esa-sceva/satcom-chunk-collection huggingface dataset:
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology
### Ray-Tracing
#### 2.2.1 Field Computation  
The contribution to the electric field of a ray impinging a given receiver in air is calculated according to Equation (1) or Equation (2), depending on whether the ray comes from the reflection at the soil interface or from the target. In these equations \(E_{\text{inc}}\) is the incident field amplitude; \(A_{t}\) and \(A_{r}\) are used to take into account the transmitter and receiver antenna beamwidth; \(G_{\text{in}}\) and \(G_{\text{out}}\) are the in-plane and out-of-plane geometrical spreading factors [10]; \(\Gamma\) and \(\tau\) denote the reflection and transmission coefficients; \(R_{m}\), \(a_{m}\) and \(\beta_{m}\) are the total ray path-length and the attenuation and phase constants in medium \(m\) (where \(m=0\) is air and \(m=s\) is soil). The ray-tracing method implemented in this contribution calculates the path length in each medium (\(R_{m}\)), which is then multiplied by the propagation constants so as to perform a multifrequency simulation.  
\[E_{\text{soil}}=\frac{E_{\text{inc}}A_{t}A_{r}}{G_{\text{in}}G_{\text{out}}} \Gamma_{\text{air-soil}}\exp(-j\beta_{0}R_{0}) \tag{1}\]  
\[E_{\text{target}}=\frac{E_{\text{inc}}A_{t}A_{r}}{G_{\text{in}}G_{\text{out}}} \Gamma_{\text{air-soil}}\Gamma_{\text{soil-target}}\tau_{\text{soil-air}} \exp(-\alpha_{s}R_{s})\exp(-j(\beta_{0}R_{0}+\beta_{s}R_{s})) \tag{2}\]  
For these ray-tracing simulations, each transmitter and receiver is characterized by its angle of incidence with respect to the soil (\(\theta_{it}\) and \(\theta_{it}^{t}\), respectively) and by its 3-dB beamwidth. The terms \(A_{t}\) and \(A_{r}\) model the antenna assuming a \(cos^{ql}\) pattern (where \(q\) is calculated according to the antenna beamwidth).  
Figure 2: 2D GPR modeling scenario.  
Assuming a moderately lossy soil, multiple reflections are not considered and the reflection and transmission angles are calculated using [PERSON]'s law without taking into account the conductivity of the soil. These angles are then used to compute the reflection and transmission coefficients (\(\Gamma\) and \(\tau\)), which do incorporate the conductivity of the soil.
## 4 Results and Discussions
### Analogy Using Confusion Matrices  
A confusion matrix, also called an error matrix, represents series of statistics. It helps to envisage the computation for the data to be classified and displayed in the form of matrices. Rows of the matrix demonstrate the prophesied class set as long as columns stand for the real class. The classifier predicts datasets' occurrence in terms of positive or negative, as it generates four outputs--false positives, false negatives, true positives, and true negatives. In confusion matrices, diagonally placed digits portray a precise range of the predicted class based on the model classifier.  
Here it can be seen that in confusion matrices, the damage level has been categorized in 5 damage labels that is, from D1 to D5. The size of the matrix is specified by the number of classes that is, each matrix with [\(5\times 5\)] dimensions. Where the X-axis shows the predicted damage label, and Y-axis shows the actual damage. Numerals placed in the confusion matrix signify the damage intensity from 0 to 5 in accordance with EMS 98, shown in Table 14.  
Figures 6-10 present the confusion matrix of each applied MCDM method in this study. As can be seen, the AHP method had the worst classification while WPM and WSM methods had better damage classifications and distribution of classes. Table 16 demonstrates a better representation of confusion matrices by revealing the accuracy of each method.  
