esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### CMC Contents  
In this study, three different CMC contents of 0%, 1%, and 4% were selected to investigate their influences on the soil electrical resistivity. The soil samples were saturated by slurry with CMC proportionally, and the water environment was deionized water. Figure 10 shows the change in the electrical resistivity of soil samples and corresponding slurry with different CMC contents. The electrical resistivity of soil samples and slurry decreased with the increase in CMC contents. However, such influence of the CMC contents on both the soil samples and slurry gradually reduced with the increase in the slurry concentration.  
Figure 7: Electrical resistivity of sand samples (0.5–2 mm) with different slurry concentrations.  
Figure 8: Relation between formation factor of sand samples and slurry concentrations with different water environments.  
Figure 9: Schemes of slurry particle distribution. (**a**) Seawater. (**b**) Deionized water.  
Figure 11 shows the change in soil formation factors with different CMC contents. The formation factors of soil increased with the increase in slurry concentrations while they decreased with the increase in CMC contents. For the microstructural analysis, Figures 12 and 13 present the grain size distributions and micrographs (\(20\times\)) of slurry with different CMC contents. The slurry sample with the concentration of 4% was selected for the tests. As shown in Figure 12, the aggregate size measured by laser particle size analyzer corresponding to the maximum frequency increased from 0.089 to 0.123 mm with the decrease in the CMC contents. A similar change was also observed in Figure 13. The zeta potential is an indirect measure of the net charge on the colloidal particles in the slurry and reflects the ability of the particles to repel or attract each other. The zeta potential, which is an important indicator for the slurry stability, is also given in this figure. The larger the absolute value of the zeta potential, the more stable the slurry. It was measured by Zetasizer (Malvern, UK). The value of the zeta potential increased from \(-28.5\) to \(-10.8\) mV with the increase in CMC contents, and negative signs indicated that slurry particles carried negative charges. It turned out that the increase in CMC contents enhanced repulsive forces among the slurry particles and behaved as the dispersant in the slurry material. In summary, the increase in the CMC contents produces aggregates with smaller size and increases the pore spaces of soil samples, resulting in a decrease in the formation factor.  
Figure 10: Relation between electrical resistivity of the samples (slurry and sand) and slurry concentrations with different CMC contents.  
Figure 11: Formation factor of soil samples (0.5–2 mm) with different slurry concentrations.  
Figure 12: Frequency distribution of slurry aggregate size.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 5 Results And Discussion
### Ablation Experiments  
To verify the effectiveness of the strategies proposed in ITACIE-GP, this paper conducted ablation experiments for comparison. The comparison methods were divided into four types: the ITACIE-GP method proposed in this paper, ACIE-GP without the program structure based on information transmission, ITIE-GP, which has the same program structure as ITACIE-GP but does not use the accompanying evolution strategy, and ITAC  
Figure 8: Convergence curves of EFLGP and ITACIE-GP on the KTH dataset in five different scenarios. Figure 9: Convergence curves of EFLGP and ITACIE-GP on the ORL dataset in five different scenarios. GP, which has the same program structure as ITACIE-GP but lacks a multi-generational individual ensemble strategy. The four methods were run independently five times on different datasets using the same random seed. The experimental results have been analyzed based on the average and standard deviation of the classification accuracy on the test sets. As shown in Table 18, in the **overall** row, the performance differences of ITACIE-GP compared with ACIE-GP, ITAC-GP, and ITIE-GP in terms of average values are shown. Specifically, \"+\" indicates that ITACIE-GP outperforms the compared methods, \"=\" indicates that the performance of ITACIE-GP is identical to that of the comparison methods, and \"-\" denotes that ITACIE-GP underperforms relative to the comparison methods. In the 16 performance comparisons with ACIE-GP, ITACIE-GP outperformed the comparison method in all scenarios. ITACIE-GP builds efficient feature combinations based on predefined feature types, delivering outstanding classification performance for datasets in complex scenarios. Conversely, ACIE-GP mainly relies on predefined manual features, leading to diminished classification capabilities when handling images from diverse categories or complex scenarios. In 16 performance comparisons with ITIE-GP, ITACIE-GP outperformed the comparison method in 15 instances and performed equally in 1. This result indicates that a significant decline in classification performance occurs after losing the accompanying evolution strategy for continuous optimization. The reason is the lack of substantial improvement in multiple generations of optimal individuals. When implementing an individual ensemble strategy, it may combine solutions that are too similar and not particularly distinguished in classification performance, which not only fails to improve classification efficacy but sometimes may even lead to a reduction in performance. In 16 performance comparisons, ITACIE-GP showed superior performance to ITAC-GP in 10 instances, presented equivalent performance in 4, and was slightly inferior in 2. Generally, the absence of the individual ensemble strategy leads to reduced classification performance. This happens because forming a final solution with multi-generational individuals/solutions effectively avoids the overfitting issue that reliance on a single solution might cause.
## 6 Strengths, Limitations, and Future Research Directions
### Future Research Directions
#### 6.3.2 Robustness  
As documented in [5], deep learning models are typically vulnerable to adversarial attacks, where small perturbations to an input image can cause the model to misclassify it. Future research should focus on developing architectures that are more robust to adversarial attacks (for example by further augmenting the robustness of ViTs), as well as exploring ways to detect and defend against these attacks.  
Beyond that, most studies (as the ones reviewed in this work) have focused on the performance of deep learning architectures on image classification tasks, but there are many other image processing tasks (such as object detection, segmentation, and captioning) that could benefit from the use of these architectures. Future research should further explore the effectiveness of these architectures on these tasks.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 2 Data Source  
In order to acquire land and coastal waters with high-resolution optical imagery, the European Commission and the ESA jointly developed the Sentinel-2 satellite mission under the Copernicus program [35; 36]. The Sentinel-2 mission is composed of two satellite constellations (Sentinel-2A and 2B) running in sun-synchronous orbit. The orbit height is 786 km, the inclination is 98.5 degrees, the revisit period is 5 days, and the design life is 7 years [28].  
The payload of the Sentinel-2 satellites is a push-broom multispectral imager (MSI) and the payload parameter information is shown in Table 1. It has 13 channels, the spectral range is from 0.4 \(\upmu\)m to 2.4 \(\upmu\)m, and the spatial resolutions are 10 m, 20 m, and 60 m [37]. The Sentinel-2 data product is Level-1C (L1C) top-of-atmosphere (TOA) reflectance data processed by geometric correction. The download address for the data source is [[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)) (accessed on 9 March 2018).  
The selection of study areas required the consideration that the experimental areas are representative and, thus, the water and nonwater types in the study areas should be abundant and diverse. Water bodies include rivers, lakes, reservoirs, offshore, and other conventional water bodies, as well as special water bodies containing sediment, salt, and ice. Nonwater bodies include vegetation, buildings, bare land, and other objects. According to the selection criteria, Taihu Lake, the Yangtze River, Chaka Salt Lake, and Chain Lake are selected as the experimental areas. Figure 1 shows the specific locations of the study areas and the land cover descriptions of the study area data are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bands** & **Wavelength Range/\(\upmu\)m** & **Spatial Resolution/m** \\ \hline Band 1—Coastal aerosol & 0.433–0.453 & 60 \\ Band 2—Blue & 0.4575–0.5225 & 10 \\ Band 3—Green & 0.5425–0.5775 & 10 \\ Band 4—Red & 0.65–0.68 & 10 \\ Band 5—Vegetation Red Edge 1 (VRE1) & 0.6975–0.7125 & 20 \\ Band 6—Vegetation Red Edge 2 (VRE2) & 0.7325–0.7475 & 20 \\ Band 7—Vegetation Red Edge 3 (VRE3) & 0.773–0.793 & 20 \\ Band 8—Near-infrared (NIR) & 0.7845–0.8995 & 10 \\ Band 8A—Vegetation Red Edge 4 & 0.855–0.875 & 20 \\ Band 9—Water Vapor & 0.935–0.955 & 60 \\ Band 10—Shortwave Infrared 1 (SWIR 1) & 1.36–1.39 & 60 \\ Band 11—Shortwave Infrared 2 (SWIR 2) & 1.565–1.655 & 20 \\ Band 12—Shortwave Infrared 3 (SWIR 3) & 2.1–2.28 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Payload parameter information of the Sentinel-2 satellites.
## 1 Introduction
### Building Envelopes in Jordan  
The building envelope acts as the interface between indoor and outdoor environments, crucially regulating heat flow from inside to outside in winter and vice versa in summer. Essential for achieving energy-efficient thermal performance, modifications to the building envelope adjust heating and cooling loads to meet internal thermal requirements. Components of the building envelope encompass external walls, roof, floor, external windows, and doors.  
Figure 1: Electrical energy consumption portions by sector in Jordan, 2021.  
The building envelope's heat transfer often results in substantial heat loss or gain, prompting scholars to focus on energy-saving technologies. These include optimizations in insulation materials [14], low-emittance windows [15], hollow windows [16], Trombe walls [17], thermally activated building systems [18], and enhancements to building codes [19]. While developed countries have adopted concepts like nearly zero-energy buildings, many developing nations, particularly in the MENA region, lack such standards, especially for hot and tropical climates [20]. In Jordan, building insulation codes are defined by the National Building Council under the National Building Law [21], ensuring specified requirements and regulations for construction activities, from permitting to surveillance phases.  
Among the common typical externally insulated wall structures in residential buildings in Jordan are those illustrated in Figure 2. Basically, walls consist of five main layers arranged from the outer layer (right side) as follows: hard stone, concrete, thermal insulation (mainly extruded polystyrene), hollow blocks, and cement plaster.  
According to the Jordan National Building Council, the maximum allowable walls' overall heat transfer coefficient (U-value) according to the codes is 0.57 W/m\({}^{2}\cdot\)K [20]. The overall heat transfer coefficient (U-value) is the rate of heat flow through a unit area of building envelope material or assembly, including its boundary films, per unit of temperature difference between the inside and outside air, and it is expressed in (W/m\({}^{2}\cdot\)K). Unfortunately, most of the external walls in Jordanian buildings are either uninsulated or poorly insulated, as shown in Figure 3a,b, which illustrates real examples of the most common constructed walls in residential buildings in Jordan [23].  
Figure 3: Real examples of constructed walls in residential buildings in Amman, Jordan. (**a**) Uninsulated wall; (**b**) poorly insulated wall; (**c**) fully insulated wall.  
Figure 2: Typical externally insulated wall construction design in Amman [22].
## 4 Results and Discussion
#### Impact of the Part Batches and Auxiliary Module Inter-Cell Travelling over the Pareto Points  
A relevant issue to explore is the impact of the part batches inter-cell flows on the total objective function value, showed in Figure 3.  
As expected, the part batches appear to travel more frequently in correspondence of the solutions characterized by good ergonomic performances. This happens because in correspondence of such Pareto points, the part batches move toward the RMT already  
Figure 3: Part batches trend over the Pareto points.  
Figure 2: Pareto frontier.  
equipped with the necessary auxiliary modules, rather than stay on the same RMT, changing its configuration.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 4 Field Experiment Results
### Comparison of the IoT Device and AWAC--Sea Levels
These could be caused by the different sampling rates; the IoT device measures for 40 s, and the average is taken for the sea-level measurement, while the AWAC ADCP takes the average over 10 min.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
###### Abstract  
Free space optical (FSO) communications have the potential to be one of the most essential technologies for solving the high-bandwidth demands of communications between satellites and ground stations. In this study, we examine the impact of the atmosphere on satellite-ground FSO uplink and downlink communications. To consider diverse atmospheric conditions on both uplink and downlink, we derive FSO channel elements such as the fog attenuation coefficient, refractive index parameter, coherence length, turbulence model, and angle-of-arrival fluctuation. Unlike conventional work, we provide FSO channel analysis based on variations in the Fried parameter, zenith angle, scintillation index, and Rytov variance. Using simulation results from the optical settings, we examine the influence on channel performance of conditions such as atmospheric attenuation and intensity fluctuation. Based on this examination, we determine that 1550 nm is the preferred wavelength for both uplink and downlink FSO channels to mitigate the impact of turbulence and that larger receiver apertures lessen angle-of-arrival changes.  
atmospheric turbulence; FSO communications; satellite-ground communications; ground-satellite communications +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions.  
_Appl. Sci._**2022**, 12, 10944. [[https://doi.org/10.3390/app122110944](https://doi.org/10.3390/app122110944)]([https://doi.org/10.3390/app122110944](https://doi.org/10.3390/app122110944))
# Antenna-on-Chip for Millimeter Wave Applications Using CMOS Process Technology
## 1 Introduction
Therefore, the use of the area needs to be considered when designing the antenna structure, so the antenna type must be carefully selected. For example, the patch antenna [8, 17, 18, 19] and Yagi-Uda antenna [22] have a high gain and high directivity, which can make up for the low gain of the antenna-on-chip in terms of performance. This article adopts the structure of the monopole patch antenna to optimize the defects of the chip through the characteristics of high gain and high directivity. The design in this paper adopts TSMC 0.18 \(\upmu\)m CMOS process technology, as shown in Figure 1a, which consists of six metal layers (Metal 1 to Metal 6 and the top metal layer) of six layers of silicon dioxide and an Si substrate. In the 0.18 \(\upmu\)m CMOS process, the top metal layer (Metal 6) is the designed antenna layer, with a thickness of 2.34 \(\upmu\)m, while the thickness of the Si substrate is 500 \(\upmu\)m. The antenna design architecture consists of a transmission line and a tapered slit monopole patch antenna and is designed on the top metal layer (Metal 6). The design of AMC on the M1 layer, as shown in Figure 1b, can reduce the loss of electromagnetic waves due to the silicon substrate so that the antenna-on-chip designed in this paper has a better gain and wider application bandwidth. Through the high-frequency electromagnetic simulation software performing full-wave simulation, the antenna-on-chip's reflection coefficient (S11) is below the \(-\)10 dB standard, covering the frequency band from 37.5 GHz to 69.5 GHz, and the fractional bandwidth is 60%. Among them, the bandwidth covers the 38 GHz and 60 GHz application frequency bands of millimeter waves. The antenna has a maximum gain value at 62.1 GHz, exhibiting \(-\)2.64 dBi, and was measured at the Taiwan Semiconductor Research Institute of the National Research Institute. The measurement proves that the designed antenna-on-chip has a wider bandwidth. The measured bandwidth is 20.9 to 67 GHz, and the fractional bandwidth is 104.89%. This bandwidth covers the following application frequencies of the millimeter wave: 28 GHz, 38 GHz, and 60 GHz.
## 4 Results  
### DEM Usage and Land Use Changes  
The initial step involves obtaining a DEM. In this study's instance, three DEMs were obtained from SRTM data (from 2009, 2013, and 2020) to coherently explain the erosion processes that occurred.  
Regarding land use changes (LUCs) in the Donana marshes (Figure 5), the primary land use characteristic in the study area at the beginning of 2016 was the predominance of natural and forested spaces, occupying 68% of the territory [47]. As the main changes in anthropic actions arose after 2016, this is considered a reference year. It is important to highlight that this area has traditionally lacked significant economic value because the locals focused on its utility value as a resource to increase income [48].  
Figure 4: Mean yearly ombrothermic diagram (2006–2020) of the study area. Source: own elaboration from [43].  
In addition, as a result of state initiatives [47], there has been an economic model shift through the promotion of large forested areas to meet the local population's needs. Therefore, the predominant land uses observed in this area are wooded formations, with or without scattered natural shrubland, comprising 30% of the total. Considering the previously mentioned limited economic value, the extensive open spaces with sparse vegetation or occupied by shrub or herbaceous associations (approximately a quarter of the total surface area) are worth noting. Completing the spatial distribution of land uses are the rainfed crops associated with the Mediterranean trilogy of olive, vine, and cereal, the wetland mass formed by the Donana salt marshes, and the prior flooding of the Rocina area adjacent to them. Finally, it is important to point out that, although irrigation does not exceed 2% of coverage, and given that the filling of the Donana marshes is mainly due to rain and aquifer contributions, it is essential to consider it a key factor in the global warming of the surrounding area [47].
# Numerical Study of Geotextile-Reinforced Flexible Pavement Overlying Low-Strength Subgrade
## 4 Conclusions  
A numerical study of geotextile reinforcement in unpaved low-volume flexible pavements under static loading was presented in this paper. Considering rutting as a major criterion, the study aimed to investigate improvement in the pavement due to geotextile reinforcement. The pavement sections were modeled and analyzed using Finite Element Analysis with the aid of ABAQUS software. The main conclusions drawn from the study are:  
Reinforcement location has significant influence on the effectiveness of geotextile inclusion in pavement. For single-layer reinforcement, placement of geotextiles at the base-subgrade interface was more effective in reducing permanent deformation and stress distribution. When the number of reinforcement layers is increased to two, i.e., at both the base-subgrade interface and AC-base interface, stress induced on the subgrade reduces further.  
There was a significant reduction in rutting of up to 25.2% for the unreinforced pavement system, which was attained with geotextile reinforcement at the base-subgrade and AC-base interfaces.  
The deflection response behavior of the pavement system is affected by the elastic modulus of the geosynthetic material. Geotextiles with higher elastic modulus values yield better reinforcement benefits.  
Figure 9: Magnitude of vertical displacement on top of the base and subgrade for different base course thicknesses.  
As a result of reinforcement, a base course thickness reduction of up to 30% was achieved without sacrificing the pavement's structural integrity.  
As an extension of FEA, more than two reinforcement layers and reassessed placement location for flexible pavements overlying low-strength subgrade can be considered. Additionally, the cost implication in the entire project cycle can be investigated. Finally, it is recommended that further investigation through use of pilot road sections and periodical performance assessment be carried out before the results become directly applicable to practitioners.  
Conceptualization, K.M.N.; Supervision, Z.Y.; Writing original draft, K.M.N., R.L.L. and C.K.K.; Writing review & editing, Z.Y. and C.K.K. All authors have read and agreed to the published version of the manuscript.  
This study was supported by The National Key Research and Development of China (Grant No. 2016 YFC0802203).  
Not applicable.  
The authors confirm that the data supporting the finding of this study are available within the article.  
The authors declare that there is no conflict of interest regarding this submission.
## 2 Materials and Methods
### Definition of LCA+C2C Endpoint Indicators  
Once the similarities between the categories of both methodologies were identified, the related relationships were classified and quantified to create the LCA+C2C endpoint indicators. Relationships were evaluated at two levels: (1) direct, if there was direct dependence between the input (C2C) and output (LCA) indicators, considering a direct dependency relationship to be when both methodologies evaluate the same aspect, although these identify the criteria with different names; and (2) indirect, if there was indirect dependence between the input (C2C) and output (LCA) indicators, considering indirect dependence to be when the evaluated aspects present common characteristics, but the indicator is not coincident.