esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Plasmon Nanolaser: Principle, Structure, Characteristics and Applications
## 3 Structures and Characteristics of Plasmonic Nanolasers
### Nanowire Array Plasmonic Nanolaser  
In 2017, [PERSON] et al. from the University of Electronic Science and Technology of China reported the design and manufacture of a new type of regular array plasmonic nanolaser that works at room temperature [61].  
The structure is shown in Figure 8a. In this work, firstly, n-type ZnO nanowires (NWs) were fabricated on p-type GaN substrates by using chemical vapor deposition (CVD) and other subsequent microfabrication processes. The regular NW array has a length of about 5.1 \(\upmu\)m and a diameter of 120 \(\pm\) 10 nm. In order to obtain the regular array growth of NWs on the substrate, the growth positions of NWs are controlled in the regular hole array on the substrate, which is fabricated by electron beam lithography (EBL). Ag-dielectric hybrid nanofilms were sputtered on ZnO/GaN nanojunction arrays to excite surface plasmas at the interface of NWs and to ensure the limitation of plasma mode on nanolasers. Then, the laser behavior of the nano-heterojunction array device is experimentally demonstrated by the optical pumping method. The optical pumping threshold of the plasmonic nanolaser is about 184.9 mW/cm\({}^{2}\). As shown in Figure 8d, all the peak modes are at the same wavelength (387.5 nm) at different pump power densities; that is to say, the excitation light comes from the same mode. However, due to the high pump power and the excitation of an adjacent NW which is slightly different in length, another mode begins to appear. Based on the highest peak mode of the measured spectrum, the quality factor of the fabricated device is 1937.5. Compared with other nanolasers confined by the mode of surface plasmon, it has a significant quality factor advantage. Laser beam point mapping experiments show that with the increase of pump power density, as shown  
Figure 7: Applications of all-inorganic halide perovskite nanowires in laser field. (**a**) SEM (scanning electron microscope) images of CsPbBr\({}_{3}\) nanowires and nanoplates, (**b**) a single CsPbBr\({}_{3}\) nanowire isolated on a quartz substrate with a 5 nm Au sputter coat, (**c**) a dark-field photo of a single CsPbBr\({}_{3}\) nanowire below the lasing threshold, (**d**) a dark-field photo of a single CsPbBr\({}_{3}\) nanowire above the lasing threshold, (**e**) the power-dependent emission spectra of a single CsPbBr\({}_{3}\) nanowire, and (**f**) the time-resolved fluorescent spectra of CsPbBr\({}_{3}\) nanowires below (red) and above (blue) the lasing threshold [59].  
in Figure 8e-f, the laser intensity gradually increases, the light gradually concentrates, and the laser beam is combined in an angle range of +30 degrees.
## 2 Materials and Methods
### MTT Assay of Cell Viability  
RAW264.7 cells were seeded in 96-well plates at a density of 5 \(\times\) 10\({}^{3}\) cells/well in complete DMEM and incubated for 24 h under SC to adhere. THP-1 cells were seeded in 96-well plates at a density of 50 \(\times\) 10\({}^{3}\) cells/well in complete DMEM medium supplemented with 200 nM PMA and incubated for 72 h under SC to generate human M\(\varphi\) as described above. Then, RAW264.7 and M\(\varphi\) cells were incubated in the corresponding complete culture media supplemented with 5, 10 and 20 uM of the respective lipid-oligonucleotide conjugate (LON) for an additional 24 h under SC. Cytotoxicity of LONs was assessed by MTT assay. Briefly, MTT solution (Sigma-Aldrich, Darmstadt, Germany) was added to the cells up to 0.5 mg/mL and the incubation was continued under SC for an additional 2 h. Then, the MTT-containing medium was aspirated and the formazan crystals formed in living cells were solubilized with 100 uL/well of DMSO. The absorbance of each well was read at the test and reference wavelengths (\(\lambda\)) of 570 and 620 nm, respectively, on a Multiscan RC plate reader (Thermo LabSystems, Helsinki, Finland). The percentage of living cells was calculated as follows:  
\[\text{Living cells (\%)}=(\text{OD}_{570\text{exp}}-\text{OD}_{620\text{exp}})/( \text{OD}_{570\text{cont}}-\text{OD}_{620\text{cont}})\times 100, \tag{1}\]  
where OD\({}_{570\text{exp}}\) and OD\({}_{570\text{cont}}\) correspond to optical density in experimental and control wells, respectively, at \(\lambda\) 570 nm, and OD\({}_{620\text{exp}}\) and OD\({}_{620\text{cont}}\) correspond to optical density in experimental and control wells, respectively, at \(\lambda\) 620 nm. All experimental points were run in triplicate for statistical analysis.
