esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques
## 4 Conclusions  
Point process analysis is widely used for the investigation of the spatial dependences or associations between different positions in a region of interest. The proposed techniques are especially useful in cases when spatial patterns need to be simulated, and modeled realizations of the spatial process represent the real data as best they can. One of these cases is the situation when the reproduction of the antenna positions are needed to be investigated, and spatial patterns are produced by simulations of the specific type of point process, such as the Poisson point process.  
In this work, analysis of the Poisson point process is presented, and pattern realizations of the proposed process are illustrated by simulation data, explaining as closely as they can the real antenna positions. Investigation of the spatial association between position distances considering neighborhood structure is undertaken by using [PERSON]'s K-function and L-function. Application of these distance functions proves useful in the explanation of the simulated data and by extension for the real data.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 2 Materials and Methods
### Datasets: Terrain, Cell-Tower Locations, and Popular Walking Routes  
The proposed framework was evaluated through a case study of the Lake District national park, focusing on Keswick as a region with high mountainous peaks and frequent emergency callouts. Elevation data were obtained from the open-access Shuttle Radar Topography Mission (SRTM) 1-arc global dataset [34], which has a 30 m resolution (at sea-level at the Equator). Each data file covers a one-degree-of-latitude by one-degree-of-longitude tile of Earth's surface. The dataset is composed of the following formats:  
* Digital terrain elevation data, which involve a standard mapping format that contains a matrix of vertical elevation values spaced at regular horizontal intervals measured in geographic latitude and longitude units.  
Figure 1: Two-stage conceptual framework to address path planning optimization for a UAV relay.  
* Band-interleaved-by-line, which is a binary raster format with an accompanying header file which describes the layout and formatting of the file.
* Georeferenced tagged image file format, which is a TIFF file with embedded geographic information.  
These data were first imported as geographic raster files to obtain the data grids and their corresponding spatial information, including limits, grid spacing, and coordinate references. The elevation data were then interpolated to achieve a finer resolution, yielding the result shown in Figure 2.  
The cellular tower locations and popular cycling routes in Keswick were obtained from GPS data available in the Opencellid and GPS Cycle and Walking Routes open-access databases [35]. Some cellular towers, however, had an approximate location estimated from signal measurements or provided by the carrier company.
# Overview of the Development of 3D-Printing Concrete: A Review
## 3 Performance Requirements of 3D-printing Concrete Materials  
### Printability of 3D-printing Concrete Materials  
3D-printing concrete technology is different from traditional molding concrete technology, and 3D-printing concrete technology has stricter requirements for materials. The printing material not only needs to have enough fluidity to ensure the smooth pumping of the material and continuous extrusion from the nozzle. It also needs to have good water retention to avoid the clogging of the pumping tube due to material segregation, and it also needs to have enough hardening speed to maintain the stable accumulation of subsequent layers to build [14]. Therefore, the printability of the material mainly includes fluidity, extrudability, buildability, and setting time.
## 5 Conclusions  
SIF images will improve our understanding of carbon cycles in forest ecosystems. Due to complex vegetation canopy and topographic conditions, it is challenging to accurately model SIF in boreal forests. To obtain continuous SIF images of forests at finer spatial resolutions, MODIS EVI and meteorological temperature data were selected to build multi-regression models in the growing season of a boreal forest, and conclusions were drawn.  
In the growing season, forest SIF increased from Julian day 145 to 209 and subsequently decreased until Julian day 257 which is in agreement with the plant-growing season. Broad-leaved forests usually have a higher SIF than needle forests because of a number of complex effects related to the leaf and canopy bio-chemical and structural variables, as well different efficiencies in using absorbed sunlight.  
Contrary to previous research, no significant relationship between SIF and GPP was found in this study due to different spatial resolutions, GPP dataset accuracy, and vegetation types. A significant correlation was found between SIF and EVI, and the correlation coefficients depend on the spatial resolutions; they are inversely proportional to spatial resolutions. Relationships between SIF and temperature and precipitation and PAR on daily and 16-day time scale were also calculated, and a significant correlation between SIF and temperature was found at both daily and 16-day time scale.  
Finally, spatially continuous maps of SIF with a high spatial resolution were computed by means of a multiple regression model between MODIS EVI and air temperature; satisfactory results were obtained (R\({}^{2}\) = 0.71, RMSE = 0.13 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)), i.e., better than only using MODIS EVI (R\({}^{2}\) = 0.58, RMSE = 0.14 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)), indicating that in forest regions, MODIS EVI and temperature could explain SIF.  
