RDIA), Saudi Arabia, under grant number (13292-psu-2023-PSNU-R-3-1-EF). The authors were also supported by the Prince Sultan University, Riyadh, Saudi Arabia.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the author on request.  
The authors would like to acknowledge the support of Prince Sultan University, which paid the Article Processing Charges (APCs) for this publication. Also, the authors would like to thank the RFTONICS center at KSU for allowing access to their facilities.  
The author declares no conflicts of interest.  
The following abbreviations are used in this manuscript:  
FSOFree Space Optical  
ITU-RInternational Telecommunication Union Radiocommunication Sector  
LDLaser Diode  
Log Likelihood  
MAEMean Absolute Error  
PDPhotodiode  
PDFProbability Density Function  
\(R^{2}\)Coefficient of Determination  
RFRadio Frequency  
RMSERoot Mean Square Error  
Terahertz
## 5 Conclusions  
In this paper, a strain transfer model has been proposed for FBG sensors bonded to the surface of CFRP laminates. The measurement structure was abstracted into the silica core, coating, adhesive layer, and CFRP laminates, and the CFRP laminates were regarded as an anisotropic multilayered structure. A coordinate system was set up for the measurement structure, and the strain transfer function was derived according to the shear-lag theory.  
The strain transfer characteristics were analyzed according to the transfer function. The stacking directions, stacking numbers, and stacking sequences of CFRP laminates have a distinct influence on the strain transfer efficiency. The CFRP laminates with bigger direction angles, larger stacking numbers, and lower surface stiffness are disadvantageous for the strain transfer. The effect of the cross-sensitivity on strain measurement was compensated and the influence of temperature on the strain transfer model was discussed.  
The accuracy of the proposed model was verified by experiments on a nondestructive tensile system using three kinds of CFRP laminate specimens. The maximum error between theoretical calculation and experimental results is less than 0.5%. Besides, the model was applied to the strain measurement of the CFRP wing skin. The experimental results indicate that the proposed model can significantly compensate strain measurement errors. The measurement errors decrease by 11.6% to 19.8% after the compensation. The work can be used as a reference to enhance the strain measurement accuracy of CFRP laminates.  
Conceptualization, Z.W. and H.L.; Data curation, J.X.; Investigation, H.L and L.Z.; Methodology, H.L.; Resources, J.X.; Supervision, L.Z. and J.X.; Visualization, H.L.; Writing--original draft, H.L.; Writing--review & editing, Z.W.  
This research was funded by [National Key Research and Development Plan of China] grant number [2016 YFF0203801] and [Natural Science Foundation of China] grant number [No.51575032].  
The authors declare no conflict of interest.  
Figure 14: Comparison among measured strain, modified strain, and simulated strain at the three installing points.
## 4 Discussion
### Similarity between of the Laboratory Test and Tunnel Floor Heave Phenomenon  
There is a certain similarity between the macroscopic phenomena found in indoor experiments and actual engineering. Figure 15a shows that the layered surrounding rock at the bottom of the inverted arch is separated into a platy structure under the action of two-direction loads and breaks off from the middle, resulting in displacement towards the free surface in the model experiment. This was consistent with the fracture and floor heave at the bottom of the underground hole found by [14]. This study also showed that at the notch tip, the dilation mechanisms in the process zone were visible [14]. If this failed material was removed, a slab would form on the flank of the notch. The surrounding rock of the tunnel underwent deformation toward the free surface under high stress. Similarly, under the influence of a stable lateral stress and increasing axial load, cracks generated and propagated along the weak planes between layers in the schist, resulting in the formation of multilayer platy structures. The platy rock experienced buckling failure due to the axial load, and the local stress in the central region exceeded its strength and fractured. The macroscopic phenomenon was an upward uplift at the bottom of the inverted arch. Furthermore, the maximum depth of failure occurred in the middle of the bottom of the inverted arch, forming an inverted trapezoidal failure surface. The main reason was that the stress concentration coefficient at this location was the highest. In contrast, the results of the DIC system indicated that the initial failure occurred in the region near the surface of the inverted arch and gradually propagated toward the interior, away from that region.  
The uplift and cracking of the inverted surface in the indoor experiment are shown in Figure 15b. Generally, only surface deformation can be seen from actual engineering phenomena without special treatment, usually manifested as the uplift and cracking of the tunnel surface. This phenomenon was also found in the indoor test. From the cross-sectional view of the tested samples, the displacement caused by the surface uplift was due to the bottom surrounding rock being separated into a plate shape and broken from the middle, forced out toward the outermost surrounding rock. However, due to local deformation, heterogeneity of the rock, and the limiting effect of lateral stress, the deformation of the sample did not lead to the overall uplift of the entire sample. Instead, the \"S\"-shaped crack and local protrusion could be observed. The formation of surface cracks was mainly caused by tensile failure.  
Figure 15: Destructive morphology of floor heave: (**a**) fracture of sample platy rock; (**b**) surface cracks in the sample.
# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 2 Methods
### Interference Fringe Characterization  
Figure 3a shows the geometric schematic diagram of InSAR. \(B\) is the length between antennas \(A_{1}\) and \(A_{2}\) or, in other words, the baseline length. \(H\) is the aircraft flight height, \(\alpha\) is the baseline inclination angle, \(h\) is the elevation value of ground point \(P\), \(\theta\) is the downward viewing angle, \(R\) is the squint distance between antenna \(A_{1}\) and \(P\), and \(R+\Delta R\) is the squint distance between antenna \(A_{2}\) and \(P\). According to the InSAR geometric schematic diagram in Figure 3a and the cosine theorem, triangle \(\Delta A_{1}A_{2}P\) has the following relations:  
\[(R+\Delta R)^{2}=R^{2}+B^{2}-2 RB\text{sin}(\theta-\alpha) \tag{1}\]  
In practice, \(B\) is much larger than \(R\), and \(R+\Delta R\) is much larger than \(R\). Therefore, Equation (1) can be rewritten as follows:  
\[B\text{sin}(\theta-\alpha)=\frac{B_{2}}{2R}-\frac{\Delta R_{2}}{2R}-2R\approx -\Delta R \tag{2}\]  
According to the principle of generating DEM products by the InSAR system [28], the interference phase \(\Delta\phi\) can be expressed as Equation (3), where \(\lambda\) is the wavelength.  
\[\Delta\phi=\frac{-4\pi\Delta R}{\lambda} \tag{3}\]  
According to the geometric relationship in Figure 3a, the downward viewing angle can be obtained:  
\[\theta=\alpha-\text{arcsin}\frac{\lambda\Delta\phi}{4\pi B} \tag{4}\]  
The elevation value \(h\) of point \(P\) is:  
\[h=H-R\cos\theta \tag{5}\]  
Figure 2: Algorithm flow.  
The following can be obtained through simultaneous calculation of Equations (1)-(5):  
\[\Delta\phi=\frac{-4\pi B}{\lambda}\sin(\alpha\cos(\frac{H-h}{R})-\alpha) \tag{6}\]  
According to the simulation parameters in Equation (6) and Table 1, the relationship between the ground elevation value error and the interference phase error is simulated as shown in Figure 3b.  
The elevation value error of 5 m causes a 46.95\({}^{\circ}\) interference phase change, which is monotonic, so that the phase values of the interference fringes also express the rich topographic information contained in the DEM data. Topographic relief features are not specific to a single isolated pixel point but are a regional feature [29]. Traditional matching algorithms, such as SIFT and SURF, extract feature points based mainly on the texture gradient of the image and independently of the terrain orientation. The high degree of similarity between mountains leads to the traditional matching algorithm not being sufficiently effective to meet the requirements. Since the topographic features of the interference fringes are mainly embedded in the mountain range line features as in the DEM data [30], in this study, an algorithm is designed for matching the interference fringes based on this point.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 3 Economy-Financial Analysis  
### Main Calculation  
#### 3.1.1 Net Profit Value--NPV  
The plant's NPV is calculated for the total investment cost of the plant, calculating both operation and maintenance costs using a simple formula assuming constant annual utilization of the system, operating costs and maintenance remains constant and total investment costs, with a focus on year 0:  
\[\mathrm{NPV}=(\mathrm{R}-\mathrm{c}_{\mathrm{om}})\mathrm{k}_{\mathrm{a}}- \mathrm{I}_{\mathrm{T}} \tag{1}\]  
where, \(\mathrm{R}\) is the annual revenue, \(\mathrm{c}_{\mathrm{om}}\) is the cost of operating and maintaining the system, \(\mathrm{I}_{\mathrm{T}}\) is the total investment cost of the plant and  
\[\mathrm{k}_{\mathrm{a}}=\frac{(1+\mathrm{a})^{\mathrm{n}}-1}{\mathrm{a}(1+ \mathrm{a})^{\mathrm{n}}} \tag{2}\]  
where, \(\mathrm{a}\) is the discount rate and \(\mathrm{n}\) is the life of the plant in years.  
For PV installations, while the panels come with a long 25-year performance warranty, the inverter is the weak point of the system that needs to be replaced. Therefore, the NPV formula has been modified to account for substitutions according to Equation (3) [17; 35; 44; 45]:  
\[\mathrm{NPV}=(\mathrm{R}-\mathrm{c}_{\mathrm{om}})\mathrm{k}_{\mathrm{a}}- \mathrm{I}_{\mathrm{T}}-\frac{\mathrm{C}_{\mathrm{inv}}}{(1+\mathrm{a})^{ \mathrm{n}_{1}}}-\frac{\mathrm{C}_{\mathrm{inv}}}{(1+\mathrm{a})^{\mathrm{n}_ {2}}}-\frac{\mathrm{C}_{\mathrm{inv}}}{(1+\mathrm{a})^{\mathrm{n}_{3}}} \tag{3}\]  
where, \(\mathrm{C}_{inv}\) is the replacement cost of the inverters and \(\mathrm{n}_{1}\), \(\mathrm{n}_{2}\) and \(\mathrm{n}_{3}\) is the year in which the replacement will take place.  
NPV shows the feasibility or infeasability of a plant. When the NPV is positive, the investment is recouped, the minimum rate of capital return is achieved, a surplus is obtained, and thus, the plant is viable. When the NPV is negative, the plant is not profitable because the minimum rate of return is not achieved. The higher the discount rate, the lower the NPV.
# An Overview of Fog Data Analytics for IoT Applications
## 3 Data Analytics
### Role of Fog Computing in Data Analytics  
As we have already discussed, big data contain the following characteristics: volume, velocity, variety, and veracity. Dealing with all of these for real-time applications by ensuring availability and deliverability is a cumbersome task for the cloud. Data processing and analytics need to rely on robust and highly scalable messaging systems, commanding software engines for data stream processing and scalable data storage solutions. Fog computing has much potential for solving issues with huge data storage and for quick  
Figure 5: Flow diagram for data analysis.  
data analysis to respond to numerous events that call for prompt decision making and action [28]. Therefore, we introduced the paradigm of fog computing earlier in the paper. Now, we will see how fog computing will help us to achieve all of the benefits that it is known for, especially in data analytics. The role of fog computing in data analysis and the segregation of jobs from the cloud is best explained by Figure 6.  
Fog computing presents a distributed method of computing, as opposed to the holistic approaches of the cloud [29]. Fog data analytics is a field where fog computing is used for data analytics at the edge of the network to reduce latency for applications needing a high availability and faster response times. The fog nodes are installed near the edge of the network, and they distribute the load of data going to the cloud by using virtualization techniques and absorbing all of the raw data from the IoT edge. A fog node can be thought of as a mini cloudlet. Then, these nodes can also perform data analytics on their own, responding directly to the devices, offloading local insights to the cloud in the background, and communicating with neighbor nodes about the learning. Augmented data analytics is widely used for automatic data processing and insight generation. The fog nodes cannot perform all data analytics work like the cloud due to the little resources of the cloud, but they are sufficient for local analytics. The cloud then performs a more complex analysis of all of the data collected from different fog nodes for obtaining global and heuristic insights into data [30].
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
## 5 Analyzing Surface Codes Under Noise
### Surface Codes Under Various Noise Types
Figure 5 shows rotated surface codes outperforming unrotated ones due to their reduced complexity and fewer accumulated errors, as detailed in Table 1. This is also due to fewer accumulated errors from a lower gate count. Hence, from the subsequent subsection onward, we confine our experiments to rotated surface codes exclusively. While repetition codes show higher thresholds, they cannot detect or correct Z errors. This limitation makes them suitable only for specific cases with dominant bit-flip errors, whereas surface codes effectively address both X and Z errors.
# Ultrafast Optical Heating Induced Polarization-Dependent Optical Switching in Gold Nanowires
## 1 Introduction  
Plasmonic optical switching effects have been reported on extensively, and are based on the spectral shift of the localized surface plasmon resonance (LSPR) of the metallic nanostructures [1; 2; 3; 4]. Modification on the electron-electron and electron-phonon scattering processes by the intensive ultrashort laser pulses has been the main physical mechanism [5; 6; 7]. Additionally, ultrafast optical switches have been proposed using nonlinear plasmonic nanoantennas [8]. Using different materials [9] and different shapes [10] of plasmonic nanostructures has diversified and enriched the research work on ultrafast plasmonic optical switching effects. Working at low optical excitation powers or excitation intensities is always a direction to strive for optical switching devices [11]. However, the amplitude of the switching signal is generally very small due to the very small laser-excitation-induced spectral shift. Therefore, amplification mechanisms are required to improve the performance of the optical switching device. For example, Fano coupling between the plasmonic and photonic resonance modes can be utilized to introduce dramatic change in the spectroscopic response, which favors enlargement of the differential spectrum that determines the amplitude of the optical switching signal [12; 13]. Recently, a cross-plasmon has been demonstrated in the three-dimensional (3D) network of gold nanowires, where Fano coupling between Rayleigh anomaly and such cross-plasmon provides us new opportunities to achieve efficient optical switching devices [14]. In fact, such cross-plasmons can be achieved very commonly in metallic nanostructures with very small separation gaps, which depend strongly on the gap width and on the polarization direction of the light. In this work, we investigate the polarization-dependent optical switching effects in a waveguide-gold-nanowire grating. We used the thermal effects induced by femtosecond laser pulses to modify a cross-plasmon in the discontinuous gold nanowires. The thermal expansion of the gold nanostructures and the scattering of the hot plasmonic electrons have been responsible for such optical switching effects.
## 5 Conclusions  
The deployment of WRSNs may require a large number of sensors. The use of fewer chargers in WRSNs to provide sufficient power for the sensors, reduce deployment costs, and maintain the operation of WRSNs is an important issue. Therefore, this study proposes an SMRU algorithm that uses fewer directional chargers to cover all sensors in WRSNs. In our proposed SMRU, the deployment position of the charger can be calculated in a short time, and all sensors in WRSNs are covered with a small number of chargers. Additionally, all sensor power is supplemented to avoid sensor interruption due to power consumption that causes WRSNs to fail and reduces the cost of deploying WRSNs. The performance of the proposed algorithm was compared with that of the RPRO and PBAD algorithms. Experimental results show that the average number of chargers used by the SMRU algorithm is fewer than that of RPRO and PBAD, and the execution time performance is much better than that of PBAD. In the future, we shall design a new algorithm that saves more chargers and computing time.  
Conceptualization, T.-L.L. and H.-Y.C.; methodology, T.-L.L. and H.-Y.C.; software, Y.-H.W.; validation, T.-L.L. and H.-Y.C.; formal analysis, T.-L.L.; investigation, T.-L.L.; resources, H.-Y.C.; writing--original draft preparation, T.-L.L. and H.-Y.C.; writing--review and editing, T.-L.L.; visualization, T.-L.L. and H.-Y.C.; funding acquisition, T.-L.L. and H.-Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology (MOST) of Taiwan, grant number 108-2321-B-415-003- and 109-2321-B-415-005-. MOST provided research funding and devices.  
The authors declare no conflict of interest.