# Overview of Mobile Communications in Colombia and Introduction to 5G
## 3 Analysis and Results
### Failures in 5G Deployment in Colombia
While the first three events took place over four years, it took six years to carry out the auction of 4G services, which is an important part of the future 5G rollout, as forecasts show that its initial deployment phase will be based on the nonstandardized architecture (NSA) used by the 4G-LTE network, allowing 5G to be implemented in less time using existing infrastructure [76; 77]. Three years have passed since the 2019 auction and there is still no planned or estimated date for the 5G spectrum auction, adding to Colombia's delay in its implementation. As explained above, the NSA will facilitate the deployment of 5G, but without allowing access to the full benefits of standalone (SA). Therefore, it is expected that by the end of 2022 at least 80% of mobile Internet access will be via 4G [61]. Despite initiatives and expectations, 4G classification in Colombia has not been uniform, as not all municipalities have access to all operators' networks and only one operator offers service in close to 99% of localities [61]. To improve this situation, companies that won spectrum in the 700 MHz band in the 2019 auction made commitments related to increasing coverage. MinICT Resolution 3078 of 2019, which regulated the process, obliges providers to upgrade 100% of municipalities that had 2G or 3G in 2019 to 4G technology within four years. However, there is no explicit obligation to extend coverage to localities that do not have any kind of mobile network, nor to extend their networks to all of them [78], and by 2025, the projected percentage of coverage will be 80% in rural areas [79], with a significant percentage remaining unserved.
## 2 Materials and Methods
### Multi-Platform Point Cloud Fusion  
Considering the 3D-NDT algorithm changes the traditional fine alignment strategies based on corresponding point pairs, which divide the discrete point cloud into continuous and differentiable probability distribution functions (PDFs), a transformation parameter can be calculated that makes the target point cloud most likely to be located in the reference point cloud through the standard optimization technique. Therefore, after extracting the identical area based on the minimum moment of inertia, the improved 3D-NDT algorithm is introduced to register the identical areas. A detailed description has been presented in the work of [PERSON] et al. [38].  
Although multi-platform point cloud alignment compensates for the loss of data from a single platform, the overlapping areas also increase the amount of point cloud. Considering roughness is an important feature that characterizes the smoothness of the point cloud surface, it can be adopted during the denoising of the point cloud. However, roughness metrics vary between TLS and HLS datasets due to different capture angles and resolutions, making it difficult to apply a uniform downsampling criterion, which leads to inconsistent point density and data quality across the fusion point cloud. In our study, the roughness means and standard deviation of the point clouds are regarded as the setting threshold. Specifically:  
1. Setting the search radius, and a local plane is fitted by the least squares algorithm based on the point cloud inside the local circle.
2. The distance from the original point cloud to the projection of the reference plane is calculated.
3. The mean value of these distances is calculated to characterize the point cloud roughness.
4. When the roughness of the point cloud is larger than the setting threshold, the point cloud is removed. On the other hand, if the roughness of the point cloud is less than the setting threshold, the point cloud is preserved.