**Author Contributions**: [PERSON] and [PERSON] contributed equally to this work and are co-first authors of the article. Methodology, M.G.; software, M.G. and [PERSON]; formal analysis, [PERSON] and [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON] and [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; funding acquisition, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by the National Natural Science Foundation of China (41871103 and 41771179) and the National Key Research and Development Projects (SQ2018 YFE020343).  
**Acknowledgments**: We would like to thank the NASA Goddard Earth Science Data and Information Services Center for the use of OCO-2 data in this study. We also would like to thank NASA/GMAO (Global Modeling and Assimilation Office) for the use of MERRA-2 data and the National Meteorological Information Center of China for the meteorological data.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Effects of Feature Reuse Structure and HDAM  
To investigate the effects of the feature reuse structure and HDAM on the OSR performance of HCPL, we carried out OSR experiments on the Jamming-7 dataset using HCPL-CNN, HCPL-FRNet and HCPL-HAFRNet, and the experimental results are shown in Figure 8. As seen in Figure 8a, HCPL-HAFRNet has the highest classification accuracy of the known classes, and the rest are too close to each other. When the JNR > 5 dB, the classification accuracy of all three algorithms for the known classes is close to 100%. Therefore, even with close-set multiclassification for interference signals, HAFRNet can improve the accuracy under low JNR while maintaining the accuracy under high JNR. As seen from Figure 8b, the HCPL-HAFRNet algorithm has the highest rejection performance of the unknown classes, and the other two are close. Hence, it can be seen that HDAM can improve the performances of both the known class classification and unknown class rejection.  
Figure 7: Effects of the hyperparameters in HCPL. (**a**) The classification accuracy under specific \(\lambda_{c}\). (**b**) AUROC under specific \(\lambda_{c}\). (**c**) The classification accuracy under specific \(\lambda_{r}\). (**d**) AUROC under specific \(\lambda_{r}\).  
The computational complexity and storage complexity of an algorithm are related to the number of the network parameters. The CNN has 518,768 parameters, and FRNet has 389,744 parameters, while HARFNet has 416,624 parameters. Therefore, HARFNet improves the performance of the known class classification and unknown class rejection at the same time while reducing the complexity compared with the CNN.  
The dataset used in each experiment consists of known and unknown classes that are randomly sampled from nine interference signal classes and combined. Therefore, the rejection performance of the unknown classes in each random experiment using the same method is different because of the difference of the split of known and unknown classes. The variance of the AURoc is used to measure the effects of different partitions of known and unknown classes on the rejection performance of the unknown classes. We perform OSR experiments on the Jamming-7 dataset using HCPL-CNN and HCPL-HAFRNet. The results are shown in Figure 9. The variance of AURoc of HCPL-HAFRNet is much lower than that of HCPL-CNN under various JNR, and it decreases as JNR increases, while the variance of AURoc of HCPL-CNN increases as JNR increases. Therefore, the robustness of HCPL-HAFRNet in the unknown class rejection is much better than that of HCPL-CNN.
# Assessing the Performance of Different Grains in Gluten-Free Bread Applications
###### Abstract  
A comparative analysis of quinoa, sorghum, millet and rice flours and breads in terms of proximate composition, resistant starch, antioxidant activity and total phenolic content was realized in this study. Quinoa whole flour had the highest content of proteins, fat, ash and total dietary fiber, followed by millet and sorghum flours. Quinoa and rice breads had higher specific volume (192.22 and 181.04 cm\({}^{3}\)/100 g, respectively) and lower crumb firmness (10.81 and 13.74 N, respectively) compared to sorghum and millet breads. The highest total phenol content was obtained in the case of bread prepared with quinoa flour (398.42 mg ferulic acid equiv/100 g d.w.), while the lowest content was obtained for the rice flour bread (70.34 mg ferulic acid equiv/100 g d.w). The antioxidant activity of gluten-free breads decreased in the following order: sorghum \(>\) quinoa \(>\) millet \(>\) rice. Quinoa bread had the highest resistant starch content of 3.28% d.w., while the rice bread had the highest digestible starch content of 81.48% d.w. The slowly digestible starch varied from 15.5% d.w. for quinoa bread, to 6.51% d.w. for millet bread. These results revealed the huge potential of quinoa, sorghum and millet to be used for developing functional gluten-free bread.  
