esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Study
### Burst Tests  
The burst test is an intuitive and effective method for evaluating the safety and reliability of a pipe's materials and structure. The pipeline burst test was carried out to measure the ultimate internal pressure bearing capacity, pressure duration, and the corresponding expansion deformation of the PVC pipe. In addition, the failure mode was also studied through the damaged specimen. To validate the theoretical method, 4 groups  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameters** & \(\sigma_{y}\) **(MPa)** & \(\sigma_{t}\) **(MPa)** & \(\epsilon_{t}\) & \(E\) **(MPa)** & \(\sigma_{t}\) **(MPa)** & \(\epsilon_{t}\) **(m)** & \(\kappa\) **(K)** \\ \hline value & 43.67 & 45.15 & 0.047 & 1259.43 & 47.28 & 0.046 & 0.046 & 57.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: PVC material tensile testing result.  
Figure 7: Stress–strain curve of PVC material and tensile loading device: (**a**) tensile testing and (**b**) stress–strain curve.  
of 16 specimens were prepared for burst tests and placed at 23 \({}^{\circ}\)C before water injection and pressurization (Figure 8). The pipe samples used in the test were all obtained from the same batch of products. The details of the PVC pipe geometry values are listed in Table 2.  
The burst test was conducted under room temperature according to ASTM D1599-18 standard. The testing equipment used to apply sustained internal pressure on the PVC pipes is shown in Figures 9 and 10. Before the test, both ends of the PVC pipe were blocked by test fixtures. The tie bar connected two end closures with a bolt and sealed each side of the PVC pipe with O-rings. Before pressurization, the prepared pipes were placed in a burst basin at the prescribed water temperature for 6 h. Then, the switch was turned on and started filling the pipe with water at 23 \({}^{\circ}\)C. The pressurizing device was composed of a PLC-controlled pressurizing pump, a water pressure sensor, and a feeding system. The pressurizing rate was controlled by water flow, and the pressurization accuracy reached up to 0.01 MPa. Lastly, we linked the water inlet to the water pump with a high-pressure hose and injected water to increase the internal pressure of the PVC pipe until it ruptured. The internal pressure value of the pipe was obtained by the pressure gauge installed at the end closure. Two strain gauges were attached to the outer wall of the pipe to monitor the strain change during the pressurization process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pipe Number** & **Length (\(L\)/mm)** & **Wall Thickness (\(t\)/mm)** & **Mean Diameter (\(D\)/mm)** & **SDR(\(D\)/\(t\))** & **Burst Pressure (\(P_{g}\)/MPa)** \\ \hline P1200-1 & 1800 & 45.9 & 1200 & 26 & 3.66 \\ P1200-2 & 1800 & 36.7 & 1200 & 33 & 2.90 \\ P1200-3 & 1800 & 29.4 & 1200 & 41 & 2.32
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
###### Abstract  
Visible Light Communication (VLC) systems are favoured for numerous applications due to their extensive bandwidth and resilience to electromagnetic interference. This study delineates various constructions of Optical Orthogonal Frequency Division Multiplexing (O-OFDM) approaches employed in VLC systems. Various factors are elaborated within this context to ascertain a more effective O-OFDM approach, including constellation size, data arrangement and spectral efficiency, power efficiency, computational complexity, bit error rate (BER), and peak-to-average power ratio (PAPR). This paper seeks to assess these approaches' BER and PAPR performance across varying modulation orders. Regrettably, in VLC systems based on OFDM methodology, the superposition of multiple subcarriers results in a high PAPR. Therefore, this study aims to diminish the PAPR in VLC systems, enhancing system performance. We propose a non-distorting PAPR reduction technique, namely the Vandermonde-Like Matrix (VLM) precoding technique. The suggested technique is implemented across various O-OFDM approaches, including DCO-OFDM, ADO-OFDM, ACO-OFDM, FLIP-OFDM, ASCO-OFDM, and LACO-OFDM. Notably, this method does not affect the system's data rate because it does not require the mandatory transmission of side information. Furthermore, this technique can decrease the PAPR without impacting the system's BER performance. This study compares the proposed PAPR reduction technique against established methods documented in the literature to evaluate their efficacy and validity rigorously.  
