esa-sceva/satcom-chunk-collection huggingface dataset:
# Computational Fluid Dynamics-Aided Simulation of Twisted Wind Flows in Boundary Layer Wind Tunnel
## 4 Development Stage
### Incorporation of ABL Characteristics  
The LES technique was adopted herein to incorporate the ABL characteristics in the numerical wind tunnel. The validation work for grid independence and the LES turbulence model followed the process described in Section 4.1. Subsequently, conventional passive devices, including spires, fences, and ground roughness elements, were modeled in the numerical wind tunnel to generate the ABL characteristics of the flow, while guide vanes were implemented based on the optimal configuration obtained in Section 4.2, i.e., \(d_{pane,wall}=750\) mm, \(d_{pane,wane}=700\) mm, and \(d_{pane,model}=4\) m. Based on the longitudinal wind speed profile in Figure 13a that corresponds to such a configuration of vanes, the conventional passive devices were carefully set up in the numerical wind tunnel so that the speed profile could be further modified into a power-law form, as expressed in Equation (4). Figure 14 depicts the mesh details in the computational domain as well as the setup of all passive devices. The simulation duration is 42.8 s in total with a time step of 0.001 s, and the results obtained within the first 6.5 s were excluded to avoid the unsteady flow generated at the start of the process.  
Figure 14: Mesh details for optimal setup in numerical wind tunnel: (**a**) overview; (**b**) top view of test section; (**c**) connection of grid nodes; (**d**) close-up view of spires and fence; (**e**) close-up view of guide vanes; (**f**) close-up view of ground roughness elements.  
To evaluate the performance of the numerical wind tunnel setup, Figure 15a,b illustrate the time variations in the instantaneous longitudinal and lateral wind speeds, respectively, over time at the central plane of the wind tunnel. Figure 15a shows that, due to the employment of passive devices for generating ABL winds, the longitudinal wind speed increased with height as desired. On the other hand, the lateral wind speed increased significantly in the downstream region of the guide vanes, as shown in Figure 15b. All flow fields captured at time instances T = 12, 16, 20, and 24 s presented high similarity, suggesting that the TWF was stable during such a time period.  
Figure 16 plots the profiles of TWF at the center of the model test region. As shown in Figure 16a, the longitudinal wind speed and turbulence intensity follow the power law expressed by Equation (4) and Equation (5), respectively. This indicates that the objective of incorporating ABL characteristics is generally satisfied. On the other hand,  
Figure 15: Time variations in the instantaneous wind speeds: (**a**) longitudinal speed; (**b**) lateral speed.  
Figure 16b shows that the twist angle profile is generally in good agreement with the target profile (i.e., the [PERSON] spiral model) at heights above 0.4 m. However, the twist angles below 0.4 m were considerably lower than the desired values, which is believed to be attributed to the attenuation over distance and will be further improved at the subsequent closeout stage.
## 4 Results
### Results of Real SAR Images
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**CLASS** & **SVM** & **CKS-FCM** & **CKFF** & **SVM-CK** & **MWSG** & **MAKC** \\ \hline \(\downarrow\) runway & 56.00 & 88.58 & 82.83 & 85.08 & 92.50 & 88.54 \\ bare land & 60.83 & 61.85 & 91.40 & 87.21 & 91.85 & 96.33 \\ infrastructures & 97.05 & 25.01 & 61.52 & 86.62 & 68.11 & 92.39 \\ AA & 71.29 & 58.48 & 78.58 & 86.30 & 84.15 & 92.42 \\ OA & 60.85 & 65.16 & 89.47 & 86.87 & 91.45 & 95.06 \\ Kappa & 0.25 & 0.35 & 0.69 & 0.64 & 0.74 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification precision results on SAR1. comparison of different methods is represented in Figure 7c-h. As can be seen, the proposed MAKC method in Figure 7h provides the best regional consistency compared with other test methods. The best consistency of vegetation areas in the red box was the proposed method, while the boundary distinction between bare land and crops was better maintained by the proposed method than the other methods.
## 4 Conclusions  
The susceptibility of dyes (Indigo Carmine, Tartrazine, and Allura Red AC) to degradation in advanced oxidation processes depended on their chemical structure, chromophore groups, and interactions with oxidizing reagents. Indigo Carmine exhibited moderate susceptibility to photodegradation under UV light, as its aromatic rings are stable but can be broken down in the presence of hydroxyl radicals (-OH). After introducing a catalyst, a 95% removal efficiency of this dye was achieved. Tartrazine showed low susceptibility to photodegradation, even in the presence of a catalyst, as its azo groups are stable against UV radiation. In contrast, Allura Red AC exhibited moderate susceptibility to photodegradation in the presence of a catalyst. Its aromatic and azo groups can partially absorb UV radiation, but degradation requires the presence of oxidizing reagents. A higher number of aromatic rings may lead to the formation of more complex intermediate products, which require further oxidation. Indigo Carmine was less stable than Tartrazine and Allura Red AC, which meant it was more susceptible to breakdown in a shorter time.  
Differences in susceptibility to ozonation primarily result from the dyes' different chemical structures. Indigo Carmine is easily degraded due to the presence of oxidizable bonds and the lack of stabilizing functional groups, while Tartrazine and Allura Red AC are more resistant to ozone, requiring more aggressive conditions or additional methods to aid their degradation. The introduction of an additional amount of oxidant in the form of hydrogen peroxide and waste catalyst to the ozonation process increased the efficiency of removing all dyes and shortened the degradation process time. In a neutral reaction environment and at pH = 3, these processes were the most effective of all those analyzed in removing dyes. On the other hand, the ultrasound process was ineffective in removing the analyzed dyes, even after introducing an oxidant into the system.  
Conceptualization, M.K. and S.Z.; methodology, M.K. and S.Z.; software, M.K.; formal analysis, M.K.; investigation, M.K. and S.Z.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, M.K.; visualization, M.K.; supervision, M.K. and S.Z. All authors have read and agreed to the published version of the manuscript.  
The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Financed by the Minister of Science and Higher Education Republic of Poland within the program \"Regional Excellence Initiative\".  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Financed by the Minister of Science and Higher Education Republic of Poland within the program \"Regional Excellence Initiative\".
## 3 Results and Discussion
### Classification Discussion Based on Overall Results
#### 3.3.1 Different Brands of IFC  
After comparing the data for case 2 and case 3 in Figure 8, it was obvious that brand B IFC (W) was better than brand A IFC (W). Figure 11 shows the specimens of case 2 and case 3 after the fire test. It could be seen from the observation that the fire-retardant coatings of case 3 still adhered to the tray after expansion, while the coatings of case 2 had poor adhesiveness, and a large amount of the coatings fell off at the bottom and side of the tray. The adhesion effect shown here was consistent with the adhesive strength of this two coatings in Table 2.  
The coatings of case 2 and case 3 after the fire test were taken for thickness measurements, and the data was recorded in Table 5. It could be seen that the expansion ratio of case 3 is larger than that of case 2. Hence, we could conclude that the adhesive property and the expansion effect of the brand B IFC (W) were better than those of brand A.  
Figure 11: Appearance of specimens after fire test: (**a**) specimen of case 2; (**b**) specimen of case 3.  
Figure 10: Insulation resistance change curves for 12 cases.  
Therefore, before selecting IFC for large-scale projects, we can heat the coated plate and compare the expansion progress, cohesiveness and char structure of coatings to select a better one.
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Fundamentals System Models  
A simple system model has transmitting antennas (\(M_{T}\)) and receiving antennas (\(M_{R}\)). The relation between the transmitted and received signal is spatial multiplexing. The memoryless MIMO flat fading channel (narrowband model) is given by  
\[Y=Hx+n \tag{1}\]  
According to the framework of Equation (1), the matrix \(\mathrm{H}\), which represents a signal response channel matrix with dimensions \((M_{R}\ \times M_{T})\), is utilized to clarify the application of both massive MIMO and ultra-massive MIMO systems. Furthermore, the variable \(n\), defined as an additive white complex Gaussian noise vector with dimensions \((M_{R}\ \times\ 1)\), is recognized as a primary noise model in information theory. This model is specifically crafted to replicate the effects of the diverse random processes commonly observed in natural settings. The matrix representation thus effectively captures the relationship between transmitted and received signals, illustrating the intricate dynamics of signal propagation in these advanced communication systems as  
\[\begin{bmatrix}Y_{1}\\ Y_{2}\\ \vdots\\ Y_{M_{R}}\end{bmatrix}=\begin{bmatrix}h_{11}&h_{12}&\cdots&h_{1,M_{T}}\\ h_{12}&h_{22}&\cdots&h_{2,M_{T}}\\ \vdots&\vdots&\ddots&\vdots\\ h_{M_{R},1}&h_{M_{R},2}&\cdots&h_{M_{R},M_{T}}\end{bmatrix}\begin{bmatrix}X_{ 1}\\ X_{2}\\ \vdots\\ X_{M_{T}}\end{bmatrix}+\begin{bmatrix}n_{1}\\ n_{2}\\ \vdots\\ n_{M_{R}}\end{bmatrix} \tag{2}\]
###### Abstract  
A well-functioning smart grid is an essential part of an efficient and uninterrupted power supply for the key enablers of smart cities. To effectively manage the operations of a smart grid, there is an essential requirement for a seamless wireless communication system that provides high data rates, reliability, flexibility, massive connectivity, low latency, security, and adaptability to changing needs. A contemporary review of the utilization of emerging 6G wireless communication for the major applications of smart grids, especially in terms of massive connectivity and monitoring, secured communication for operation and resource management, and time-critical operations, are presented in this paper. This article starts with the key enablers of the smart city, along with the necessity of the smart grid for the key enablers of it. The fundamentals of the smart city, smart grid, and 6G wireless communication are also introduced in this paper. Moreover, the motivations to integrate 6G wireless communication with the smart grid system are expressed in this article as well. The relevant literature overview, along with the novelty of this paper, is depicted to bridge the gap of the current research works. We describe the novel technologies of 6G wireless communication to effectively perform the considered smart grid applications. Novel technologies of 6G wireless communication have significantly improved the key performance indicators compared to the prior generation of the wireless communication system. A significant part of this article is the contemporary survey of the considered major applications of a smart grid that is served by 6G. In addition, the anticipated challenges and interesting future research pathways are also discussed explicitly in this article. This article serves as a valuable resource for understanding the potential of 6G wireless communication in advancing smart grid applications and addressing emerging challenges.  
6G; applications of smart grid; key performance indicators; smart city; smart grid; wireless communication +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Human Activity Representation
#### 2.2.1 Motion Profile
The displacement vector of each skeletal joint \(X\in s_{dv}\) is computed with respect to the first frame in the sliding window as follows: \[\mathbf{D}\mathbf{V}_{\mathbf{X}(k)}=[(\mathbf{X}(k)-\mathbf{X}(1)],\] (4) where \(\mathbf{D}\mathbf{V}_{\mathbf{X}(k)}\) is the displacement vector of the joint \(X\) in the \(k\)-th frame within the current sliding window. \(\mathbf{X}(1)\) and \(\mathbf{X}(k)\) are the 3D coordinates of the joint \(X\) in the first and \(k\)-th frames, respectively, within the current sliding window, where \(k\in\{2,3,\cdots,W\}\). Then, we identify the direction of motion of each joint in the set \(s_{dv}\) with respect to the three anatomical planes by calculating the signed distance between the displacement vector of that joint and each one of the three anatomical planes. The signed distance (\(SgnDist\)) of the displacement vector \(\mathbf{D}\mathbf{V}_{\mathbf{X}(k)}\) with respect to the anatomical plane \(\mathbf{Y}\), where \(\mathbf{Y}\in\{TP,CP,SP\}\), can be calculated as follows: \[SgnDist(\mathbf{D}\mathbf{V}_{\mathbf{X}(k)},\mathbf{Y})=\left(\frac{( \mathbf{Y}(2)-\mathbf{Y}(1))\times(\mathbf{Y}(3)-\mathbf{Y}(1))}{\parallel( \mathbf{Y}(2)-\mathbf{Y}(1))\times(\mathbf{Y}(3)-\mathbf{Y}(1))\parallel} \right)\cdot\mathbf{D}\mathbf{V}_{\mathbf{X}(k)},\] (5) where \(\mathbf{Y}(i)\) is the \(i\)-th joint that was used to construct the plane \(\mathbf{Y}\) and \(i\in\{1,2,3\}\). The operators \(\cdot\) and \(\times\) indicate the vector dot-product and cross-product operations, respectively. Depending on the sign (positive or negative) of the \(SgnDist\) computed for a specific displacement vector with respect to each one of the three anatomical planes, we can determine if the displacement vector is located above or below the TP, in front or behind the CP and to the left or right of the SP. 1.4 The motion profile of each video frame in the current window position is defined as a vector, which consists of the calculated displacement vectors along with their associated signed distances for the skeletal joints in the set \(s_{dv}\). 2. Then, we move the sliding window to the next position and repeat the procedure in the first step. In the next subsection, we describe the construction procedure of the pose profile for each frame in the depth-map video sequence.
# Ultra-Low-Loss Silicon Waveguides for Heterogeneously Integrated Silicon/III-V Photonics
## 2 Ultra-Low Loss Waveguides for Si/III-V Heterogeneous Integration
### Waveguide Geometry  
The ULL waveguide cross-sectional geometry is shown in Figure 2a. To lower the optical mode interaction with the rib sidewall, the waveguide rib height is targeted to be about 10% of the total Si layer thickness. An etch depth of 56 nm was chosen because it can be accurately controlled using a laser-based interferometric etch depth monitor (Intellemetrics LEP500) system, which is commercially available. Thermal oxide serves as the bottom buried oxide (BOX) while the top cladding is deposited oxide, either by plasma-enhanced chemical vapor deposition (PECVD) or sputtering. Each cladding layer is 1 \(\upmu\)m thick to minimize substrate leakage or loss due to the metal heaters, which are commonly put on the top of the waveguide resonators. The effective index of the transverse electric (TE) modes versus the waveguide width is plotted in Figure 2b, suggesting that the waveguide guides a single TE mode when the width is 1.8 \(\upmu\)m or lower.  
Figure 1: (**a**) Optical mode profiles of the heterogeneous (Si/III-V) waveguide with 500 nm thick silicon in 3 different operating regimes: (**a.1**) \(n_{m-y}>n_{si}\) with a slab III-V on 0.7 \(\upmu\)m wide silicon waveguide, which results in higher confinement in the active III-V layer. (**a.2**) \(n_{m-y}\approx n_{si}\) with a slab III-V on 1.3 \(\upmu\)m wide silicon waveguide, which results in equal confinement in the III-V and silicon. (**a.3**) \(n_{m-y}<n_{si}\) with 0.5 \(\upmu\)m wide III-V on 1.3 \(\upmu\)m wide silicon waveguide, which results in higher confinement in silicon. (**b**) Effective indices of strip silicon waveguides of three thicknesses (220 nm, 500 nm and 3 \(\upmu\)m) with varying width is plotted together with the effective index of a typical 2 \(\upmu\)m thick III-V waveguide (used in [29]). Noted that all simulations were carried out at 1550 nm wavelength.  
Figure 2: (**a**) Cross-sectional geometry of the ultra-low loss (ULL) Si waveguides in this work. The thickness of the buried oxide (BOX) and the Si device layer are 1 \(\upmu\)m and 500 nm, respectively. The 56 nm tall Si rib is formed by dry-etching, leaving a 444 nm thick Si slab. (**b**) Effective index versus waveguide width in the 56 nm rib Si waveguide. The waveguide is quasi-single mode within the yellow colored region. Inset: Electric field profile of the _fundamental_ mode in 1.8 \(\upmu\)m wide waveguide.
# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 5 Conclusions  
We have tested three potential SAR-coherence-based landslide classification methods at a range of resolutions against a manually mapped landslide inventory for events triggered by the 2015 Gorkha, Nepal earthquake [2]. We have tested two classifiers that have previously been suggested to contain landslide information: absolute coherence and the ARIA method for urban damage proxy mapping. We have also presented a new method for landslide classification, in which the difference between boxcar and sibling-based coherence (Bx-S) is used as a classifier. Using ROC analysis, we showed that none of the methods tested here were able to identify landslides at a pixel-by-pixel (20 m \(\times\) 22 m) scale. However, all three classifiers were more successful when the resolution was coarsened by aggregating pixels, which corresponds to the real-world application of classifying larger regions of high landslide density. Our Bx-S method is more successful than existing SAR coherence methods, with an ROC of 0.77 for 300 m \(\times\) 330 m aggregate pixels. This suggests that our new method may be able to provide useful and timely information on the large-scale distribution of landslides following future triggering events, such as earthquakes, even under heavy cloud conditions that limit the applicability of optical satellites for this purpose.  
[PERSON] carried out curation, validation and formal analysis of the data and preparation of the original draft. [PERSON] and K.B. worked on software used in the study. Conceptualization, administration, funding acquisition and supervision of the project were performed by [PERSON], D.M. and A.L.D. All authors were involved in developing the methodology and in reviewing and editing the manuscript.  
This work was carried out as part of a PhD project funded by the Institute of Hazard, Risk and Resilience at Durham University through the Action on Natural Disasters PhD scholarship programme. The work was partly supported through the NERC Centre for the Observation and Modelling of Earthquakes, volcanoes and Tectonics (COMET).  
Sentinel-1 interferograms and coherence maps are a derived work of Copernicus data, subject to ESA use and distribution conditions. Some figures were made with the public domain Generic Mapping Tools [52]. We thank [PERSON] and [PERSON] for their helpful comments and feedback. We thank four anonymous reviewers for their helpful and constructive reviews.  
The authors declare no conflict of interest.
## 3 Methodology
### Retrackers
#### 3.2.2 Offset Center of Gravity (OCOG) Retracker  
The OCOG retracker ([PERSON] et al., 2016; [PERSON] et al., 2016) was designed to calculate the center of gravity of the reflected waveform based on the power levels in the bins. It is an empirical retracker that implements a combination of the center of gravity (COG) and a conventional offset to estimate the related epoch (the leading edge position). Three main parameters are estimated for the OCOG retracker: the COG, the window size (W) and the amplitude (A).  
[PERSON]'s definition ([PERSON] et al., 2016) was considered in our analysis using squares of the power samples:  
\[\begin{array}{l}COG=\frac{\sum_{n=n_{th}}^{m}y^{2}(n)}{\sum_{n=n_{th}}^{m}y ^{2}(n)}\\ W=\frac{\left(\sum_{n=n_{th}}^{m}y^{2}(n)\right)^{2}}{\sum_{n=n_{th}}^{m}y^{4} (n)}\\ A=\sqrt{\frac{\sum_{n=n_{th}}^{m}y^{4}(n)}{\sum_{n=n_{th}}^{m}y^{2}(n)}}\end{array} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
where \(y(n)\) is the \(n_{\text{th}}\) power sample of the input waveform, and \(n_{1}\) and \(n_{2}\) are the initial and end range bins or positions, respectively, in the waveform used for the OCOG parameter estimation.  
The estimated epoch can then be implemented using the conventional OCOG definition of the epoch:  
\[epoch=COG-W/2 \tag{3}\]
## 3 Results and Discussion
### Ecotoxicological Effect of NOM on ZnO
Previous studies reported that Zn ions are toxic, but our dissolution test results in Section 3.2.5 revealed that ZnO dissolution is negligible regardless of the presence of NOM, suggesting that ion species distribution is not the reason. Hence, we turned our attention to the stability (i.e., dispersibility) of ZnO without versus with NOM. The presence of NOM increased the dispersibility of ZnO and reduced the toxicity level for all cases, which indicates the strong relationship between ZnO dispersibility and toxicity level. At present, the exact toxicological mechanisms are not clear, and it requires further study. To identify the causes of reduced Zn toxicity with NOM, we studied relevant literature on ionization tendencies. We observed that, besides ionization effects, several critical parameters influenced Zn toxicity, including the reduction in oxidative stress caused by reactions between NOM and free radicals Reactive Oxygen Species (ROS) and the reduction in toxicity through passivation of particle surfaces [14; 15; 50; 51; 52]. Based on these findings, further investigation is required to clarify the mechanism for reduced toxicity in the presence of NOM.