quinoa; millet; sorghum; rice; gluten-free bread +
Footnote †: journal: Applied Sciences
# Cybersecurity Challenges in the Maritime Sector
## 3 Cyberattacks on the Ship Automation Systems
### Video Surveillance Systems  
Video Surveillance Systems (VSSs) play a crucial role in the security and safety of vessels, cargo, and crew in all types of modern ships [45]. These systems are mainly used for monitoring and tracking the ship's critical operations and for protecting against attacks by terrorists and pirates [45; 46]. However, VSSs have been recently found vulnerable to several cyberattacks, and a number of security issues have arisen [24; 46]. For instance, researchers from Bildefender found that two models of CCTV cameras, used in modern ships, are vulnerable to buffer overflow flaws. By exploiting this vulnerability, the researchers were able to track the activities of the hacked camera and overwrite passwords [46]. Moreover, this vulnerability can cause a VSS system crash or, worse, create an entry point for other cyberattacks [46].
## 2 Background and Related Work
### Modelling Obstructions
#### 2.1.2 Modelling with 2.5D Rasters  
Although photogrammetric principles applied to imagery captured by manned aircraft [24] are a well practiced method for creating building models for GNSS prediction surveys, it is also possible to model an obstruction using a 2.5D raster DSM (LOD0). Unmanned aerial vehicles (UAVs) are now a well established method for creating this type of DSM. Figure 3b displays a DSM of a portion of the North Campus, Maynooth University created using a Falcon 8 Octocopter [25], a Sony Nex-5N SLR and the Pix4 Dmapper software [26]. Using 2.5D raster datasets to represent obstructions in an area are a less complex alternative to 3D vector datasets such as the campus model, and UAVs are increasing in popularity among surveyors for topographic and building surveys. Additionally, 2.5D raster datasets are generally much smaller than their 3D vector counterparts and this reduced filesize allows for more efficient processing. Although 2.5D rasters are suitable for GNSS shadow prediction, they still require a survey of the area of interest and subsequent processing to create the DSM. The approach proposed in this paper is a methodology for bypassing the time-consuming aerial survey requirement by employing existing 2D vector data and basic elevation data to create the 2.5D DSM.  
Figure 3: Modelling obstructions (**a**) a 3D vector model of Maynooth University South Campus created during the 3D Campus project (**b**) a 2.5D raster DSM of buildings on Maynooth University North Campus created using photogrammetric methods from imagery captured by a Falcon 8 UAV.
# Potential of Liquid-Crystal Materials for Millimeter-Wave Application
###### Abstract  
In this study, we reviewed three topics regarding the application of liquid-crystal (LC) materials to millimeter-wave (MMW) devices. It is essential to develop useful measurement methods for refractive indices of LC materials in the MMW region. Herein, a novel measurement method using optical short is demonstrated using a Si semiconductor substrate. There are two approaches to develop MMW LC devices. One is the quasi-optical approach, which involves scaling up the optical components, and the other approach involves integrating the LC materials into high-frequency electric circuits. A three-dimensional (3D) printer is used to fabricate the Fresnel lens, which is a typical quasi-optical device useful in the MMW region, where we can develop the tunable lens by introducing LC materials. A planar-type MMW waveguide is advantageous for integrating the LC materials to develop LC MMW devices using the second approach. We investigated a useful microstrip-line-type LC phase shifter by developing a novel conversion circuit to introduce the LC material onto the dielectric substrate surface. A phase shifter is an important MMW component that is used to attain a phased array antenna system, and a minimal twin antenna array is demonstrated using the microstrip-line-type LC phase shifters.  
liquid crystal; millimeter wave; refractive index; 3D printer; Fresnel lens; phase shifter; phased array; beam steering +
Footnote †: journal: Applied Physics Letters
# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 3 Method  
In the proposed method, we combine the properties of both feature-based and area-based methods. The area-based methods use image similarity between overlapped areas between the reference and the sensed images. Template matching is one of the area-based method. Usually, one template matched image provides one control point between the template and the target images. To increase the numbers of control points, we use multi-template matching approach. The main steps in the proposed method are shown in Figure 3. First, the input images (the reference and the sensed images) undergo a thorough contrast adjustment process based on global and local histograms matching. In the next step, area based features are extracted by using zero mean normalized cross correlation (ZNCC) and fast Fourier transform (FFT). Once sufficient and well-distributed control points are extracted and matched, the remaining steps for registration such as transformation estimation and re-sampling are done. The detailed description of each step is given in subsequent sections.  