VLC; MCM; LACO-OFDM; ASCO-OFDM; PAPR Article  
1  
E.K.I.; [PERSON], [PERSON]; [PERSON], [PERSON]; Optica-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation. _Sensors_**2024**, 24, 26-5, [[https://doi.org/10.3390/s24102965](https://doi.org/10.3390/s24102965)]([https://doi.org/10.3390/s24102965](https://doi.org/10.3390/s24102965))  
1
## 4 Experiments and Analysis
### Comparison Experiment  
Table 2 shows the results from the ten-dimensional tests derived from the five CEC2014 test sets. Every algorithm was executed individually on each test function with the same machine, completing a total of 20 runs, while addressing the resource-intensive optimization challenge. Evaluations were confined to a peak of 120 for 10D and 240 for the 20-dimensional (20D) tasks, starting with a population size of 50. These numerical trials took place with decision variable dimensions set at D = 10 and D = 20, specifically targeting the resource-intensive optimization issue. The convergence profiles of ASAPSO, PSO, GPEME, and CALSAPSO on four functions are illustrated in Figure 3, Figure 4, Figure 5, and Figure 6, respectively. From Figures 3-6, it can be further substantiated that ASAPSO exhibits faster convergence on almost all functions compared to other compared algorithms. In all tables, the best ranks are shown in bold. Tables 3 and 4 is the Friedman test of all algorithms on 10D and 20D, respectively.  
Figure 4: Convergence of ASAPSO, PSO, GPEME, and CALSAPSO on the D = 10, D = 20, Griewank function.  
Figure 5: Convergence of ASAPSO, PSO, GPEME, and CALSAPSO on the D = 10, D = 20 Rastrigin Function.  
Figure 6: Convergence of ASAPSO, PSO, GPEME, and CALSAPSO on the D = 10, D = 20 Rosenbrock Function.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Ranking** \\ \hline
**ASAPSO** & **1.00** \\ PSO & 4.00 \\ GPEME & 2.75 \\ CALSAPSO & 2.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Friedman test of algorithms on 10D.  
The experimental results reveal that ASAPSO, due to its effective transition between local and global surrogate models, can more successfully explore the locally uncertain regions where potential optimal solutions may exist. Therefore, ASAPSO exhibits optimal search performance. Moreover, the successful operation of the transition mechanism also benefits from the accurate evaluation of the models.
## 5 Discussion  
The mitigation of sound is a scientific and technological challenge for several applications. Recently, the diffusion of sound produced by sonic crystals, as 2D periodic arrays of rigid scatterers embedded in air, has been used as a noise mitigation mechanism to reduce the sound level around an acoustic source placed in front of this periodic system [14]. These results are a proof of concept in which the main motivation is to incorporate these crystals in future launch-pad designs as a broadband noise reduction system for aerospace applications [48]. However, sonic crystals are not efficient at low frequencies and in the previous work only the diffusion was used as mitigation mechanism. In the current work, we introduce absorption at low frequencies combined with the multiple scattering processes that arises from the discreteness of the array allowing also diffusion of sound at higher frequencies.  
Figure 8 shows the ILR of the designed structure in this work covering the low frequency range, in which the absorption has been optimized, and the medium/high frequencies, in which the diffusion of sound, i.e., the reduction of specular reflections, is activated. On the one hand, the perfect absorption between 400 Hz and 560 Hz of the proposed array, produces a peak of ILR in which sound is dissipated in the structure due to viscothermal losses. It is worth noting here that this kind of behavior would be rarely obtained employing sonic crystals. In fact, in order to have similar peaks of ILR with sonic crystals, huge periodic structures with periodicity of the order of the wavelength of the frequencies to be attenuated would be required. On the other hand, at higher frequencies the proposed structure presents similar diffusion as the sonic crystals producing several peaks of ILR. In this range of frequencies the wave can penetrate in the structure and activate the multiple scattering mechanism together with the higher mode resonances.  
At this stage it is interesting to evaluate the overall ILR as evaluated in reference [14], in order to see what are the capabilities of the system to mitigate sound. The overall ILR calculated by integrating the ILR in the whole range of frequencies (200-1700 Hz; where the plane wave approximation in our system is fulfilled) is 5 dB. This shows an improvement of the noise mitigation properties with respect  
Figure 7: Diffusion coefficient of the proposed system of Helmholtz resonators with cavities compared to the one obtained for a system of 5 Helmholtz resonators without cavities optimized for maximum absorption at the same range of frequencies.  
to the previous analyzed sonic crystals (where a 2 dB level decrease was predicted) by combining the effect of absorption at low frequencies, and diffusion at medium/high frequencies.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 7 Simulation Verification
### Simulation Results of the Proposed Dual-Mode Quadrature Oscillator in Figure 3  
The VM quadrature sinusoidal output waveforms of the proposed resistorless dual-mode QO circuit in Figure 3, with \(g_{mki}=1\) mA/V and \(C_{1}=C_{2}=100\) pF, are shown in Figure 13. The circuit was designed to oscillate at a frequency of \(f_{osc}=1.59\) MHz. The simulated frequency of oscillation was recorded at 1.58 MHz, exhibiting a phase difference of 92.85\({}^{\circ}\) between the quadrature outputs. Figure 14 displays the corresponding Lissajous plot (\(x\)-\(y\) mode) between \(v_{osc1}\) and \(v_{osc2}\). The total harmonic distortion analysis of \(v_{osc1}\) is also derived, yielding a value of approximately 1.89%. An additional pertinent concern is the phase noise performance of the oscillator. As the next step, the phase noise of both outputs, \(v_{osc1}\) and \(v_{osc2}\), is determined, and the obtained results are plotted in Figure 15. Table 4 provides the phase noise performance data evaluated at various relative frequencies.  
Furthermore, Figure 16 illustrates the simulated quadrature output waveforms of \(i_{osc1}\) and \(i_{osc2}\) in Figure 3. Note that the waveform appears visibly mismatched in both amplitude and symmetry. These asymmetries could indeed stem from the differences in the VDGA implementation shown in Figure 3 and the biasing between the two signal paths. To address this issue, the layout-level symmetry or tuning mechanisms should consider and potentially incorporate phase and amplitude error metrics. The electronic tunability of both calculated and simulated \(f_{osc}\) is plotted in Figure 17, with changing \(g_{mB1}\) value  
Figure 13: Simulation results for \(v_{osc1}\) and \(v_{osc2}\) of the proposed dual-mode QO circuit in Figure 3. (**a**) Initial state; (**b**) steady state.  
while keeping \(g_{mA1}\) constant at 1 mA/V. According to Figure 17, the useful \(f_{\text{osc}}\) ranges from roughly 750 kHz to 1.87 MHz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Frequency**} & \multicolumn{2}{c}{**Phase Noise (dBc/Hz)**} \\ \cline{2-3}  & \(\mathbf{v_{osc1}}\) & \(\mathbf{v_{osc2}}\) \\ \hline
1k & \(-35.50\) & \(-34.80\) \\
10k & \(-55.80\) & \(-55.80\) \\
100k & \(-79.10\) & \(-78.90\) \\
1M & \(-101.20\) & \(-99.80\) \\
10M & \(-123.40\) & \(-119.50\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Phase noise performance of the proposed QO circuit evaluated at various frequencies.  
Figure 14: Simulated Lissajous plot between \(v_{osc1}\) and \(v_{osc2}\).  
Figure 15: Phase noise of \(v_{osc1}\) and \(v_{osc2}\) of the proposed dual-mode QO circuit in Figure 3.
# C3 VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images
## 4 Results
### Representing Microglia as Graphs  
C3 VFC is an automatic tracing algorithm that detects the cells in an image, traces the branches back to the soma, and also converts the tracing into a graph for bioinformatics analysis. Microglia comprises of a soma and processes that extends from it. This cell structure could be represented as a tree, or an acyclic undirected graph [53; 54]. An acyclic graph is a graph with no loops. An undirected graph contains edges with no direction. Our method represents each foreground pixel in the traced skeleton as a vertex and is connected by an edge link, with a weight value of the Euclidean distance between connected pixels, where the graph is defined as \(G=\{V,E,W\}\). We use the mathematical tree representation of the microglia to acquire structure information and complexity analysis of the cell structure. A bifurcation is when the path divides into two parts. In microglia, the ramification and addition of new processes over time is telling of its environment. Microglia are highly ramified as it is surveying its surroundings. However, the processes are less ramified, with less bifurcations, in a diseased setting, as described in Section 1.  
Figure 12 depicts a microglia represented as a tree, with a different color constituting a different hierarchy of the paths. The path hierarchy of a vertex is defined in [55] as the number of bifurcations that is crossed when traversed back to the root node or the soma. The paths closest to the root node will have a value of one and the hierarchy number increases with more bifurcations.
## 3 Experimental Results
### CO Detection Device  
Figure 11 shows our CO detection device. For its construction, the electronic circuit which was shown in Figure 9 and the DC supply source shown in Figure 10 were considered. The Printed Circuit Board (PBC) was designed using the CAD Altium design program, version 17. Finally, the required materials were: a transformer 120:24 Volts, a diode bridge KBL610 (3A), seven capacitors (\(C_{1}=C_{2}\), \(=C_{3}=C_{4}=2200\)\(\mu\)F (50 V), \(C_{5}=C_{6}=0.1\)\(\mu\)F, \(C_{7}=1\) nF), seven precision resistors (\(R_{1}=R_{2}=R_{3}=R_{4}=R_{5}=R_{6}=I\) K\(\Omega\), \(R_{7}=2\) K\(\Omega\)), a potentiometer (\(R_{v}=10\) K\(\Omega\)), four rectangular cermet trimmer potentiometers (\(R_{x}=1\) K\(\Omega\), \(R_{AF}=1\) K\(\Omega\), \(R_{G1}=10\) K\(\Omega\), \(R_{G2}=10\) K\(\Omega\)), a TL084 (3 V to 32 V, DIP, 14 Pines), a phenolic one side plate (15 cm \(\times\) 15 cm), three screw connectors, an LM317T (regulator to 5 V), an LM7812 (regulator to 12 V), and an LM7912 (Regulator to \(-\)12 V).  
In the CO detection device, the sensitivity was increased collocating two resistors, \(R_{G1}\) and \(R_{G2}\). That is possible because \(R_{4}\) and \(R_{G1}\) were connected using a serial array whereas \(R_{6}\) and \(R_{G2}\) were also connected to applying a serial array. Therefore, Equation (8) is satisfied if and only if \(R_{G1}=R_{G2}\). The output voltage \(V_{o}\) will be  
\[V_{o}=A_{m}(V_{B}-V_{A}) \tag{11}\]  
where the modified amplification is given by \(A_{m}=\frac{R_{4}+R_{G1}}{R_{3}}=\frac{R_{6}+R_{G2}}{R_{5}}\) and its value can only be between 1 and 11.  
On the other hand, the device operates as Figure 12 illustrates. If CO concentration is equal or greater than 50 ppm, the sensor's resistance diminishes and as a consequence, the Wheatstone bridge is unbalanced, provoking the signal alarm \(V_{Alarm}\approx 11.2\) V (Alarm state \"On\"). However, if CO concentration is lower than 50 ppm, the Equation (6) is not satisfied and then the signal alarm is close to zero, \(V_{Alarm}\approx 0\) V (Alarm state \"Off\"). For our new toxic gas detector device, the thresholding value can be selected through \(R_{x}\): if \(R_{x}>560\)\(\Omega\) then the device will detect CO concentrations lower than 50 ppm (or higher) but if \(R_{x}<560\)\(\Omega\) then the device will detect CO concentrations greater than 50 ppm.  
Figure 10: Electronic diagram of our DC supply voltage.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 3 The Proposed Framework
### The Dual-Stream Framework  
#### 3.1.1 Illumination Maps  
The illumination map is an inherent attribute of the image and difficult to process uniformly, which can be considered as a major indicator for splicing forgery detection. GE and IIC are two state-of-art illumination maps, which are effective for mapping the tampered regions in splicing. GE assumes that the average reflection of the object surface has no color difference, which is easy to obtain and has low computational complexity. The process of GE illumination estimation \(GE_{ill}\left(x\right)\) of pixel \(x\) can be formulated as:  
\[GE_{ill}(x)=\frac{1}{k}(\int\left|\frac{\partial^{n}f^{\sigma}(x)}{\partial x ^{n}}\right|_{p}dx)^{1/p} \tag{1}\]  
where \(k\) is the scale factor, \(\left|\cdot\right|\) represents the absolute value, \(f^{\sigma}(x)\) denotes the intensity through Gaussian filter with kernel \(\sigma\), \(p\) is the Minkowski norm, \(n\) is the order of the derivative.  
The IIC applies the inverse intensity and chromaticity estimation to recolour each pixel, which has fewer surface colors and good robustness. The IIC illumination estimation \(IIC_{ill}\left(x\right)\) on channel \(c\in\left\{R,G,B\right\}\), is formulated as:  
\[IIC_{ill}(x)=m(x)\frac{1}{\sum_{c\in\left\{R,G,B\right\}}p_{c}(x)}+\gamma_{c} \tag{2}\]  
where \(m(x)\) is a parameter that depends on the surface orientation, diffuse chromaticity and specular chromaticity; \(p_{c}(x)\) is the color intensity, \(\gamma_{c}\) is the chromaticity on channel \(c\).
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 5 Simulation Analysis
### Simulation Results
#### 5.2.3 ML Robustness to Other SNRs  
As shown in Figure 10, the model of the proposed method was trained with only the set SNR, and the performance is compared by entering data for each SNR. In both scenarios, there is little performance loss compared to the correct trained models for each SNR. Moreover, in the \(L=1\) scenario, below about 10 dB, the model trained with 10 dB data performs better than that trained with 20 dB data, but above about 10 dB, the model trained with 20 dB data performs better than that trained with 10 dB data. Consequently, the proposed method is robust against other SNR data, as there is little performance loss even if SNR data different from that of the trained data are inputted.  
Figure 10: Performance based on different SNR data than the trained SNR data.  
Figure 9: Performance comparison between proposed algorithms with 1 and 3 hidden layers.
## 6 Conclusions  
In this paper, a crack detection model named Pyramid Hierarchical-Convolution-based U-Net (PHCNet) with MGAM, EFEM and SAM is proposed by us. Firstly, the Pyramid Hierarchical Convolution Module (PHCM) is used to extract features of cracks with different sizes. Thus the features extracted by PHCM could adapt varying shapes and sizes of cracks. Secondly, the Mix Global Attention Module (MGAM) is used to fuse global feature information, guiding the model in the restoration of high-resolution features with global information. Thirdly, the Edge Feature Extractor Module (EFEM) is used to learn the edge features of cracks. Finally, the Supplementary Attention Module (SAM) is used to solve the interference caused by stains in crack images, since it can continuously remove the crack features that have already been extracted by the model from the high-level output to compel the model to learn new crack features. The experimental results show that our PCHNet could achieve an accuracy of 0.929, 0.823, 0.989 and 0.801 on the Cracktree200, CRACK500, CFD and OAD_CRACK datasets, respectively. This article improves the architecture of existing deep-learning-based crack detection models, solving the problems of multi-scale crack detection, insufficient refinement of crack edge feature extraction, and the influence of interference features in crack samples. However, the model size of the PCHNet is still very large; our future research would focus on lightweight models.  
Conceptualization, X.Z. and H.H.; methodology, X.Z.; software, X.Z.; validation, X.Z. and H.H.; formal analysis, X.Z. and H.H.; investigation, X.Z. and H.H.;
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 3 System Model
### 5G Threats
#### 3.2.4 Attacks against Authentication
According to the proposed architecture, designing a security approach, which protects the systems from DDoS and Malware attacks, becomes more complicated and dynamic. By introducing the concepts of SDN and NFV, the design of SDSec will be as follows:  
* The softwareized components of the network, i.e., the NFV version of routers, switches, and firewalls, should be integrated with the SDN environment, which enables the monitoring topology types and manages the softwarized devices (NFV functionalities) directly and indirectly via their own element managers. * SDN controllers have the capability to control the traffic flows and communication between points and to implement the security policy. Additionally, information on the network and the traffic analytics can be collected and processed by the SDN. * Network security components can be applied through northbound APIs with the SDN controller in order to detect and respond to spoofing DDoS attacks. The advantage of SDN is that it can efficiently detect the DoS attacks and achieve optimal network wide effectiveness; however, it enforces overhead to the network access as well as overhead to network utilization performance.
## 6 Technical Challenges in IoT Integration for Smart Buildings
### Data Administration  
The substantial amount of data produced by IoT devices requires sophisticated data management solutions for proper handling and analysis. Identifying suitable storage options that can expand with data expansion while ensuring accessibility is crucial for extracting insights from this data. Numerous applications need real-time data analysis for prompt decision-making, requiring substantial processing capacity and effective algorithms. Moreover, safeguarding data throughout its lifecycle--encompassing collection, storage, and transmission is essential for ensuring user privacy and adhering to rules. Inadequate data management may result in operational inefficiencies, missed insights, and even data breaches, compromising the overall efficacy of smart building systems.
# A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data
## 5 Conclusions
The centroid-based approach and rough to fine-scale optimization (100 m \(\times\) 100 m to 2 m \(\times\) 2 m) offered precision without restricting very high time constraints.