Accuracy is determined with the number of correct predictions among all provided data. In confusion matrices, the overall accuracy depends on the dataset's consistency, where the number of observations differs according to classes. Therefore, the higher numbers lie in diagonal series show better accuracy of the matrix. Figure 11 shows the percentage of damage distribution in each group between observed actual damages and assessments by different methods. Amid all MCDM methods, the highest accuracy achieved with the WSM and WPM followed by TOPSIS approach.  
Figure 8: Confusion matrix for TOPSIS.  
Figure 10: Confusion matrix for Weighted Product Method (WPM).  
Figure 9: Confusion matrix for Weighted Sum Method (WSM).
# Development of a Multi-Sensor GNSS-IoT System for Precise Water Surface Elevation Measurement
## 2 Methodology
### Hardware Device
#### 2.1.1 Ultrasonic Sensor  
The ultrasonic sensor provides a downward distance measurement between the sensor to the nearest surface. The ultrasonic sensor distance measurements are useful for understanding the compression mechanism of land subsidence areas or the platform situation of instrument installation by measuring distance offsets to the surface of interest. In this study, the device is deployed on a floating platform, vertically above the water surface so that the ultrasonic sensor can be used to measure variation in the antenna height above water and platform draft variations. Figure 2 demonstrates an example of the ultrasonic sensor and ambient temperature data at a fixed station over two weeks in Australian autumn (month of April). The ultrasonic sensor collected measurements every 30 min, continuously over 24 h cycles. It is clearly seen that diurnal temperature variations caused the ultrasonic sensor to fluctuate periodically. Figure 2c illustrates that the ultrasonic sensor's epoch reading can fluctuate by up to 40 mm during a 35 \({}^{\circ}\)C change in temperature, while Figure 2d presents the average daily distance data change of 8 mm corresponding to a 13 \({}^{\circ}\)C temperature variation. Therefore, without proper temperature profiling along the wave path, the error in the ultrasonic measurements can be up to 40 mm. The errors of temperature impact are not identical for individual days, but 40 mm accounted for most scenarios of this device during the observation period.  
Figure 1: Hardware device with main components labelled.  
Figure 2: The fixed station ultrasonic sensor measurement and ambient temperature example. (**a**) Ultrasonic sensor epoch readings every 30 min. (**b**) Ambient temperature recordings every 30 min. (**c**) A scatter plot of ultrasonic sensor and temperature epoch measurements. (**d**) A scatter plot of ultrasonic sensor and temperature average daily measurements.  
In addition, since the ultrasonic sensor reference point is not aligned to the device's Bottom of Dome Ground plane (BDG), the offset value needs to be determined. Table 1 shows four tests that we conducted with different sensor-to-surface distances and a constant indoor temperature of 24 degrees. From these tests, the BDG to ultrasonic sensor offset was determined to be 0.011 m and is applied via subtraction from the ultrasonic sensor reading measurement.
# TwSense: Highly Robust Through-the-Wall Human Detection Method Based on COTS Wi-Fi Device
## 5 Results and Evaluation
### Analysis of Experimental Results
#### 5.2.3 Influence on Different Distances of Equipment  
In order to verify that the device spacing can have an impact on the final experimental results, we set the distance between the transmitter and receiver between 1 m and 5.5 m, with an interval of 0.5 m for each distance, and verified it by using walls of different materials. The experimental results are shown in Figure 12. In the concrete wall experimental scenario, when the device spacing was about 1.5, the highest recognition accuracy reached 94.7%; when the device spacing was about 2.5 m, the action recognition rate dropped to 83.3%. When the device spacing was 5.5 m, the accuracy was the lowest, reaching 54%. Similarly, in the two experimental scenarios of plaster wall and door, the recognition accuracy was highest when the device spacing was 1.5 m, both at 95% and above. In the glass wall experimental scenario, the effect of device spacing on the experimental results was more minor compared to other materials of the wall. Recognition accuracy was the highest. Thus, if we want to obtain the best experimental effect, the equipment spacing should be set at 1.5 m, at which time the router is 1 m away from the wall, which can realize the effect of high-precision human detection. The final results of the experiment showed that the recognition accuracy of human detection through concrete and plaster walls was lower than that of the two materials of glass walls and doors. The main reason is that the signal attenuation of glass walls and doors is smaller than that of walls. With the increase of the equipment spacing, the recognition accuracy decreased, the accuracy of the equipment spacing outside the 5.5 m was less than 50%, and the detection effect became worse and worse. When the equipment spacing was within 3.5 m, the probability of action recognition was maintained at 80% and above, indicating that the system had a better performance. The results also showed that the size of the equipment spacing affected the accuracy of the detection, but a smaller spacing of equipment was not better. The results also showed that the size of the equipment spacing affected the detection accuracy, but a more minor equipment spacing was not better. We need to find the most suitable distance. At this time, the quality of the collected data and the final classification effect was also the best.  
