esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Distributed Measurement Method for Array Antenna Position
## 4 Conclusions  
In the process of aircraft flying, the aircraft wing vibration directly affects the measurement accuracy of the Earth observation system. In order to provide a high-precision measurement method, a method of measuring the phase center of the array antenna based on FBG sensing technology and distributed POS technology combination is designed. The method combines distributed POS technology, FBG sensor technology, and finite element analysis. In order to verify the feasibility of the method, a verification experimental device installed on the belly of an aircraft is designed. Firstly, the system error is analyzed. Then, through a strength check, it is proved that the strength of the experimental structure meets the requirements. The test of the U-shaped flight path proves that the device can meet the requirements of the flight experiment. Therefore, the design method in the paper can meet the flight conditions in terms of structural strength. The measurement method proposed in the paper provides a reference for improving the Earth observation performance of SAR and lays a foundation for further verifying the effect of assistant transfer alignment of the FBG sensors. In the design of the paper, only the measurement method under a constant temperature environment is considered. The temperature changes greatly in the flight process, which will directly affect the measurement accuracy of the FBG sensor. So it is necessary to study the temperature compensation of the FBG sensor in a flight condition in the future.  
Figure 11: Flight path.  
Figure 10: Cloud chart of plate element calculation stress in the case of lateral gust.  
**Author Contributions:** Z.M.; Conceptualization, methodology, formal analysis, writing--original draft preparation, X.C.; Conceptualization, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 61873064 and No. 51375087) and the Scientific Research Foundation of Graduate School of Southeast University (No. YBPY1982).  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (No. 61873064 and No. 51375087) and the Scientific Research Foundation of Graduate School of Southeast University (No. YBPY1982).  
**Conflicts of Interest:** The author declares no conflict of interest.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 3 Proposed Method
### Efficient Competition Learning Particle Update Strategy
#### 3.1.5 Procedure of ECLUS  
In summary, the procedure of ECLUS is shown in Algorithm 1. The main steps include the following: First, number the Pareto layers and calculate the SDE of each individual in the current population. Next, group the population in pairs and select the winner and the loser from each pair. Then, update the loser according to the proposed ECLUS. Last, combine the updated loser and the winner to form a new population.  
```
Input: Current population \(P\) (even individual) Output: New population \(P^{\prime}\)
1 Obtain the Pareto number of each individual by non-dominated sorting \(P\);
2 Calculate the SDE of each individual in each Pareto layer according to (24);
3 Obtain competitive particle pairs by grouping individuals in pairs within \(P\);
4 Select the winner and the loser from each pair based on the Pareto number and SDE;
5 Update the real and binary decision variables for all losers based on (14), (19), (21), (22), and (23);
6 Generate the new population \(P^{\prime}\) by combining updated losers and winners;
7 return\(P^{\prime}\);
```  
**Algorithm 1**Procedure of ECLUS
### Remotely Sensing DOC  
One of the most challenging aspects of developing robust, global ocean color algorithms for DOC is that the relationship between DOC and CDOM (i.e., the DOC-specific absorption) is highly variable, in some cases negatively correlated (e.g., Southern Ocean, [46]) and often poorly defined, particularly in open ocean areas such as the Sargasso Sea [47; 48]. In some cases, the relationship is better constrained within a particular region and season, as shown by measurements made in the Mid-Atlantic Bight on the eastern shelf of North America [28]. Because absorption by CDOM is the only way in which ocean color is impacted by DOC, some other independent knowledge of water type is needed for retrieval of DOC from space.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication
### Advantages of mmWave Communication
#### 2.1.2 Inherent Security  
The 60 GHz communication is sensitive to blockage due to weak diffraction capability around the objects in an industrial environment [67]. Moreover, because of beamforming, the nature of communication is highly directional. Even a small misalignment between the receiver and transmitter can lead to communication deafness. However, this aspect can be positively used as a security enabler. This also helps in avoiding jamming signals due to the directionality of the transmitter and receiver antenna systems. The problem of jamming detection and mitigation in 5G-enabled Industry 4.0 indoor factory deployments is presented in [68].
## 3 Results
### Connectivity Analysis  
Connectivity increased at global and local scales (Tables 5 and S2, Figures 6 and 7). Overall connectivity increased to 32.62% in the flux metric, 2.51% in equivalent probability, and 3.15% in connectivity probability, indicating that connectivity in Ecuador's mangrove forests increased between 1996 and 2020. However, the number of components increased, indicating that zones without connectivity appeared during the study period.  
Figure 6: Connectivity of Ecuadorian mangrove forests per patch in the years 1996 and 2020. The size of the circle indicates the size of the patch and the color its connectivity. (**A**) Galapagos. (**B**) Guayas and the El Oro region. (**C**) North of Manabi and Esmeraldas.  
Figure 7: Average connectivity of mangrove forests per patch in the years 1996 and 2020 by province.
# The Definition of Perennial Streams Based on a Wet Channel Network Extracted from LiDAR Data
## 3 Results and Discussion  
### The Relationship between the Wet Channel Length and Streamflow  
In this study, we applied [PERSON] et al. [28]'s method to all of the study watersheds using LiDAR-based DEM and intensity data in order to extract valley networks and identify wet channels. The wet channel length increases with the streamflow and has been described by an empirical power-law function based on field observations. This has been proven to be effective in quantifying this relationship, as reported by [PERSON] and [PERSON] [37], [PERSON] [38], and [PERSON] and [PERSON] [39]. [PERSON] et al. [28] also corroborated that the power-law relationship holds when using wet channels identified by integrating LiDAR intensity and elevation data. The scaling exponent of the power-law relationship can be unique to the season (e.g., wet or dry) [37; 40] and the position in the hydrograph (e.g., rising or recession limbs) [41]. This is due to the dependence of the wet channel network on the local watershed properties such as precipitation, soil, vegetation, and topography [42; 43; 44; 45].  
The relationship between wet channel length (\(L_{W}\)) and streamflow (\(Q\)) for the study watersheds is plotted in Figure 5, and the total valley length, wet channel length, and wet channel ratio are listed in Supplementary Table S1, organized in the climate aridity index (\(E_{P}/P\)) from the smallest to the largest. Three watersheds were excluded from the data as they were excessively humid (\(E_{P}/P\): 0.3-0.4) and arid (\(E_{P}/P\): 2.7), and they displayed uncharacteristic patterns of wet channel length and streamflow patterns. The range of \(E_{P}/P\) was 0.8 to 2.1 for the remaining 27 watersheds with 29 LiDAR snapshots, and the best fit function was \(L_{W}=161.22Q^{0.623}\) with \(R^{2}=0.74\), as shown in Figure 5. The scaling exponent of the power-law relationship (i.e., 0.623) was within the range of the reported values (i.e., 0.042-0.688) in the literature from fieldwork conducted in 14 watersheds in other regions [38].
## 3 Proposed Method  
Let \(x\in\mathbb{R}^{W\times H\times C}\) and \(y\) represent a training image and its label, respectively. The main idea of RSMDA is to create a new training image with its label \((\hat{x},\hat{y})\). For that purpose, we select two training samples with corresponding labels, \((x_{1},y_{1})\) and \((x_{2},y_{2})\). A combination of these training samples can be defined as:  
\[\hat{x}=M\odot x_{1}+(1-M)\odot x_{2} \tag{1}\]  
Additionally, their labels' combination is defined as:  
\[\hat{y}=\lambda\odot y_{1}+(1-\lambda)\odot y_{2} \tag{2}\]  
where \(M\in[0,1]^{W\times H}\) is a binary mask that is filled with 0 and 1, where 0 and 1 are for excluding and including the image pixel, respectively. The symbol \(\odot\) shows element-wise multiplication, and \(\lambda\) is the combination ratio of the two images and their labels, which is sampled from a beta distribution, such as CutMix [36] or MixUp [35]. In \(Beta(\alpha,\alpha)\), we set alpha to 1, following previous data augmentation, and \(\lambda\) is distributed technically from the normal distribution.  
For sampling the binary mask \(M\), we randomly obtain slices of the size \(S\) in a certain range. To obtain the total number of slices, we divide \(W\) or \(H\) by \(S\).  
In the case of the total number of column slices:  
\[TotalSlices=\lfloor W/S\rfloor \tag{3}\]  
In the case of the total number of row slices:  
\[TotalSlices=\lfloor H/S\rfloor \tag{4}\]  
The next step is to ascertain how many slices should be mixed. To obtain this, we multiply the total slices by \(\lambda\), which gives the number of slices to be mixed.  
\[N_{mix}=\lfloor\lambda\times TotalSlices\rfloor \tag{5}\]  
In Equation (5), \(N_{mix}\) is the number of slices to be selected from the target sample and pasted to the source image. To do so, we fill \(N_{mix}\) number of slices in mask \(M\) with 1 to select the slices from the target image. In order to generate an augmented sample pair \((\hat{x},\hat{y})\), the selected slices from the target image are pasted to a source image. Then, the \((\hat{x},\hat{y})\) augmented pair is used for training the model.  
Furthermore, we propose and investigate the three different strategies of the proposed technique. Each of them is discussed below:  
* **Random slices mixing row-wise (RSMDA-R)**: In this strategy, we obtain \(N_{mix}\) number of slices horizontally from the target image and paste it to the source image. Their corresponding labels are also mixed following the whole process discussed in Section 3. RSMDA-R is shown in Figure 2a.
* **Random slices mixing column-wise (RSMDA-C)**: This is another strategy that we explore, in which we follow the same method used in RSMDA-R, except that we obtain the slices vertically, as shown in Figure 2b.  
* **Random slices mixing row-column-wise (RSMDA-RC)**: This is the third strategy. We apply both RSMDA-R and RSMDA-C based on binary randomness to each learning step, as shown in Figure 2c.
## 4 Results and Discussion
### Interpretability of CNN Models  
CNNs are designed to automatically extract hierarchical features from images, progressively learning visual patterns that contribute to classification decisions. Each convolutional layer refines its understanding, from detecting edges and shapes in early layers to recognizing more complex spatial structures in deeper layers. In this study, the CNN architectures (AlexNet, VGG-19, and ResNet-50) were trained on a labeled dataset manually annotated based on Michigan UD-10 crash reports. Each pedestrian crash diagram in the dataset was reviewed and labeled for six key attributes, ensuring the models were trained on well-defined and structured data.  
During training, the CNN models learned to differentiate intersection types (three-leg vs. four-leg) by identifying road connectivity and geometric structures, focusing on the number and orientation of intersection legs. The classification of roadway type (divided vs. undivided) was based on the recognition of visual indicators such as median barriers and lane separations. The presence of marked crosswalks was classified based on high-contrast linear features typically found at pedestrian crossings. Similarly, intersection skewness was detected by analyzing angular deviations in roadway alignments.  
The models distinguished U-turn designs for Michigan left turn classification and offset left-turn lanes by recognizing specific road curvature and lane placement patterns. The classification of adjacent residential areas relied on identifying contextual features surrounding the crash location, such as street layouts, smaller road networks, or the absence of sizeable arterial infrastructure. These learned representations indicate that CNNs successfully capture meaningful spatial patterns that align with domain knowledge in traffic safety research.  
Although this study does not include direct feature visualization, the classification results demonstrate that CNN models can reliably extract and classify key attributes from pedestrian crash diagrams. Future research could incorporate explainability techniques such as feature attribution methods or visualization tools to analyze the key regions influencing CNN predictions further. This addition would enhance transparency and contribute to the broader adoption of AI-driven methods in pedestrian safety research.
## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Testing Procedures  
#### 4.3.1 Illumination Setup  
Two different tests were used to evaluate the illumination setup. The first one measured the influence of using the grazing light to illuminate the surface of the cork stopper. The procedure followed in the first test (from now on named illumination setup--lighting test) is described as follows:  
1. Randomly select 20 cork stoppers from the database (10 cork stoppers from each type of cork: agglomerated and natural);
2. Capture four images using the same smartphone camera per cork: two images using with the light turned on, and two images with the light turned off;
3. Match the images with and without light using RIOTA's implementation;
4. Store the matching results for further analysis.  
The second test, (hereafter called illumination setup--repeatable features test) measures the success of capturing repeatable features using different camera sensors. For this test, only 10% of the \"best\" detected keypoints, from the original version of RIOTA's implementation, were used to match two images of cork stoppers. Moreover, RIOTA's implementation was tuned for this test (details about the adjustments are further explained). The test procedure used is exhibited as follows:  
1. Randomly select 10 cork stoppers from the database (5 cork stoppers from each type of cork: agglomerated and natural);
2. Capture six images per cork using different cameras: rear camera of Sony Z3 Compact, USB camera Logitech C930, rear camera of Samsung Galaxy Note8, USB camera PlayStation Eye and Asus Zenfone 5 A500 CG;
3. Match the images using the tuned version of RIOTA's implementation;
4. Store the matching results for further analysis.  
The selection of the \"best\" keypoints was achieved by sorting the keypoints in descending order, according to its response, and using the first 10% best keypoints. The keypoints response was calculated using Harris corner measure [42]. The adjustments of the RIOTA's implementation include the tuning of the RANSAC re-projection error, which was tuned to 1 pixel to ensure that the keypoints matched between two images correctly.
## 1 Introduction
### Geology and Hydrogeology
#### Hydrogeology  
The hydrostratigraphyy of the study area has been explained by several researchers [31; 37; 38; 39; 40; 41; 37; 42]. Depth to the aquifer units varies across the basin. The depth ranges from 5-23 m for the primary unconfined aquifer while depth to three confined aquifers are in the range of 7-80 m, 63-188 m, and 245-261 m, respectively (Figure 3). The respective thicknesses of these aquifers range from 7-26 m, 6-67 m, 20-143 m, and 61-117 m. The description of these aquifers is presented in stratigraphical sections along profiles AB and CD, as shown in Figure 3. The aquifers are bounded by intercalation of shale, tar sands, and layers of sandy clay with relatively low hydraulic conductivity. Two main aquifer units were identified within the Upper Coal Measures. The depths to the top/thicknesses of the aquifer units are 9.8 m (1.7 m) and 23 m (5.3 m), respectively. The Nkporo Shale had a thickness and depth to the top of the only identified aquifer unit as 10 m and 16 m, respectively [39]. At the northern parts of the study area, the sedimentary rocks of the Abeokuta formation thin-out on the Precambrian basement rocks. Most of the wells and boreholes in this area are drilled through the upper sandy layers into the weathered profile of the Precambrian basement rocks tapping water through weathered overburden, and sometimes fractured basement rocks, depending on locations and the weathering status. The regional groundwater flow direction across the basin is north-south as shown in Figure 3. Though, local groundwater flow direction is north-east and east-west as it recharges the tributaries to major rivers (River Ogun, Oluwa, and Ose) as baseflow. These rivers flow approximately southward and ended up in the ocean (Figure 2).  
Figure 2: Map of the study area showing sampling points, contour line, rivers and geology.
## 4 Data and Methods
### Statistical Quality Assessment  
In order to provide a reliable and robust statistical evaluation of the products of the cloud removal, it is mandatory to apply meaningful and effective statistical measures. [PERSON] and [PERSON] [36] propose a general framework for a quality assessment of a fused image product. [PERSON] and [PERSON] [37] propose a categorization of statistical distances to measure the quality of a fused image in comparison to a reference. This categorization enables the tackling of every aspect of image comparison. On the other hand, redundancies amongst the measures can be avoided. Since the categorization is developed mainly for image fusion methods for the sake of an enhancement of the spatial resolution, the evaluation framework is slightly adopted in the framework of this study. The selection of the statistical measurements is based upon the recommendations of [PERSON] and [PERSON] [36], [PERSON] _et al._[38] and the findings of [PERSON] _et al._[39].  
Figure 4: The Closest Feature Vector (CFV) is determined for each cloudy pixel \(C_{x,y}\) by returning its geolocation to a cloud-free fill image. The Euclidean Distance (\(ED\)) for each image band \(k\) is calculated for the Pixel \(F_{x,y}\). The Pixel \(F_{i,j}\) representing the most similar properties to \(F_{x,y}\) is referred to as CFV. The geolocation of this CFV is returned in order to obtain the DNs of the Pixel \(C_{i,j}\). These DNs are used to substitute the cloudy pixel \(C_{x,y}\). (after [PERSON] _et al._[35]).  
Spectral Fidelity  
A measure for the spectral differences between the images is given by the Spectral Angle Mapper (SAM). The angle \(\alpha\) is calculated on the \(k\)-dimensional vectors of the spectra of a pixel \(O_{ij}\) and \(F_{ij}\)[40]. SAM is defined as:  
\[SAM=\cos^{-}1\left(\frac{\overrightarrow{O_{ij}}\cdot\overrightarrow{F_{ij}} }{\|\overrightarrow{O_{ij}}\|\cdot\|\overrightarrow{F_{ij}}\|}\right) \tag{9}\]  
where \(\overrightarrow{O_{ij}}\) and \(\overrightarrow{F_{ij}}\) are representing the vectors in the \(k\)-dimensional multi-spectral feature space for each pixel at the location (\(i,j\)). The pixel-wise computation of SAM is averaged for the whole image. A small SAM indicates a high spectral similarity between the filled image and the original image. The ideal value is therefore zero [40, 41, 42].
## 3 Results and Discussion
### Historical Storm Surge Event, January 2019
#### 3.1.3 Correlation Between Sea Level, Wind Parameters and Storm Surge in the Lagoon  
The time series plot in Figure 3 shows fluctuations in water levels at the Gulf of Gdarsk (sea level), Nowakowo and Tolkmicko (southwest lagoon level) gauging stations (Figure 1) throughout January 2019.  
The analysis of water level variations in the Gulf of Gdarsk and the southwestern Vistula Lagoon (stations: Nowakowo and Tolkmicko) reveals a strong correlation between sea-level fluctuations and water levels in the lagoon. The Pearson correlation coefficient between the Gulf water level and that of Nowakowo and Tolkmicko reached 0.77, indicating that water level changes in the Gulf significantly influenced the lagoon. A lagged correlation analysis showed that the highest correlation (0.95 for Tolkmicko and 0.94 for Nowakowo) occurred with a 6 h delay, confirming that the storm surge propagated from the Gulf into the lagoon over time. The maximum recorded water levels during the event were 1.11 m in the Gulf, 1.57 m in Nowakowo, and 1.36 m in Tolkmicko, demonstrating the amplifying effect of hydrodynamic interactions within the confined lagoon.  
The delayed response of water level fluctuations is also a characteristic feature of many Mediterranean coastal Lagoons and has been extensively analyzed using tidal propagation models. In these systems, lagoon hydrodynamics, water exchange efficiency, and water level variability are significantly influenced by shallow bathymetry, bottom friction, and the morpho-geometric attributes of tidal inlets. These factors collectively modulate the amplitude and phase of the tidal signal during its propagation from offshore waters--typically from the adjacent gulf--into the confined lagoon environment [43; 44].  
Wind conditions (Figure 4) played a crucial role in the storm surge. The average wind speed during the analyzed period was 9.41 ms\({}^{-1}\), with a peak of 18.0 ms\({}^{-1}\). The dominant wind direction was variable, but during surge events, it was predominantly from the northeast (NE), which is critical for water accumulation in the southwestern part of the lagoon and plays a role in driving water masses towards the Polish coast. The correlation  
Figure 3: Water stage elevation measured by IMGW-PIB at gauging stations.  
between wind speed and water levels was 0.50 for Nowakowo and 0.57 for Tolknicko, indicating that stronger winds contributed significantly to water level rise.  
This analysis confirms that the storm surge in January 2019 resulted from a combination of external sea-level rise, wind-driven transport, and lagoon hydrodynamics, with a peak response occurring several hours after the initial sea-level rise in the Gulf. The storm parameters, presented in the analysis, were used to perform and validate the numerical simulation of lagoon hydrodynamics during the January 2019 storm event.
# Measurement of Contrast and Spatial Resolution for the Photothermal Imaging Method
###### Abstract  
Analyzing the quality of images generated from an imaging method is essential for determining the limits and applicability of that method. This study analyzed the quality of images resulting from a photothermal imaging method by applying the line spread function and the modulation transfer function to the spatial resolution and contrast, on the basis of certain parameters of the photothermal imaging method for a copper-resin double-layered structure. The parameters are the ratio of the first-layer thickness to the thermal diffusion length (\(L_{f}/L_{d}\)) and the ratio of the pump-beam radius to the thermal diffusion length (\(R_{b}/L_{d}\)). The phase delay profile (edge response function, ERF) of the subsurface structure derived from the photothermal imaging method becomes dimensionless upon division by the thermal diffusion length; as the ratio \(L_{f}/L_{d}\) increases, the spatial resolution and contrast increase.  
photothermal imaging; image quality; contrast; spatial resolution; MTF; ERF; LSF Article