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data  
### Study Area  
The study area, Tarim city, is located in the Hadhramout province of Yemen (15 deg45'-16 deg15'N, 48 deg45'-49 deg15'E) (Figure 1). The geology of the study area is a series of thick, flat-lying sedimentary formations eroded into a complicated Wadi pattern, including limestone [53]. The soil type's classifications in Wadi Hadramout are dominated by rock (73.09%), which directly affects increased runoff. Gravel soil is classified as the second type (13.18%). This results in reduced soil infiltration and surface water flow (Runoff). Sand, clay, and silt are the remaining categories, accounting for 13.52% [54].  
During summer, the mean temperature of the study area is around 35 \({}^{\circ}\)C, while the mean temperature in winter is approximately 19.7 \({}^{\circ}\)C. The annual mean precipitation of the area is 100 mm [55]. Due to changes in land-use patterns, rapid population growth, migration, unplanned urbanization, and facilities construction in flood-prone areas without adequate drainage capacity, environmental degradation and global climate change are significant reasons for unexpected flooding. Another cause is that hills surround the study area; the rainfall-runoff from this hilly area brings a considerable water inflow to Tarim city during the monsoon season [55; 56].  
During 1996 and 2008, several floods occurred in the study area, which destroyed, killed, and washed away human and animal lives, hydraulic structures, and fertile land [3; 54]. On 2 May 2021, a flash flood affected the area, resulting in four confirmed deaths and injuries; officials and partners claimed that 167 households were impacted, where their homes were either partially or wholly destroyed [57]. The intensity of precipitation on 27 October 2008, was nearly 91 mm, which resulted in catastrophic floods in the Hadramout [3]. The majority of the damaged structures in the study region were composed of conventional mud bricks with stone foundations (Figure 1) [55]. The natural disaster had a tremendous impact on housing, with 561 dwellings demolished, and floods not only damaged buildings but also wreaked havoc on agricultural land [52]. While the studied region received heavy precipitation in 2021, flooding in the neighborhood of \"Al-Shabika\" and [PERSON]'s \"[PERSON]'s palace\" destroyed dozens of historic dwellings and caused large-scale damage [57].
# Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer
## 2 Materials and Methods
### Score-Based Multi-Scale Feature Enhancement Method  
Metal corrosion areas often have irregular shapes and sizes, with significant variability between different corrosion regions. To address the characteristics of metal corrosion, multi-scale convolutional kernels can be used for feature extraction in metal corrosion area detection, and feature enhancement can be applied to improve model performance. SimAM (Simple Attention Module) is a module designed to enhance feature saliency in three-dimensional space based on neural theory, aiming to strengthen the representational capacity of convolutional neural networks [21]. This method is grounded in visual neuroscience theory, where neurons carrying more information stand out compared to their neighboring neurons and cause spatial inhibition to adjacent neurons. When processing visual tasks, neurons that contain more critical information should be assigned higher weights [22].  
The structure of the score-based feature enhancement module proposed in this paper is shown in Figure 8. The input features first undergo multi-scale convolution operations to expand the receptive field and improve the ability to capture details, followed by feature enhancement using the SimAM module. Given the input feature map \(F_{1}\in R^{C\times H\times W}\), the calculation process of SimAM is as follows:  
1. For each channel C of the given input feature map, calculate the mean value of the entire channel \(\mu_{c}\): \[\mu_{c}=\frac{1}{H\times W}\sum_{h=1}^{H}\sum_{w=1}^{W}x_{c,h,w}\] (8)  
Figure 7: Multi-layer feature fusion based on SE-Block.  
