esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Results with Dual-Polarimetric Data
#### 4.2.1 Enhancement Performance with X-Band Dual-Pol Data
It is worth noting that, although the three polarimetric images were acquired on different dates, meaning sea conditions might have changed and the selected ships were not the same in all cases, the improvement after applying the optimization remains very similar. In fact, an increase of approximately 10 dB with respect to the best conventional detection channel (either HH or VH/HV) is observed. Figure 7: Optimization results of the dual-polarimetric \(\mathrm{HH/HV}\) image acquired by PAZ over the coast of Rio Grande (Brazil). Figures (**a–c**) represent the image intensity in logarithmic scale ranging from \(-35\) to \(5\) dB. Figure (**d**) represents a transect through the ship highlighted with the red rectangle in (**a**).
# Damping Assessment of Lightweight Timber Floors Under Human Walking Excitations
## 2 Materials and Methods
### Floor Vibration Serviceability Parameter  
For evaluating the dynamic vibratory performance of a floor, it is necessary to calculate some reference parameters that can be compared with acceptance criteria established in regulations. In  
Figure 9: Logical diagram of damping ratio estimation based on the Stochastic Subspace Identification (SSI) method. Extracted from ARTeMIS Modal Pro [21].  
order to study the influence of the damping ratios on the floors' dynamic serviceability, the vibration dose value (VDV) was selected. VDV is a time-accumulated vibration parameter that correlates well with the subjective perception of people exposed to vibrations. The physical meaning for this is based on two aspects: (i) VDV uses the fourth power of accelerations because people are sensitive to the maximum acceleration intensity, and (ii) VDV measures accumulated vibrations, instead of averaging it, because people feel more discomfort when they are exposed to long-duration vibrations. For these reasons, the VDV parameter has been recommended by relevant international regulations [23; 24] and is currently being used by researchers to evaluate the vibration performance of wooden floors [10]. Its mathematical expression is given by the fourth root of the time integral of the fourth power of the acceleration after it has been frequency weighted (\(a_{w}\)). When the vibration conditions are constant or repeated regularly, only one representative sample, of duration \(\tau\) in seconds, needs to be measured (Equation (1)). The acceleration must be weighted, as how people perceive building vibrations depends on various factors, including the vibration frequency and direction. For vertical accelerations, the weighting curve modulus demonstrates the maximum sensibility in the frequency range from 4 Hz to 12.5 Hz.  
\[VDV_{\tau}=\left\{\int\limits_{0}^{\tau}[a_{w}(t)]^{4}dt\right\}^{0.25} \tag{1}\]  
On the other hand, it is necessary to extend the effect of an intermittent vibration of a short duration to a daytime of 16 h [24]. According to [25], in residential buildings, quiet scenarios of people walking are generally expected, so a regular short-duration event of vibration induced by walking should occur every 15 min, that is, 32 times in 16 h. Therefore, the VDV associated with a 16 h daytime can be expressed as shown in Equation (2):  
\[VDV_{day}=N^{0.25}.VDV_{\tau}=32^{0.25}.VDV_{\tau}=2.38\cdot VDV_{\tau} \tag{2}\]  
where \(VDV_{day}\) is the vibration dose value for 16 h daytime exposure (in m\(\cdot\)s\({}^{-1.75}\)), and N is the number of times an intermittent vibration of t seconds of duration occurs in 16 h.  
Finally, the \(VDV_{day}\) obtained from the measurements must be compared with the vibratory performance criteria recommended by [24]. The judgment made is of the probability that the determined vibration dose might result in an adverse comment by those who experienced it (Table 4).
# Optimal Thresholding for Multi-Window Computed Tomography (CT) to Predict Lung Cancer
## 2 Proposed Method
### Lung Segmentation
#### 2.2.2 Segmentation in Mediasthal Window  
We recall that mediastinal window images usually show two types of gray level intensities; however, in some cases, there are more than two groups, which results in inaccurate thresholds computed with the multi-Otsu method. For instance, lung segmentation on a sample mediastinal window using the multi-Ostu threshold is shown in Figure 5. Figure 5b shows the histogram of the mediastinal image shown in Figure 5a. The segmentation result using this threshold is shown in Figure 5c. Many patches can be noted in the segmented image, which have been incorrectly classified.  
Figure 4: Lung segmentation results using the proposed algorithm; in each pair, the left is the test image and the right is the segmented lung.  
Figure 3: The two threshold values given by the multi-Otsu algorithm on the lung window shown in Figure 1a.  
Analysis of the mediastinal window histograms shows that the incorrect threshold occurs when there are a few closely appearing peaks on the higher end of the histogram, instead of one large peak. The Otsu method in such cases sometimes results in converging the threshold between these peaks instead. A simple strategy to avoid an incorrect threshold for lung segmentation in the mediastinal window is to compute the image's global mean and use it as a threshold value. Let \(I\) be an image of size \(M\times N\); the global mean \(\mu_{g}\) of \(I\) can be estimated as  
\[\mu_{g}=\left[\frac{1}{MN}\sum_{x=1}^{M}\sum_{y=1}^{N}I(x,y)\right] \tag{1}\]  
This mean serves as a better threshold than that estimated with the Otsu method in many cases; however, it still can suffer from bias toward the larger part. Specifically, if there is a significant number of large values (gray) compared to the background (black), the mean can be found in a similar region to the Otsu threshold. In Figure 6, we present some results obtained using the global threshold for lung extraction. The top two rows show the results where segmentation using global-mean-based thresholding produced good results, and the bottom two rows show some failure cases.  
To overcome this problem, we use the global mean to roughly separate the image into two regions, \(\Omega_{1}\) and \(\Omega_{2}\). The set \(\Omega_{1}\) consists of those image pixels that lie in the interval [0, \(\mu_{g}\)], and \(\Omega_{2}\) contains the rest of the pixels, i.e., the pixels with intensity lying within [\(\mu_{g}\), 255]. The local means of the two regions are computed and used to find a better threshold. The local mean \(\mu_{1}\) of the region \(\Omega_{1}\) is then computed.  
\[\mu_{1}=\frac{1}{|\Omega_{1  
watershed algorithm [39; 40]. The obtained lung mask is used as the seed for the watershed algorithm. The third column in Figure 7 shows the result after applying the watershed algorithm. The resulting images present accurate lung segmentation.
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 6 Implementation Results and Discussion
### Comparative Analysis  
To facilitate a comparative analysis, the Verilog hardware description language (HDL) was employed to model the architecture. It was subsequently subjected to simulation to verify its functionality using a test pattern generated by a C++ simulator. The design functions were verified successfully, following which, the architecture was synthesized while adhering to suitable time and area constraints. Synopsys design tools were employed during both the simulation and synthesis stages in combination with TSMC 65 nm CMOS standard cell technology. The results obtained post-synthesis are presented in Table 7. Specifically, the proposed SD architecture occupies an area of 1.10 mm\({}^{2}\) and achieves a throughput of 1.12 Gbps, while the power consumption is 410 mW.  
The comparison of the proposed SD with other LDPC decoders is provided in Table 7 considering implementation and performance. The proposed design has been shown to reduce the decoder architecture's complexity significantly. These designs exhibit varying implementation parameters and include code length and decoding algorithms such as combined min-sum (CMS) and normalised min-sum (NMS). Compared to the reported decoders, the proposed architecture exhibits area efficiency increases of 26% compared to [32], 62% compared to [33], 51.11% compared to [34], and 77.46% compared to [35], while delivering energy efficiency improvements of 52% compared to [33] and 21.6% compared to [35].
## 3 Modeling the VSG Control Strategy  
### SM Mathematic Model  
The structure and mathematic model of synchronous machines can be found in classic textbooks [16; 17]. When the magnetic field is also considered, suitable assumptions are made to simplify analyses. Smooth air gaps form between the stator and rotor, which are concentric cylinders. The magnetic-saturation effect and the magnetic reluctance of the iron core are neglected. The flux leakage and magnetic motive force (MMF) are assumed to be sinusoidal. Also, the electric machine has one pair of poles per phase on the stator, and one pair of poles per phase for the rotor. The self-inductance of the stator and rotor windings is constant. The mutual inductances between the stator windings of different phases are also constant, while the mutual inductances between the rotor and stator windings depend on the angular position of the rotor. The current of the excitation field winding is controlled and constant. The structure of an ideal two-pole SM consists of combining the circuits of a three-phase stator and a rotor that has spatial displacement in its rotation, as shown in Figure 3.  
Virtual terminal voltage \(e\), electromagnetic torque \(T_{e}\), and reactive power \(Q\) can be deduced as follows [17; 18]:  
\[e=M_{f}i_{f}\theta \tag{1}\]  
Figure 3: Structure of a two-pole electric machine.  
Figure 2: Technical gap of virtual synchronous generator (VSG)-based renewable integration.  
\[T_{e} = -\frac{\partial E}{\partial\theta}\bigg{|}_{i,i_{f}=cons\tan t}=M_{f}i_ {f}\langle i,\sin\theta\rangle \tag{2}\] \[Q=-\theta M_{f}i_{f}\langle i,cos\theta\rangle \tag{3}\]  
where the mechanical rotor angle is replaced by an electric angle, \(\theta=\theta_{m}=\theta_{e}\). \(\left[M_{af},M_{bf},M_{cf}\right]^{T}\) are the mutual inductances between the stators and field, which depend on the relative position with a peak value of \(M_{f}\). \(i=\left[i_{a},i_{b},i_{c}\right]^{T}\) is the stator current, \(i_{f}\) is the excitation current, and \(E\) is the co-energy stored in the magnetic field for energy conversion?  
The output characteristics of synchronous machines require two important equations that address the mechanical part and the electrical part. The mechanical part is determined by a swing equation [19; 20], which is:  
\[J\frac{d\omega}{dt}=T_{m}-T_{e}-D_{p}(\omega-\omega_{g}) \tag{4}\]  
where \(J\) is the rotating inertia; \(T_{m}\) and \(T_{e}\) are the mechanical and electromagnetic torque, respectively; \(D_{p}\) is the damping factor; and \(\omega\) and \(\omega_{g}\) are the angular velocity of the VSG and the grid, respectively.  
For the electrical part, the AC side circuit is shown in Figure 11, and the AC side voltage equation is:  
\[L\frac{di_{abc}}{dt}=e_{abc}-u_{abc}-Ri_{abc} \tag{5}\]  
As for VSG, \(L\) and \(R\) are the inductance and resistance of the inductance and capacitor (LC) filter, \(e_{abc}\) and \(u_{abc}\) are the voltage of the converter output and the PCC, respectively, and \(i_{abc}\) is the three-phase current.
## 3 Results
### Internal Camera Parameters  
The camera's internal parameters define the geometries of the camera's lens and sensor. These parameters allow users to correct distortion and to obtain more precise information about the geometry of the captured images. Table 5 shows the results of the camera's internal parameters' optimisation according to the camera model for the four scenarios. The relative difference between the initial and optimised internal camera parameters obtained for each scenario is as follows: 3.56% for Scenario A, 0.34% for Scenario B, 3.3% for Scenario C, and 0.16% for Scenario D. For the Perspective Lens, it is calculated as the percentage difference between the initial and optimised focal length. This is the first parameter that the Pix4 Dmapper software calculates during camera optimisation. If the difference between the estimated value and the initial value of the focal length exceeds 5%, it is considered an indicator of optimisation error, as it could result in an inaccurate geometric reconstruction. This may result in a curved or deformed model, negatively affecting the accuracy of the final project.  
Figure 5: Distribution of the Root Mean Square Error (RMSE) for the Ground Control Points (GCPs) and checkpoints (CPs) and Ground Sampling Distance (GSD) in the four studied scenarios.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Parameters**} & **Focal** & **Principal** & **Principal** & & & & & & \\  & & **Length** & **Point x** & **Point y** & **R1** & **R2** & **R3** & **T1** & **T2** & **Result 1** \\  & & **[pix]** & **[pix]** & **[pix]** & & & & & & & \\ \hline \multirow{4}{*}{A} & Initial Values & 4564.399 & 2698.159 & 1910.765 & \(-\)0.004 & \(-\)0.043 & 0.087 & \(-\)0.003 & 0.004 & \\  & Optimised Values & 4741.210 & 2639.595 & 1942.754 & \(-\)0.006 & 0.014 & \(-\)0.015 & \(-\)0.001 & 0.001 & 3.56\% \\ \cline{1-1}  & Uncertainties & \multirow{2}{*}{15.865} & \multirow{2}{*}{0.541} & \multirow{2}{*}{1.503} & \multirow{2}{*}{0.000} & \multirow{2}{*}{0.000} & \multirow{2}{*}{0.001} & \multirow{2}{*}{0.000} & \multirow{2}{*}{0.000} & \multirow{2}{*}{0.000} & \multirow{2}{*}{0.000} & \\  & & (Sigma) & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pix4 Dmapper quality report on the internal camera parameters for Scenarios A–D; R1, R2, R3: Radial distortion coefficients; and T1, T2: Lens tangential distortion coefficients (dimensionless).  
The uncertainties (\(\sigma\)) of the camera's internal parameters are calculated from the covariance matrix derived from the photogrammetry optimisation process. The software relies on the quantity and quality of the images and the available 3D points (or control points) to adjust the photogrammetric model.
# Digital Twin for Supply Chain Coordination in Modular Construction
## 1 Introduction
Then, the digital twin collects real-time IoT sensor data (e.g., GPS) from its physical asset (e.g., module), simulates and analyzes the data to predict potential logistics risks, and finds alternative delivery routes with accurate estimated times of arrival (ETA). Such proactive risk detection and accurate delivery estimation minimizes logistical uncertainties, thus facilitating effective supply chain coordination for more productive modular projects.
## 3 Results
### Correlation between Batu Ferringhi and Persiaran Bayan Indah  
Figure 10a,b show the data correlation of grain size and cumulative frequency of samples at Batu Ferringhi. In comparison, the data correlation of grain size and cumulative frequency of samples at Persiaran Bayan Indah (the Queensbay Mall area) are shown in Figure 10c,d. The five samples from Batu Ferringhi display an even grain size. The graphic mean varies from medium sand to gravel. The sorting sample is mostly moderately sorted and increases to well sorted. The samples are primarily near-symmetrical in their grain size distribution pattern. The graphic kurtosis is mostly leptokurtic. These data indicate that sediment along the natural coastal landform is homogenous and the grain sizes are distributed evenly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Mode** & \begin{tabular}{c} **Graphic Mean** \\ **(M)** \\ \end{tabular} & \begin{tabular}{c} **Graphic Standard** \\ **Deviation (SD)** \\ \end{tabular} & \
\begin{tabular}{c} **Graphic** \\ **Skewness (S)** \\ \end{tabular} &
\begin{tabular}{c} **Graphic Kurtosis** \\ **(K)** \\ \end{tabular} \\ \hline QB1 & \(2\,\Phi\) & Medium sand & Moderately well-sorted & Negatively skewed & Platykurtic \\ QB2 & \(1\,\Phi\) & Medium sand & Poorly sorted & Near symmetrical & Mesokurtic \\ QB3 & \(-0.5\,\Phi\) and \(1\,\Phi\) & Coarse sand & Moderately sorted & Near symmetrical & Mesokurtic \\ QB4 & \(-0.5\,\Phi\) & Coarse sand & Moderately well-sorted & Near symmetrical & Leptokurtic \\ QB5 & \(-0.5\,\Phi\) & Very coarse sand & Moderately sorted & Near symmetrical & Leptokurtic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary on analysis of frequency curves of the five sediment samples in Persiaran Bayan Indah (the Queensbay Mall area), representing the sediments of anthropogenic coastal landform or reclamation coastline.  
Figure 10: Comparing grain size and cumulative frequency of samples at Batu Ferringhi (**a**,**b**) and Persiaran Bayan Lepas (**c**,**d**).  
The five samples from the Persiaran Bayan Indah (the Queensbay Mall area) consist of varying grain sizes. The mean size varies largely from medium sand to very coarse sand. In contrast with samples from the Batu Ferringhi, which are relatively well sorted, in Persiaran Bayan Lepas, the sorting is mostly moderately to poorly sorted. It shows unclear trends for the sample content, which means the sediments dumped on the anthropogenic coastal landform are heterogeneous.
## 4 Numerical Analysis
### Analysis of Temperature Gradients and Temperature Stresses in Carbon Fiber-Heated Pavement  
The link between the temperature gradient in the depth direction (hereinafter referred to as the temperature gradient) and the temperature stress is the topic of this section.  
The temperature gradient and temperature stress patterns were extremely similar, as seen in Figure 12.  
To further examine the relationship between the temperature gradient and temperature stress, the correlation between the two was fitted, as shown in Figure 13. The fitted linear interpolation yielded an excellent correlation coefficient (R\({}^{2}\)) of 0.995. This confirmed that the temperature gradient in the carbon fiber-heated pavement was the predominant cause of temperature stress. Moreover, the temperature gradient exhibited a linear relationship with thermal stress. Taking the horizontal direction as an example, the linear correlation between temperature gradient and thermal stress at the same depth is expressed in Equation (7):  
\[\sigma_{T}=2.523E\alpha\Delta S\Delta T \tag{7}\]  
where \(\sigma_{T}\) is the temperature stress (MPa), \(\Delta T\) is the temperature gradient (\({}^{\circ}\)C/m), \(E\) is the Young's modulus (MPa), \(\alpha\) is the linear expansion coefficient (1/\({}^{\circ}\)C), and \(\Delta S\) is the distance between data points numbered A and numbered B in the same horizontal direction (fixed at 4 cm). The concrete pavement was assumed to be isotropic in this model, and the temperature stress equation was calculated using the Westcard-Bradbury method in conjunction with the above Equation (7) to obtain the temperature gradient and temperature stress relationship equation for carbon fiber-heated concrete pavement at various depths, as shown in Equation (8).  
\[\sigma_{T}=\frac{E\alpha(|I_{0}-h|)\Delta T}{2}\times 5.046 \tag{8}\]  
Figure 11: Trend of temperature stress of A\(-\)numbered pavement at different depths.  
Figure 12: Temperature gradient versus temperature stress with time.  
where \(h_{0}\) is the carbon fiber burial depth (m), \(h\) is the pavement depth (m), and 5.046 is the temperature stress factor.  
The temperature stresses at different depths of pavement can be estimated using the above equation during carbon fiber heating of concrete pavement. This simplified mathematical approach can be used as a reference for real-world tasks.
## 4 Simulation and Numerical Results
### Trade-Off Performance  
In the integrated MIMO-OFDM-IM system, to evaluate the influence of different activated subcarrier combinations on system performance, we constructed a trade-off curve between radar and communication through simulations employing different activated subcarrier combinations, and the trade-off parameters include radar target detection probability and communication BER. In the simulations, we carried out \(10^{4}\) Monte Carlo experiments, and in each experiment, the target was detected in the radar monitoring range. Figure 11 shows the variation in the radar-communication trade-off curve with the number of activated subcarriers under different SNRs. It can be found that the radar target detection probability increases monotonically with \(K\) and becomes stable when the number equals or is more than five. On the other hand, when \(K=5\), the communication BER performance is the best. Therefore, the appropriate activated subcarrier combinations can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Number of Complex Multiplications**} \\ \cline{2-4}  & \(N_{t}=4\) & \(N_{t}=8\) & \(N_{t}=12\) \\ \hline ML detection & \(3.26\times 10^{6}\) & – & – \\ MMSE detection & \(6.62\times 10^{2}\) & \(5.28\times 10^{3}\) & \(9.35\times 10^{3}\) \\ Improved MMSE detection & \(2.07\times 10^{3}\) & \(9.23\times 10^{3}\) & \(2.84\times 10^{4}\) \\ Subcarrier-wise detection & \(2.23\times 10^{3}\) & \(8.51\times 10^{3}\) & \(1.5\times 10^{4}\) \\ Proposed improved detection & \(2.27\times 10^{3}\) & \(6.04\times 10^{3}\) & \(9.61\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm complexity comparison of different detection methods.  
Figure 10: The BER performance of different information detection methods under the proposed integrated MIMO-OFDM-IM system (\(N_{r}=N_{t}=8\)). (**a**) \(N_{c,sub}=8\), \(K=3\); (**b**) \(N_{c,sub}=8\), \(K=6\).  
be selected according to different requirements of the actual environment, while a general rule-of-thumb approach would be using five activated subcarriers since it gives the best communication performance and relatively high target detection probability.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks
### Unmanned Arial Vehicle (UAV)
#### 4.4.2 Types of UAV  
There are several varieties of UAVs. To maximize the efficiency of UAV use, it is necessary to utilize an application-particular type. UAVs are categorized based on their altitude and the type of wings [155].  
Figure 14 depicts the classification of UAVs. UAVs are classified as low-altitude or high-altitude UAVs as follows:  
1. Low-altitude platforms (LAPs) are easier to install and deploy than high-altitude platforms, but their coverage area is smaller, and their endurance time is shorter than high-altitude platforms.
2. High altitude platforms (HAPs) can support the task for many months, but they are more expensive to deploy than low altitude platforms (LAPs). Depending on the wing type, UAVs can be classified as fixed-wing or rotary-wing as follows:  
1. A fixed-wing creates lift utilizing forward-moving wings. It requires a runway for takeoff and landing, and it must be able to maintain a certain forward speed. Its features are simple construction, high speed, and large cargo.
2. A rotary wing uses blades that revolve around a rotor shaft to generate lift. It is capable of hovering and moving in every direction. Its mechanism depends on vertical takeoff and landing. Its features are a lower payload, a shorter range, and a slower speed [156].  
Figure 14: Classification of UAV.