Figure 3: Flow chart of the proposed registration steps: the red box indicates image adjustment, while the blue box indicates feature extraction.  
Figure 2: Relative spectral response: panchromatic images (0.50–0.90 μm), blue band (0.45–0.52 μm), green band (0.52–0.60 μm), red band (0.63–0.69 μm), and NIR band (0.76–0.90 μm).
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 4 Results and Discussion
### Comparative Studies  
The proposed model was compared withFamNet [28], VLCounter [37], CSRNet [12] and SAFECount [29] across four public datasets. To ensure a fair comparison, all experiments were conducted on the same hardware, and the comparative models were trained using the optimal weight files provided in their original publications. Table 2 presents the results of the comparative experiments on the four datasets.  
The experimental results in Table 2 show that, compared to other models, the proposed method achieves varying degrees of reduction in both MAE and RMSE across four datasets, with optimal performance observed on the TRANCOS and UCSD datasets. This indicates that multi-angle processing of support and query features in the spatial dimension helps capture feature information from different directions, enhancing the model's comprehensive understanding of object features. This, in turn, mitigates the difficulties in feature extraction that are caused by variations in object angles. Moreover, the global pooling and feature enhancement of similarity features in the channel dimension facilitate the integration of information across channels, enabling the model to more comprehensively understand object features in the image.  
A noteworthy comparison can be made with SAFECount. While SAFECount also adopts a similarity-matching framework, it overlooks the multi-angle feature processing of objects. As a result, its recognition performance is suboptimal for images with diverse object angles. In contrast, the proposed SSA module addresses this issue effectively. As shown in the experimental results, our model outperforms the SAFECount overall.  
However, during the experiments on the CARPK dataset, although the MAE and RMSE of the method presented in this chapter have decreased compared to the SAFECount model, the performance still has not reached the expected ideal state. Through in-depth exploration of the root cause, it is found that this is mainly due to the unique characteristics of the CARPK dataset itself. All the image data in this dataset were sourced from an unmanned aerial vehicle (UAV) collection platform, and the collection height is fixed at 40 m. Under such specific collection conditions, the angular variations of objects in the acquired images are relatively insignificant, which significantly differs from the issues focused on in this chapter. When the angular variations of objects are not prominent, the feature extraction and processing mechanisms designed in this chapter's method to address angular changes are difficult to use to their full advantage. As a result, the performance of the model on this dataset is limited. This outcome further emphasizes the importance of the adaptability between the model and the characteristics of the dataset, providing valuable references for the subsequent optimization of the model and the selection of datasets.
# Analysis of Cavity Corner Geometry Effect on Recirculation Zone Structure
## 4 Conclusions  
In this study, dynamics of laminar flow in open-type cavities with differently rounded corner geometries were numerically investigated.  
Investigation of recirculation zone structure based on cavity corner shape showed that corner geometry determines the size and number of vortices in the cavity. The strongest recirculation zone is formed in the right-angled cavity (Ch1), and vortex size decreases in half-rounded (Ch4) and fully rounded (Ch2) bottom corners and rounded upper (Ch3) corners due to a reduced adverse pressure gradient behind the leading cavity wall. Rounded corner type and radius also affect the location and size of the transverse vortex centre with respect to the side and trailing walls of the cavity.  
Parametric analysis revealed that pressure losses are notably lower with rounded upper cavity corners (Ch3) due to reduced recirculation zone size and an increased effective flow cross-section. Flow stability analysis showed that fully rounded cavity corners (Ch2) negatively impact flow stability by increasing _Tu_ near the trailing cavity wall. The gradually decreasing channel cross-section near the trailing wall strengthens the interactions between the recirculation zone and the main flow, causing flow perturbations to appear.  
Broader analysis of flow structure on flow regime and investigation of heat transfer processes dependent on small changes to cavity geometry will be presented and discussed in forthcoming papers.  
J.S.: Conceptualization, Validation, Investigation, Formal analysis and Writing. P.V.: Conceptualization, Investigation, Supervision and Writing. N.P.: Conceptualization, Resources and Project administration. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors