esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Useful information, such as water levels, currents, salinity and temperature dynamics in water bodies, are obtained through numerical models in order to pursue scientific research or consultancy. Model validation dates back long ago, since such models started to be developed in the 1960s. Despite their usefulness and reliability in complex situations, some issues related to well-known benchmarks are still present. This work aims to analyse in detail the behaviour of the velocity profile, vertical eddy viscosity and tangential stresses at the bed in two cases of free surface flows; namely: uniform flow in an inclined rectangular channel and a wind-induced circulation in a closed basin. Computational results strongly depend on the turbulence closure model employed and a reasoned comparison is necessary to highlight possible improvements of these models. The strong differences that arise are deeply analysed in this work.  
turbulence; hydrodynamic modelling; numerical modelling; ocean modelling; \(k-\epsilon\) model; \(k-\omega\) model; Mellor-Yamada model; Delft3D; ROMS +
Footnote †: journal: Journal of Marine Science and Engineering
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable Only to External/Surface Defects
#### 3.1.1 Visual Testing (VT)  
This technique is the most common form among all NDT methods due to its simplicity. Although most of a visual inspection is performed with the naked eye, some cases may require visual aids such as magnifiers, binoculars, scopes, and borescopes. The inspector in this case needs to be experienced and familiar with the types and levels of damage in the steel. This is the most economical and fastest method of inspection [11]. However, because of the subjectivity of the visual inspection method, different results could be reported by different inspectors. Visual inspection limits the inspector to the surface defects that are sufficiently noticeable [12]. Different conditions can affect the inspector's judgment and focus, including traffic, working at a height, limited visual angle, lighting conditions, and multitasking with other tasks for bridge element rating [10]. These factors can be problematic for the inspectors to decide whether a flaw should be considered a defect and if it requires follow-up action. An example of challenging visual inspection is shown in Figure 3, where the lack of lighting and height can be of concern.
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 4 Results and Discussion  
### Experimental Setup  
In a coal mine in northwestern China, the full deployment of 5G networking equipment and the underground coverage of 5G signals have been achieved, which provide conditions for the verification of interference-suppression technology. The deployment of the experimental tunnel base station is shown in Figure 5.  
The 5G frequency band is 3500 MHz and the bandwidth is 100 MHz. The antenna of the base station is a directional antenna with a gain of 8 dBi, a lobe width of 65 degrees in the horizontal plane, and a lobe width of 60 degrees in the vertical plane. The underground deployment sites mainly include fully mechanized mining faces, substations, water pump rooms, refuge chambers, etc. Under different interference model conditions, the signal set in the output space of the multi-channel radio frequency antenna is collected.
###### Abstract  
In order to study the dynamic response characteristics of a pile-soil foundation when an adjacent tunnel exists, both model tests and numerical simulations were performed in this study. Taking the peak vibration acceleration, peak vibration velocity, and peak vibration displacement as the evaluation indexes of the dynamic response, the dynamic response of the pile-soil foundation with an adjacent tunnel under high-speed train loads is studied. A method of dividing the affected zones of the dynamic response based on the safety threshold is provided in this paper, which can evaluate the safety of the adjacent tunnel under the action of train load. Additionally, the effect of train speed on dynamic response is further analyzed. The results show that the dynamic response index inside the foundation decays significantly with increasing distance from the surface when the train load is applied, regardless of the presence or absence of the adjacent tunnel. Compared with an ordinary pile-soil foundation, the dynamic response indexes inside the foundation increase significantly when there is an adjacent tunnel. With the increase in the horizontal distance from the pile foundation, the distribution characteristics of the horizontal transverse direction of each dynamic response index inside the foundation change from a \"wavy\" distribution to a monotonically decreasing distribution. According to the safety threshold of vibration velocity, the foundation is divided into the hazardous affected zone, strongly affected zone, and weakly affected zone, and then the corresponding vibration reduction measures are adopted. With the increase in train speed, the effect of tunnel structure on the attenuation of dynamic response in a pile-soil foundation becomes more obvious.  
high-speed railway; adjacent tunnel; pile-soil foundation; dynamic response; model test; safety threshold +
Footnote †: journal: Applied Sciences
## 4 Improved Fuzzy Analytic Hierarchy Process (IFAHP)
### Establishment of Hierarchical Model  
As shown in Figure 1, the hierarchical model of performance evaluation indexes for earthquake search and rescue robots takes the comprehensive performance of earthquake rescue robots as the target layer, while the criteria layer comprises four key aspects: survivability, mobility, detection, and perception ability, and communication and control ability. Each criterion contains multiple specific indexes.
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.6 Accuracy of Distance and Angle Measurements  
The accuracy of range measurement in FMCW LiDAR reflects the deviation between the measured distance averages and the true values, primarily influenced by the linearity of the light source's frequency modulation and the system's calibration. Currently, the modulation methods for the laser carrier frequency in FMCW LiDAR can be categorized into two types: internal and external modulation. Internal modulation offers advantages such as a simpler system architecture and relatively easier control. However, its challenge lies in the need for precise control of the cavity's physical parameters, which can be affected by temperature fluctuations and mechanical vibrations in practical applications. On the other hand, external modulation enables more complex modulation schemes and higher modulation frequencies, enhancing the system's flexibility and measurement accuracy. Nonetheless, this method may introduce additional optical losses and complexity, necessitating careful design of the optical path and modulation components to minimize these effects.  
Another critical function of FMCW LiDAR is the accuracy of angular measurement. The radar system must not only measure distance but also determine the orientation of the target object. The angular accuracy of the emitting components is crucial for the overall performance of the system. The precision of angular measurements is directly affected by the control accuracy of the laser emission angle. This requires the FMCW LiDAR system to possess high-precision mechanical positioning and a stable operating platform. Consequently, a high-precision three-dimensional rotating platform is integrated within the FMCW LiDAR system, which significantly enhances the accuracy of angular measurements through precise angle control and a stable operating platform, ensuring high reliability and consistency of the system in complex environments.  
To maintain the measurement accuracy of FMCW LiDAR, regular system calibration and maintenance are essential. This includes calibrating the linearity of the modulation frequency, inspecting and adjusting the optical alignment, and periodically testing and replacing components that may degrade due to environmental influences. Effective system maintenance ensures that the LiDAR system provides reliable and precise measurement data under various environmental conditions.
## 2 Background
### Forwarding Information
The validity period of the forwarding information is called the lifetime. Any mesh STA can initiate the path discovery process to any mesh STA in the WMN. Once the path discovery process is complete, the best bi-directional path will be selected and the two mesh STAs can start to communicate with each other using the path. All mesh STAs can participate in the routing process. As discussed earlier, the WMN improves the coverage of a WLAN, while multi-hopping improves the throughput of a transmission, depending on the range of each mesh STA along the path.
## 4 Numerical Analysis
### Discussion on the Side Abutment Pressure
The damage distribution characteristics for cross sections are presented in Figure 10, which depicts similar damage patterns; i.e., after the explosion, the crushed zone was formed within a certain range around the blasthole, dense secondary cracks and radial primary cracks were distributed in the more distant rock, and the damage was arrayed in a circular shape with the blasthole at the center. The quantitative relationship between NIS distributed along the blasthole axis and the extent of damage around the blasthole was investigated next. The average lengths of the primary cracks for \(S_{0}\), \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) were 132 cm, 105 cm, 91 cm, 67 cm, and 63 cm, which decreased by 20.5%, 13.3%, 26.4%, and 6.0% compared with the adjacent cross section in front, respectively. These results indicate that the extent of damage dropped sharply with an increase in confining pressure when it was less than 20 MPa and slowly decreased when it was greater than 20 MPa. Thus, with the increase in NIS distributed along the blasthole axis, the attenuation of the damage extent along the axis changed nonlinearly. The hoop stresses at A for the four cross sections taken in Case 2 were extracted, as presented in Figure 11, to explore the evolution of hoop stress in rock resulting from the combination of NIS distributed along the blasthole axis and blast loading. The hoop stress evolution trends for the different cross sections were similar; i.e., before blasting, the hoop stresses for \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) exhibited stable curves with magnitudes of 10, 15, 20, and 25 MPa, respectively, which are equivalent to the hoop stress values induced by side abutment pressure in the gob. This indicated that the numerical model successfully applied  
Figure 9: The damage extent for the cross sections along the blasthole axis under two working conditions. the confining pressure. After blasting, the hoop stresses first declined to their minimum values, then rapidly increased to their maximum values before decreasing again and finally remaining stable. The peak values of hoop compressive stress at A for \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) exhibited a gradual upward trend with increasing NIS distributed along the blasthole axis, which were 29.5, 32.8, 36.4, and 40.3 MPa, respectively. This finding is supported by the theoretical calculation presented in Section 2.2 for a lateral pressure coefficient of 1.
## 3 Results and Discussion
### IR Analysis  
The IR spectra of the five prepared functionalized PDVB samples (1, 5, 7-2, 12, and 14 with St, GMA, HPMA, HEMA/VBC, and HEMA/VBC as functional comonomers, respectively) are shown in Figure 7. We can observe that the bands in 1450 and in 2930 cm\({}^{-1}\) exist in all these samples which is the inplane-bending or twist-bending mode, and the stretching vibration of \(\mathbf{\bar{\Psi}}\)(C-H) of methylene(-CH\({}_{2}\)-), respectively. The peak around 1725 cm\({}^{-1}\) is the sketching mode of C=O in the 4 samples (5, 7-2, 12, and 14) with GMA, HPMA, and HEMA units in their own network. Generally, the bands in 1450 and in 2930 cm\({}^{-1}\) can be used to calibrate relative content of the C=O functional group in these functionalized PDVB samples by peak area or peak height contrast with the peak around 1725 cm\({}^{-1}\)[38; 39]. Furthermore, the peak in 3030 cm\({}^{-1}\) due to the stretching vibration mode of \(\mathbf{\bar{\Psi}}\)(C-H) inbenzene was observed obviously in the P(St-co-DVB) Sample 1. In the GMA-functionalized PDVB (Sample 5), except from the peak around 1725 cm\({}^{-1}\), the bands around 910 and 845 cm\({}^{-1}\) were also observed, which are the characteristic peaks of the asymmetric vibration of the epoxy group of GMA units, and the lack of absorbance in the range from 3600 to 3200 cm\({}^{-1}\) proved that GMA units are incorporated into the P(GMA-co-DVB) network by C=C double bond, not by ring-opening polymerization of the epoxy group in GMA. In the P(HEMA-co-DVB-co-VBC) systems (Samples 12 and 14), the peak around 1265 cm\({}^{-1}\) could be attributed to the characteristic absorption peak of methyl  
Figure 6: NLDFT simulation of the pore structure of POPs (Samples 8-1/8-2/9) from template methods. (**a**) Pore size distribution curves of dS(d) vs. d and (**b**) dV(d) vs. d.  
chloride linked to benzene ring, and this peak was stronger in Sample 12 than in Sample 14 due to the higher adding ratio of the VBC comonomer.
## 4 Optimal Resource Allocation
### Optimizing the Dual Function  
We use \(s_{n,k,t}=\tau_{n,k,t}\cdot p_{n,k,t}\) to denote the actual consumption on the subcarrier corresponding to \(\tau_{n,k,t}\). Define \(\mathcal{D}\) as the set of all possible variables \(\{\mathbf{s},\mathbf{\tau},\mathbf{r}\}\) satisfying the corresponding constraints of **P2**, \(\tau_{n,k,t}\geq 0\), \(s_{n,k,t}\geq 0\), and \(r_{k}\geq 0\), where \(\mathbf{r}=\{r_{n,k,t}\}\). Then the Lagrange dual function of **P2** is expressed as  
\[g\left(\mathbf{\alpha},\mathbf{\beta}\right)\triangleq\max_{\{\mathbf{s},\mathbf{r},\mathbf{\tau} \}\in\mathcal{D}}\ L\left(\mathbf{s},\mathbf{r},\mathbf{\tau},\mathbf{\alpha},\mathbf{\beta}\right) \tag{14}\]  
where  
\[\begin{split} L\left(\mathbf{s},\mathbf{r},\mathbf{\tau},\mathbf{\alpha},\mathbf{ \beta}\right)&=\sum_{k=1}^{K}r_{k}+\sum_{t=1}^{2}\sum_{k=1}^{K} \alpha_{k,t}\left[\sum_{n=1}^{N}\tau_{n,k,t}\ln\left(1+\frac{h_{n,k,1}s_{n,k,t }}{\sigma^{2}\tau_{n,k,t}}\right)-r_{k}\right]\\ &+\sum_{k=1}^{K}\beta_{k}\left(\sum_{n=1}^{N}\left(1-\tau_{n,k,1 }\right)\xi\left(h_{n,k,1}\frac{s_{n,k,1}}{\tau_{n,k,1}}+\sigma^{2}\right)- \sum_{n=1}^{N}s_{n,k,2}\right)\end{split} \tag{15}\]  
where \(\mathbf{\alpha}=\left\{\alpha_{k,1},\alpha_{k,2}\right\}\geq 0\) and \(\mathbf{\beta}=\left\{\beta_{k}\right\}\geq 0\) are the non-negative Lagrange multipliers for the constraints (10), (11). The dual optimization problem is hence given by  
\[\begin{split}&\min_{\{\mathbf{\alpha},\mathbf{\beta}\}}g\left(\mathbf{\alpha },\mathbf{\beta}\right)\\ &\text{s.t.}\ \mathbf{\alpha}\geq 0,\mathbf{\beta}\geq 0.\end{split} \tag{16}\]  
It can be proved by the definition of [36] this dual function is convex, thus, we can apply the subgradient method to obtain the optimal dual variables \(\{\mathbf{\alpha}^{*},\mathbf{\beta}^{*}\}\) to minimize \(g\left(\mathbf{\alpha},\mathbf{\beta}\right)\) with guaranteed convergence. Then a subgradient of \(g\left(\mathbf{\alpha},\mathbf{\beta}\right)\) can be derived as below  
\[\
abla\alpha_{k,t}=\frac{1}{2}\sum_{n=1}^{N}\tau_{n,k,t}\ln\left( 1+\frac{h_{n,k,t}s_{n,k,t}}{\sigma^{2}\tau_{n,k,t}}\right)-r_{k},\forall k,t \tag{17}\] \[\
abla\beta_{k}=\sum_{n=1}^{N}\left(1-\tau_{n,k,1}\right)\xi\left( h_{n,k,1}\frac{s_{n,k,1}}{\tau_{n,k,1}}+\sigma^{2}\right)-\sum_{n=1}^{N}s_{n,k,2},\forall k. \tag{18}\]Therefore, the dual variables are updated as \(\mathbf{\Gamma}^{(m+1)}=\mathbf{\Gamma}^{(m)}+\eta^{(m)}\Delta\mathbf{\Gamma}\), where \(\Delta\mathbf{\Gamma}=\left(\Delta\beta_{1},\ldots,\Delta\beta_{k},\Delta\alpha_{1,1 },\ldots,\Delta\alpha_{k,1},\Delta\alpha_{1,2},\ldots,\Delta\alpha_{k,2}\right)\) and \(\eta^{(m)}\) denotes the step size. Follow the diminishing step size policy in [37] to ensure that the optimal variables \(\mathbf{\Gamma}^{*}\) can be converged. The complexity of this method is \(O\left(\left(3K\right)^{\
u}\right)\), where \(\
u\) is a nonnegative integer.
## 3 Results and Discussion
### Coupling Impacts
Further, the Chenab River, which is the primary source of aquifer recharge in the study area, was found to have a significant level of arsenic (As) contamination above the permissible value (WHO standard) in some areas (Figure 14). This situation is alarming for the citizens of Multan that are overly concerned and already complained to the government about the arsenic-contaminated water [37; 67] identified that arsenic leaching from phosphogypsum of Pak-Arab Fertilizer Factory is deteriorating water quality and affecting agricultural fields, from where it is moved to the food chain with severe health consequences. Water wells at the Nishter hospital Multan, Cantonment board, Metro Plaza, and Basti Khudadad show the highest level of arsenic concentration (i.e., exceeding 91 ppb) (Figure 14) [58]. **Impact 3: depletion of surface water**. Significant temperature rise has reduced the air and soil moisture and infiltration rate and has increased the rate of evapotranspiration and atmospheric CO\({}_{2}\), resulting in water scarcity in the study area. The growing volume of atmospheric CO\({}_{2}\) alters global climate systems, intensifying the hydrological regime with negative consequences on the local water system [68; 69]. The historical and projected climate change trends, place Multan as one of the world's hottest cities. Climate and land-use land cover (LULC) variations have brought surface water resources deterioration and alteration in the water cycle in many other countries around the globe [70; 71]. The discrete evaluation of these changes is difficult in the sense that results may vary from one case to another [72; 73]. Climate change vulnerability in Pakistan was recognized by the National Water Policy (2018), the Climate Change Act (2017), and the National Climate Change Policy (2012). Water security has been the main concern under climate change, as well as policy measures ranging from developing awareness and additional water conservation storage [74; 75].
## 3 Results and Discussion
### Biofilm Colonization  
In order to correlate the maximum current generation obtained with biofilm formation inside the porosity of carbon felt-based electrodes, epifluorescence microscopy was used in conjunction with ESEM imaging to explore the microbial development on and inside the porous carbon felt. Each of the bianodes formed by chronoamperometry on modified and unmodified carbon felt was cross-sectioned so that the external and the internal distribution of the microbial colonization could be imaged. The images of the LTA zeolite and bentonite-modified anodes showed an extensive biofilm covering the complete electrode surface, which was much thicker than the one obtained on unmodified carbon felt (Figure 5).  
ESEM imaging of the unmodified electrode presented an open structure with space between colonized fibers, while the modified ones had a very tight network of interwoven threads. An almost uniform biofilm was observable on bentonite-modified carbon felt (Figure 5C), while the LTA zeolite-modified  
Figure 4: Cyclic voltammetries recorded on untreated carbon felt and modified carbon felt electrodes in tannery wastewater and 20 mM of acetate inoculated with acclimated activated sludge. (**A**) At the beginning of the experiment and (**B**) at the maximum current density measured during the chronoamperometry.  
carbon felt surface was only partly clogged (Figure 5B). The images acquired in epifluorescence microscopy confirmed that the colonization was denser on the modified electrodes (Figure 5).  
It should be noted that the electrode cross section (Figure 6) shows that the modification of carbon felt allowed a large cross-sectional area for the electroactive biofilm to penetrate into the 3D structure and exploit the internal surface area of the electrode, while unmodified ones are not colonized at all. In contrast, [PERSON] et al. attributed the weak biofilm penetration into the 3D porous electrode to the formation of a surface biofilm that clogged the porosity of the felt [18]. These findings contradict our results because, even when the surface was not totally colonized, biofilm was unable to penetrate into the electrode, which could be explained by the superhydrophobic nature of the carbon felt. However, the superhydrophobic nature of the modified electrode allowed the medium to penetrate into the electrodes, thus making it possible for biofilm colonize the 3D carbon felt.  
Figure 5: ESEM and epifluorescence microscopy images of bioanodes formed on unmodified carbon felt electrodes (**A**,**D**), LTA zeolite-modified electrode (**B**,**E**) and bentonite-modified electrode (**C**,**F**) polarized at \(-0.2\) V/ECS for 45 days in tannery wastewater supplemented with 20 mM of acetate and inoculated with acclimated activated sludge.  
Figure 6: Epifluorescence microscopy images of cross sectioned anodes formed on carbon felt (**A**), LTA zeolite-modified anodes (**B**), and bentonite-modified anodes (**C**).
## 4 Core Insights and Critical Reflections  
### Evolution and Breakthroughs of Key Technologies  
The scientific evolution path of UAV-RS is profoundly reflected in the continuous evolution and breakthroughs of its key technologies, which mainly include the core aspects of flight platforms, sensor technology, flight control and navigation systems, as well as data processing and analysis methods (Table 8). The progress of these technologies collectively drives the enhancement of UAV-RS capabilities and the expansion of its application scope.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Field of Technology** & **Evolution and Breakthrough** & **Representative Technologies/Trends** \\ \hline Flight Platform & From single-function to diversification and high-performance; progress in materials, aerodynamic configuration, and navigation and control technologies. & Mult-rotor, fixed-wing, and VTOL UAVs; lightweight and high-strength materials such as carbon fiber; long-endurance, high-payload, strong environmental adaptability, and high-degree autonomy. \\ \hline Sensor Technology & From single-optics to multi-type integration; miniaturization, light-weight, high-precision, intelligitization, and integration. & RGB, multispectral, hyperspectral, thermal infrared, LiDAR, SAR, and gas sensors; solid-state LiDAR; polarimetric remote sensing; integration of sensors with AI. \\ \hline Flight Control and Navigation Systems & From manual remote control to high-degree autonomy; significant improvement in positioning and attitude control accuracy; intelligent path planning and obstacle avoidance. & GNSS (GPS, Beidou), IMU, RTK/PPK, SLAM, VIO; AI-assisted navigation (TAN); UAV swarm; advanced guidance, navigation, and control (GNC). \\ \hline Data Processing and Analysis Methods & From traditional photogrammetry to intelligent processing; widespread application of deep learning; driven by the demand for real-time and automated processing. & SfM-MVS, radiometric/geometric correction, image fusion/classification; deep learning (CNN, RNN, GAN) for classification, object detection, segmentation, change detection; edge computing; cloud computing; big data processing technologies. \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evolution and breakthroughs of key technologies in UAV-RS.