esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Signal modulation recognition is often reliant on clustering algorithms. The fuzzy c-means (FCM) algorithm, which is commonly used for such tasks, often converges to local optima. This presents a challenge, particularly in low-signal-to-noise-ratio (SNR) environments. We propose an enhanced FCM algorithm that incorporates particle swarm optimization (PSO) to improve the accuracy of recognizing M-ary quadrature amplitude modulation (MQAM) signal orders. The process is a two-step clustering process. First, the constellation diagram of the received signal is used by a subtractive clustering algorithm based on SNR to figure out the initial number of clustering centers. The PSO-FCM algorithm then refines these centers to improve precision. Accurate signal classification and identification are achieved by evaluating the relative sizes of the radii around the cluster centers within the MQAM constellation diagram and determining the modulation order. The results indicate that the SC-based PSO-FCM algorithm outperforms the conventional FCM in clustering effectiveness, notably enhancing modulation recognition rates in low-SNR conditions, when evaluated against a variety of QAM signals ranging from 4 QAM to 64 QAM.  
modulation recognition; fuzzy c-means algorithm; constellation diagram; particle swarm optimization algorithm; MQAM signal +
Footnote †: journal: Information  
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## 4 Discussion
### Uncertainty Analysis  
As described in Section 2.2, we used a different dataset for the model calibration and global LUE and GPP estimation. The uncertainty of the input parameters, such as EF, PAR and Tmean, would introduce error to our LUE and GPP products. In this part, we plotted the time series of GPP, LUE, EF, PAR and Tmean in two sites and compared data for calibration and global extrapolation. Figure 12 shows our PAR and Tmean used for global product match well with FLUXNET PAR and Tmean which agrees with the high correlations of them (both higher than 0.95). By contrast, the correlation between FLUXNET EF and global EF is only 0.681, which means EF had higher uncertainty. In CH-Lae, global EF matched well with FLUXNET EF, then we got a satisfactory LUE and GPP, while US-SRG failed to produce either a very good LUE or GPP because of the uncertainty of EF. Therefore, we think EF is a key error source for global LUE and GPP.  
Figure 11: LUE and GPP curves along latitude. V1: Parameterization approach without the CI; V2: Parameterization approach with the CI; V3: Cubist regression tree approach.
## 3 Results and Discussion
### Performance Tests of VRLA Batteries
#### 3.2.3 Cycling Performance  
Charge and discharge cycle life is a key parameter to measure VRLA battery performance. Figure 5 shows the dependence of the discharge time of four batteries on cycle number at C\({}_{2}\) rate discharge under 100% DoD conditions. The actual discharge capacity of the batteries was the discharge current multiplied by the discharge duration. In the initial cycle, the capacity of the batteries increased because of the activation process. The discharge capacity of the control batteries was reduced sharply during the cycle and failed after only 225 cycles. From this figure, at C\({}_{2}\) rate discharge, the cycle numbers of the CB1, CB2 and CB3 batteries were 350, 350 and 430, respectively, which are 52.2%, 52.2% and 86.9% higher than those of the controls, respectively, indicating that the addition of carbon black to the negative plate of the batteries can extend the cycle life of the batteries. Moreover, the trend of CB1 and CB2 batteries showed no significant difference under 100% DoD test conditions. CB3 batteries have longer cycle life than CB1 and CB2 batteries, which may be due to the uneven dispersion of CB1 and CB2 particles on the plate [34]. In addition, the discharge time of the batteries with CB3 held steady during 300 charge/discharge cycles, but the discharge time decreased rapidly after 300 cycles, possibly due to the stratification of electrolytes and the excessive loss of water in the electrolytes [13; 35]. As the cycle number of the battery increases, a large amount of big and dense PbSO\({}_{4}\) particles accumulate on the surface of the negative plate so that the Ohmic resistance of the batteries continuously increases during the charge/discharge process, causing the capacity to decline prematurely and accelerating battery failure.  
Based on the above results, it is possible to conclude that carbon black with high SSA has better performance than carbon black with low SSA when added to the negative plate, which could effectively inhibit the irreversible sulfation of VRLA batteries and prolong the cycle life [36]. In addition, the number of cycles of CB3 batteries increased by 86.9% compared to the control batteries at 100% DoD conditions. These results can be attributed to the carbon black with greater SSA than acetylene black  
Figure 5: Effect of carbon black on the cycle life of VRLA (48 V/12 Ah) batteries under 100% depth-of-discharge (DoD) conditions at C\({}_{2}\) discharge rate (6 A). Plots of the discharge time and the number of cycles for three different specific surface areas (SSAs) of carbon black are used as negative additives for lead–acid batteries and acetylene black as a control additive.
## 4 Experimental Results  
In this section, numerical simulations of GEO-LEO bistatic SAR in sliding-spotlight mode are conducted to verify the effectiveness of the imaging method. Then, the real data-processing results of an equivalent bistatic SAR experiment are illustrated to further validate the proposed method using the China's GF-3 LEO-SAR [57] as the transmitter and a stationary receiver.
## 2 Related Work
### Related Work on Machine Learning  
An extensive survey on the application of ML and DL to GNSS problems is given in [1], where the detection of interference is one of the considered sub-cases among others (estimate of ionospheric effects, signal detection, positioning), and it is often associated with jamming detection and classification. One of the first studies, which applied ML to jamming identification and classification is [6] where a twin Support Vector Machine (SVM) algorithm (TWSVM) was used for real-time interference monitoring. The TWSVM is the preferred SVM for improved classification performance. In contrast, DL algorithms were not used and the impact of the bit-depth was not considered at all. Another paper, which used shallow Machine Learning algorithms was [7], which adopted a combination of SVM and Principal Component Analysis (PCA) to reduce the feature space. The goal of the authors of [7] was to detect both jamming and spoofing attacks. However, DL was not used. Several recent papers have adopted DL algorithms, demonstrating in general a superior performance to shallow Machine Learning algorithms in this context as discussed in the next sub-section.
## 1 Introduction  
Today, renewable applications, such as energy islands (EIs), have critical roles in efficiently using renewable energy resources, increasing energy independence, and reducing carbon emissions [1]. Reducing energy consumption and improving the efficiency of renewable energy systems (RESs) are crucial sustainable energy goals for contemporary societies. Enhancing the applicability of distributed energy resource (DER) systems is necessary to achieve these goals [2]. These EIs increase renewable energy production, ensure energy supply security, contribute to zero carbon targets, and provide an effective solution in the fight against climate change [3]. Industrial cyber-physical systems can be considered the keystone factor for Industry 4.0 [4]. Thus, in Industry 4.0 systems, it is inevitable to encounter many cyber-physical issues.  
The rapid development of technology, increasing digitalization, and using Internet of the Things (IoT) devices in RESs have made these systems vulnerable to cyber-attacks. These systems can be attacked via IoT devices, supervisory control, and data acquisition (SCADA) systems, as well as cloud-based platforms used in energy production, storage, and distribution processes.
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 4 Mathematical Analysis of Atmospheric Turbulence
### Scintillation  
Scintillation is described as the temporal and spatial fluctuation of the light intensity caused by atmospheric turbulence. The scintillation index, \(\sigma_{I}^{2}\), is defined as the normalized variance of the light wave intensity:  
\[\sigma_{I}^{2}=\frac{<I^{2}>-<I>^{2}}{<I>^{2}} \tag{6}\]  
where \(I\) is a time series of intensity measurements, and the angle brackets denote a time average. The relation between the refractive index structure parameter and \(\sigma_{I}^{2}\) is [52]  
\[\sigma_{I}^{2}=1.23C_{n}^{2}k^{\frac{7}{6}}L^{\frac{11}{6}} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
\begin{tabular}{c} **Temporal Hour** \\ **Interval** \\ \end{tabular} & \(W\) \\ \hline Until \(-4\) & 0.11 & 5 to 6 & 1.00 \\ \hline \(-4\) to \(-3\) & 0.11 & 6 to 7 & 0.90 \\ \hline \(-3\) to \(-2\) & 0.07 & 7 to 8 & 0.80 \\ \hline \(-2\) to \(-1\) & 0.08 & 8 to 9 & 0.59 \\ \hline \(-1\) to 0 & 0.06 & 9 to 10 & 0.32 \\ \hline Sunrise 0 to 1 & 0.05 & 10 to 11 & 0.22 \\ \hline
1 to 2 & 0.10 & 11 to 12 & 0.10 \\ \hline
2 to 3 & 0.51 & 12 to 13 & 0.08 \\ \hline
3 to 4 & 0.75 & Over 13 & 0.13 \\ \hline
4 to 5 & 0.95 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weight Function [48].  