In the equation, \(x_{c,h,w}\) represents the value of channel C at position (\(h\),\(w\));
2. Calculate the squared difference between each pixel value and the channel mean in the feature map, which is used to highlight the deviation of each pixel: \[e_{c,h,w}=\left(x_{c,h,w}-\mu_{c}\right)^{2}\] (9)
3. Calculate the saliency score \(s_{c,h,w}\). First, normalize the squared differences to maintain numerical stability in subsequent calculation and add a regularization term \(\lambda\) to avoid the influence of extreme values. The formula is as follows: \[s_{c,h,w}=\frac{e_{c,h,w}}{4\frac{\sum_{k=1}^{h}\sum_{k=1}^{w}e_{c,h,w}}{H \times W-1}+\lambda}+0.5\] (10)
4. Apply the activation function to process the attention scores and use them to adjust the importance of each pixel in \(x_{c,h,w}\), resulting in the output \(y_{c,h,w}\): \[y_{c,h,w}=x_{c,h,w}.\sigma(s_{c,h,w})\] (11)
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 1 Introduction
Extremely high XCO\({}_{2}\) anomalies are usually accompanied by events such as extreme droughs or floods that reduce vegetation productivity [30; 31]. [PERSON] et al. [32] found a decrease in tropical XCO\({}_{2}\) corresponding to the gross primary production (GPP) increase in the wet season and a XCO\({}_{2}\) increase associated with strong biomass burning in dry season in Southern Amazonia. These studies show that XCO\({}_{2}\) observations started in 2002 are just beginning to yield insight into variability and trends in surface CO\({}_{2}\) fluxes as the records now extend to just over 15 years. Regional CO\({}_{2}\) fluxes have been inferred from satellite-observed XCO\({}_{2}\) and in-situ surface observations using atmospheric transport models [26; 27; 33]. Incorporating XCO\({}_{2}\) into atmospheric inversions improves upon sparse and location-biased in-situ surface observations and yields estimates that are less sensitive to atmospheric transport model uncertainty [34; 35; 36]. It has also been noted that including XCO\({}_{2}\) constraints in atmospheric inversions shifts regional biospheric uptake from the tropics to the boreal latitudes [33]. Surface flux estimates from atmospheric inversions are most robust across large scales, like the Transcom regions [37] and diverge at sub-continental scales and smaller [35]. This is due largely to uncertainty in atmospheric transport models and the typically sparse surface CO\({}_{2}\) constraints, which may attribute observed atmospheric CO\({}_{2}\) anomalies to many different source/sink spatial distributions. In this analysis, we assume that observed localized spatial XCO\({}_{2}\) anomalies are likely more sensitive to local surface CO\({}_{2}\) fluxes in that specific region than flux estimates derived from atmospheric inversions. This is because there is finer spatial resolution in XCO\({}_{2}\) observations than estimated inversion CO\({}_{2}\) fluxes. Therefore, sub-continental-scale CO\({}_{2}\) fluxes may be inferred from XCO\({}_{2}\) anomalies directly. Eastern China (EC) is one area with high densities of vegetation cover and greening trends. Even though there are some modeled carbon sequestration estimates of China's Grain-to-Green Program [11; 12; 13], the magnitude of CO\({}_{2}\) uptake has not been validated at the regional scale. This study aims to quantify recent trends in regional carbon uptake by quantifying changes in satellite-observed XCO\({}_{2}\) during the growing period (GP). We adopt various satellite-observed vegetation parameters to characterize the seasonality and locations of increased vegetation photosynthetic activity in EC and examine XCO\({}_{2}\) trends in those regions specifically.
# A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
## 1 Introduction
This ensemble approach, tailored to specific use cases, achieved full accuracy in overall performance testing, proving highly effective for practical applications in the manufacturing industry. It is essential to acknowledge the complexities introduced by the diverse range of defect types, such as scratches, dents, and corrosion, each varying significantly in shape, size, and visibility [14]. Traditional computer vision techniques, while less computationally demanding, often fall short in flexibility and adaptability, struggling with high noise levels and variability in defect manifestations, which are common in industrial settings [35]. Conversely, deep learning models, despite their superior performance in learning from large datasets and generalizing across different defects, require extensive computational resources and substantial amounts of labeled data, which complicates their application in real-life industrial scenarios [36]. One of the most promising solutions for industries is the YOLOv7 object detection model, which aims to bridge these gaps by enhancing detection accuracy while meeting the operational demands of real-time industrial applications [37]. YOLOv7 is the current state-of-the-art for real-time object detection tasks in terms of both speed and accuracy [37]. However, this benchmark is based on the Microsoft COCO dataset. A benchmark of YOLOv7 on the defect datasets is essential to observe and investigate its performance on metal defect detection tasks. This paper focuses on adopting the state-of-the-art model, YOLOv7, for detecting defects and damages on metal surfaces and investigating the model's behavior on some public datasets of metal defects. This can also be used as a reference for future comparisons of the enhanced models based on YOLOv7 with the standard. Furthermore, a comparison of the existing defect detection models with the YOLOv7 is conducted and studied. A case study validation is also conducted on synthetic data inspired by an industrial setting, demonstrating the real-world application and efficacy of the YOLOv7 model in identifying and classifying defects.
# Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals
## 2 Proposed Methodology
### Gaussian Filter  
A Gaussian filter uses a Gaussian function, which is defined by its mean and standard deviation and has a bell-like shape. The amount of smoothing applied to the CWT images can be modified by varying the standard deviation [32]. In this study, a Gaussian filter was used to increase the quality of CWT images by minimizing noise and smoothing the images.  
Figure 2: CWT scalograms (**a**) normal and (**b**) leak conditions.  
The 2D digital Gaussian filter can be mathematically represented as:  
\[G\ (x,y)=\frac{1}{2\pi\sigma^{2}}\ e^{-\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{2}\]  
where \(\sigma\) represents the variance of the Gaussian filter. The filter kernel size, typically ranging from \(-1\) to 1 for both x and y, is chosen by excluding values less than five percent of the kernel's maximum value [38].  
This filter works by blending the CWT image with a Gaussian function, giving more weight to nearby pixel values and less to those farther away. This process effectively minimizes minor, random variations in pixel energy while maintaining the essential features and overall shape of the image. By fine-tuning the standard deviation, the optimal amount of smoothing can be applied, making the images clearer and easier to analyze. Figure 3 shows the CWT scalograms of normal and leak images after applying a Gaussian filter.  
This method utilized the Gaussian filter's capability to maintain the key features of CWT images while reducing noise, thereby improving the visual quality and reliability of the images for subsequent processing and analysis. This approach is known for its effectiveness in noise reduction and image enhancement, making it a valuable tool in the initial stages of CWT image analysis.
## 4 Simplified Flow for Mobile Platform
### Flow of Plate Detection and Localization  
First of all, the input image is resized as shown in Figure 6. The flow of the trained neural architecture discussed in Section 3 is simplified by rescaling the input image (128 \(\times\) 64) to allow for a single license plate classification per image. Then, the plate classification (detection and localization) is performed to find the position of the plate inside the input image, if a plate is detected. This network is composed of 16 convolutional layers. The first 10 convolutional layers of this network provide shared connections for simultaneous plate detection and localization, as shown in Figure 1. The remaining six layers are for either the plate detection or localization (three layers for each task). The plate detector has two outputs, defining the detection of a plate or no plate, while the output of the localizer consists of eight values that represent the x- and y-coordinates of the four points that enclose the plate.  
The plate classifier normally generates a parallelogram. To perform the character classification, the input image is un-rotated to obtain a perfect rectangle. With other factors, the skewness of a detected plate also affects the performance of character classification. There are some skewness approaches that can be employed for reliable skew correction [16].  
After rotation (skew correction), it is necessary to obtain the updated coordinates of the detected plate. It can be performed in two different ways: by computing the coordinates based on the rotation or by reclassifying the plate. While plate reclassification is more computationally expensive, it also provides a more accurate localization, which is necessary to obtain better character detection and localization. After this re-classification, the detected plate is cropped from the original image based on the obtained coordinates; this yields a crop where the plate and characters have a fixed location and scale (given an accurate plate localization), in order to provide good conditions for the character classifier and enable it to be simplified.