Figure 2: Map of Saudi Arabia with the NEOM City Location Marked [51].  
where \(k=2\pi/\lambda\) represents the wave number, \(\lambda\) is the wavelength, and \(L\) is the transmission distance. The scintillation index is commonly used to classify intensity fluctuation, and its values for weak, moderate, and strong fluctuations are \(\sigma_{I}<1\), \(\sigma_{I}\sim 1\), and \(\sigma_{I}>1\), respectively [52]. Generally, scintillation can result in a high BER.
## 5 Experiments and Results
### Results on Pinion Manufacturing Dataset  
Section 3.2 describes the surrogate model developed for the pinion manufacturing process, which serves as the first step in the proposed framework. Based on the model's predictive performance, it was subsequently employed as the training environment for the RL agent to recommend optimal hub stroke values. Given the relative simplicity of the process, the RL agent achieved strong results within just 50 training episodes. As outlined in Section 3.2, the surrogate model attained an \(R^{2}\) score of 0.97 for flange radius and 0.99 for flange thickness on a held-out test split. The RL agent achieved reward values of 0.864 for flange radius and 0.866 for flange thickness, demonstrating a high success rate in identifying optimal stroke values as control parameters. Both flange radius and flange thickness are conflicting quality objectives. The optimization resulted in target values of 22.3 mm for radius and 3.93 mm for thickness. Figure 5 illustrates the model's performance on the evaluation data, showing the ground truth values, the RL agent's predictions, and the optimal solution point within the radius-thickness design space.
## 3 Precise Positioning Using Uncombined PPP (UPPP) Algorithm
### UPPP Positioning Accuracy Analysis
\begin{table}
\begin{tabular}{l l} \hline Device & Xiaomi Mi8 \\ \hline Measurements used & GPS (L1/L5), GLONASS (L1), Galileo (E1/E5a) \\ \hline Mode & Kinematic \\ \hline Date & 22 November 2022 \\ \hline Duration & 1 h \\ \hline Sampling interval & 1 s \\ \hline Troposphere model & Saastamoinen model \\ \hline Ionosphere model & Global ionospheric maps (GIM) \\ \hline Functional model & UPPP model \\ \hline Stochastic model & C/N0 and elevation weighting function \\ \hline Elevation mask angle & 10 deg \\ \hline C/N0 mask & 20 dB-Hz \\ \hline Satellite orbit & CODE MGEX precise ephemerides (5 min interval) \\ \hline Clock error & CODE MGEX precise clock (1 sec interval) \\ \hline Satellite DCB correction & CAS DCBs in Bias SINEX (BSX) format \\ \hline \end{tabular}
\end{table} 
Table 5: GNSS data information and processing setting. Finally, we should mention that, although there are several open-source Apps generating the typical GNSS observations from the Android location API and saving them into the RINEX format, we must still pay more attention to the generation of GNSS observations as we showed some possible issues in the generated observations.
## 3 Results and Discussion
### Scenario 3 (S3)
#### 3.3.2 Cod  
Similar to scenario 2, after implementation of schemes B and C, the average tidal volume in Haizhou Bay decreased by 1.48% and 0.15%, respectively (Table 19). Different from scenario 1 and 2, after the implementation of scheme D, COD environmental capacity reduced by 8.67%. It appears that the position of working conditions is very crucial, which directly determines the diffusion of pollutants and the EC. After the sequential implementation of schemes E and F, the reduction ratio of COD environmental capacity reached 22.20% and 16.22%, respectively. These two schemes were the same as for scenario 2, so the change in EC was also consistent.  
The feasibility evaluation results of EC demonstrated that the evaluation index \(R_{1}\) of schemes B and C was 10, so scheme B and C were feasible. The evaluation index \(R_{1}\) of scheme D was 4, and scheme D was also basically feasible. The evaluation index \(R_{1}\) of schemes E and F was 0, and schemes E and F were not feasible, that is, working conditions HZ2 and HZ4 were prohibited.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reclamation Scheme** & **COD (t/a)** & **EC Change (\%)** & \(\mathbf{R_{1}}\) \\ \hline A & 62,444 & - & - \\ B & 61,517 & 1.48 & 10 \\ C & 62,350 & 0.15 & 10 \\ D & 57,033 & 8.67 & 4 \\ E & 48,584 & 22.20 & 0 \\ F & 52,314 & 16.22 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Feasibility evaluation of COD environmental capacity.  
Figure 9: Computed region of marine EC (scenario 3). Computed region of plan A; computed region of plan B; computed region of plan C; computed region of plan D; computed region of plan E; computed region of plan F.
## 3 Materials and Methods
### Land Use and Land Cover Assessment and Change Detection  
The LULC data in 2019 were first visually assessed from Pleiades and SPOT imagery using the element of visual interpretation [22; 23]. In this study, the LULC classes were (1) urban and built-up areas, (2) paddy fields, (3) field crops and horticulture, (4) perennial trees and orchards, (5) aquaculture areas, (6) idle land, (7) evergreen forests, (8) mangrove forests, (9) scrub forests, (10) water bodies, and (11) miscellaneous land. Then, the thematic accuracy of the preliminary 2019 LULC map was assessed in a field survey in 2020 using  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Data Collection** & **Source** \\ \hline Remote sensing & Pleiades and SPOT imagery in 2019 & CNES/Airbus, Maxar Technologies (Google satellite maps) \\ GIS & 1. Administrative boundary & DEQP \\  & 2. LULC data in 2002 and 2014 & Boonchoo [21] \\  & 3. Digital elevation model & USGS \\  & 4. Soil series & LDD \\  & 5. Stream & DEQP \\  & 6. Road & PSO MOT \\ Primary data & Field survey for ground-truthing in 2020 & \\ Secondary data & 1. Daily weather between 1996 and 2019 & TMD and SRHC RID \\  & 2. Daily weather between 2000 and 2010 & NCEP \\  & 3. Daily runoff observed between 1999 and 2019 & SRHC RID \\  & 4. Registered population between 1993 and 2020 & DOPA MOI \\  & 5. Census data between 1960 and 2010 & NSO \\  & 6. Tourist arrivals data between 1993 and 2020 & TATIC TAT and ETSD MOTS \\  & 7. The average income of the population & NSO \\ \hline \hline \end{tabular} Note: USGS: The United States Geological Survey; LDD: Land Development Department; DEQP: Department of Environmental Quality Promotion; PSO MOT: Permanent Secretary Office, Ministry of Transport; TMD: Thai Meteorological Department; SRHHC RID: Southern Region Irrigation Hydrology Center, Royal Irrigation Department; NCEP: The National Centers for Environmental Prediction; DOPA MOI: Department of Provincial Administration, Ministry of Interior; NSO: National Statistical Office; TATIC TAT: TAT Intelligence Center, Tourism Authority of Thailand; ETSD MOTS: Economics Tourism and Sports Division, Ministry of Tourism and Sports.  
\end{table}
Table 1: Details of data collection and preparation for analysis and modeling in the study.  
Figure 2: Workflow of research methodology.  
660 randomly stratified sampling points based on the multinomial distribution theory with a 95% confidence level and 5% precision [24]. Finally, based on the collected and interpreted LULC data, changes were assessed using a post-classification comparison change detection algorithm, which is widely used to extract \"from-to\" change class information [25; 26].
## 4 Description of Possible Deployment for Overlay Topologies  
To deploy a VPN, many HW and SW solutions are available (e.g., use Mikrotik Routerboards, _Mikrotik_, [[https://mikrotik.com/](https://mikrotik.com/)]([https://mikrotik.com/](https://mikrotik.com/)) accessed on 23 June 2024, or, for example, with a Debian OS _DEBIAN OS_[[https://www.debian.org/index.it.html](https://www.debian.org/index.it.html)]([https://www.debian.org/index.it.html](https://www.debian.org/index.it.html)) accessed on 23 June 2024).  
Three macro application scenarios, shown in Figures 2-5, are described in [23] and deployed both with VPNs and Overlay TLS.  
These solutions guarantee secure transit through encryption algorithms such as AES (128-512) and elliptic curve implementations like ecp192. They offer a range of authentication methods, from PSK + XAUTH to certificate-based and two-step solutions using OTP or additional credential files like RADIUS + VPN IPsec/OpenVPN/OpenConnect, tailored for diverse devices and networks.  
This work assesses the current landscape of communication infrastructures (WI-FI, CABLE, 4G/5G) and the devices that can be connected with those infrastructures (NUC, mini PC, Raspberry Pi, Smartphones). It aims to provide the broadest support for various devices and technologies while ensuring optimal performance and security.  
Table 2 summarizes the implemented testbeds, outlining the network infrastructure and software used. Several of the proposed architectures remain in active use, including OpenVPN and WireGuard, as part of the COGITO project, [[https://www.icar.cnr.it/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/accessed](https://www.icar.cnr.it/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/accessed)]([https://www.icar.cnr.it/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/accessed](https://www.icar.cnr.it/progetti/cogito-sistema-dinamico-e-cognitivo-per-consentire-agli-edifici-di-apprendere-ed-adattarsi/accessed)) on 23 June 2024, Themes to interconnect the cloud and local offices.  
With the rapid expansion of technologies such as IoT [29] and the evolution of communication paradigms like Fog Computing and cloud-based solutions (e.g., AMAZON AWS), or multi-site clusters (e.g., through Kubernetes), it is essential to manage the increasing data volume while ensuring compliance with GDPR.  
The OpenWRT operating system allows these technologies to be implemented on compatible routers and devices. The wide range of supported bridges, particularly for IoT networks, enables interfacing with third-party protocols (e.g., LoRa and ZigBee) and traditional TCP/IP stack protocols. The x86-64 versions support virtualization, allowing for the secure and cost-effective creation of Fog networks of microservices and IoT multi-protocol networks. For instance, outdoor sensor networks can be installed in areas covered by 4G/5G networks and configured locally using an Edge Computing approach.  
Figure 4: Hub and spoke Overlay network scenario.  
Figure 5: Mesh Overlay network scenario.
# Axial Compression Behavior of Steel Angle-Corrugated Steel Plate-Confined Concrete Columns
## 6 Summary
#### 5.1.1 GB50936-2014  
The calculation principle of GB50936-2014 is a unified theory that regards concrete and steel as a unity. The confinement index \(\zeta\) is introduced to describe the confined effect between concrete and steel. The ultimate bearing capacity of the SA-CS column can be calculated as in the following Equations.  
\[N_{\rm u-GB}=\varphi N_{\rm sc}=\varphi A_{\rm c}f_{\rm sc} \tag{15}\]  
\[f_{\rm sc}=(1.212+B\zeta+C\zeta^{2})f_{\rm c} \tag{16}\]  
\[B=\frac{0.131f_{\rm y1}}{213}+0.723 \tag{17}\]  
\[C=\frac{-0.07f_{\rm c}}{14.4}+0.026 \tag{18}\]  
where \(\varphi\) is the stability coefficient under axial compression, whose value is 1.0;\(f_{\rm sc}\) is the compressive strength of the unity.  
The mean value of the ratio of the simulated results to calculated results (\(N_{\rm u-FEM}/N_{\rm u-Gal}\)) is 0.806, and the standard deviation is 0.038. The estimated results of the ultimate bearing capacity have poor dispersion, and the calculated value is too large. In GB50936-2014, limits are specified for the width-to-thickness ratio (B/t) of square CFST, which should not be greater than \(60\sqrt{235/f_{\rm y1}}\). However, the B/t of experimental specimens and numerical models both exceed the limit, which might be the reason for the deviation between the calculated and actual results.