Figure 11: Cumulative distribution functions of various materials with different thicknesses: (**a**) CDF of the concrete wall; (**b**) CDF of the gypsum wall; (**c**) CDF of the glass wall.
# Effective Fabric Defect Detection Model for High-Resolution Images
## 1 Introduction
Furthermore, we proposed a block recognition algorithm as well as a detection box merging algorithm to detect small defects in high-resolution images. Subsequently, we employed a multi-morphology data augmentation method to improve the detection precision in the case of a small number of defects. Moreover, Switchable Atrous Convolution (SAC) layers were used to replace 3 \(\times\) 3 convolutional layers in Stage 2 of ResNet-50 to enhance the feature extraction ability of the backbone network for multiscale targets. In addition, considering the importance of the Feature Pyramid Network (FPN) module in the detection results of multiscale targets, we optimized its up-sampling algorithm and network structure to improve the detection precision for small targets. The remainder of this paper is organized as follows. In Section 2, we introduce the proposed method. In Section 3, the experimental results are discussed. Finally, Section 4 summarizes this study and provides suggestions for further improvement.
# Spatial-Temporal Variability of Future Rainfall Erosivity and Its Impact on Soil Loss Risk in Kenya
## 3 Results  
### Estimated Baseline Soil Erosion Rates in Kenya  
The baseline and observed precipitation were statistically compared to evaluate the quality of the simulation produced by the WorldClim data (Figure 7). A correlation coefficient of approximately 0.91 was observed, indicating the strong relationship between these two variables. The mean annual \(R\) factor generated by observed rainfall data varied from 267-31,667 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\), with an average of 1714.31 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\). In comparison, the WorldClim data presented an \(R\) factor (baseline) with an average of 1665.9 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) (Figure 8a). The observed and baseline \(R\) factors showed 0.93, 273 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\) and 0.74 for \(R^{2}\), \(RMSE\) and \(NSE\), respectively, demonstrating a satisfactory model performance. Highland regions with high mean annual rainfall presented \(R\) factor values \(>\)5000 MJ mm ha\({}^{-1}\) h\({}^{-1}\) yr\({}^{-1}\).  
The Soil erodibility Factor (\(K\)) had a mean value of 0.019 t ha h ha\({}^{-1}\) MJ\({}^{-1}\) mm\({}^{-1}\) and ranged from 0.014 to 0.028 t ha h ha\({}^{-1}\) MJ\({}^{-1}\) mm\({}^{-1}\) (Figure 8b), while the cover management Factor (C) had a mean value of 0.089 (Figure 8d). The northern parts of Kenya with bare and arid surfaces recorded the highest \(C\) values. Almost 78% of the country had an \(LS\) factor between 0 and 10, while 4% had values of more than 10 (even though steep gradients may not necessarily show high erosion susceptibility [50]). The mean baseline soil erosion rate was observed to be 4.76 t ha\({}^{-1}\) yr\({}^{-1}\) which had a spatial distribution that varied equally with that of the annual precipitation of the country (Figure 9). The baseline erosivity density \(ED\) had an average value of 2.77 MJ ha\({}^{-1}\) h\({}^{-1}\) and a standard deviation 1.06 MJ ha\({}^{-1}\) h\({}^{-1}\) (Figure 9b).  
Figure 7: Model validation: (**a**) Taylor diagram for precipitation data; (**b**) Scatter plot between observed and baseline rainfall erosivity.  
Figure 8: RUSLE parameters applied for soil erosion estimation in Kenya: (**a**) Rainfall-runoff erosivity; (**b**) Soil erodibility; (**c**) Terrain factor; (**d**) Vegetation cover factor.
## 5 Conclusions  
In previous studies, when the DP algorithm was used to obtain the optimal speed profile, given the distance between stations and the target travel time, the cost function  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Weights**} & \multirow{2}{*}{**Travel Time (Single)**} & \multirow{2}{*}{**Travel Time (Multi)**} & \multirow{2}{*}{**Energy (Single)**} & \multirow{2}{*}{**Energy (Multi)**} \\  & & **(s)** & & **(kwh)** & **(kwh)** \\ \hline
0 & 75.56 & 75.56 & 59,129.12 & 59,129.12 \\
0.146262 & 100.88 & 100.88 & 33,955.46 & 33,955.46 \\
0.955694 & 166.05 & 166.05 & 26,232.48 & 26,232.48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of single-weight and multi-weight calculation results.  
Figure 5: Results of weight versus time.  
Figure 6: Comparison of multi-weighted and single-weighted DP results.  
was set according to both time and energy. Further, the weight was changed to find the optimal solution that satisfies the target travel time. These calculations were repeated until the target travel time was satisfied. This study proposed a method to reduce the time required to find the optimal solution by shortening the process of repeating weight changes and calculations.  
This paper presents the DP algorithm that divides the weights applied to time and energy into several cost functions and arranges them to shorten the time required to obtain the desired solution.  
The computation time according to the number of weights applied and the time required to obtain the optimal solution through multi-weight processing were compared with the time required for the approach, wherein single weights are repeatedly changed and calculated. This indicated that multi-weighted computational processing may reduce the time required to find a solution more effectively compared to single-weighted iterative processing. In addition, by processing multiple weights at once and checking the results, it was possible to simultaneously confirm the optimal minimum and maximum travel times and energy required for the relevant section. This result can be used to change and establish an effective train operation plan. In addition, the results of this study can be used to minimize the energy consumption for train operation and maximize the energy efficiency.  
[PERSON] developed the theoretical basis to apply dynamic programming, carried out the numerical simulation for the implementation of the algorithm, and made important contributions to the discussion of the manuscript. [PERSON] supervised the entire work and provided an understanding of the electric train's power system in general. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research and Development Program through the Development of Autonomous Train Control Core Technology by the Korea Railroad Research Institute, under grant number PK2201B1.  
The authors declare no conflict of interest.
## 4 Discussion  
The advantage of the proposed implementation of a long-wave infrared camera is the ability to monitor the technical condition of the aircraft airframe not only by inspecting the surface but also the structure of the airframe composite materials. In this way, it is not only possible to monitor the airframe but also damage to the propeller and engine components. The attachment of an IR camera to the UAV allows autonomous control based on a predefined flight pattern. In addition, if necessary, the UAV can be operated manually and the critical point inspected repeatedly if necessary. Moreover, the benefits include the option to choose a thermal camera depending on the monitoring goal. A suitable variant of the IR camera can monitor the electrical elements of the aircraft and determine the temperatures of the electrical circuits based on the radiated thermal radiation. Therefore, it would be possible to determine whether there is overheating at any point and thus avoid failure. The question is whether such a camera would be able to monitor temperature changes of smaller electrical elements. The aircraft accessories such as aircraft instruments and avionics operate at lower voltages; thus, it is questionable whether the proposed variant would be able to determine temperature differences and accurately determine any failure.  
The crucial part of the monitoring is the measurement conditions. The power line must be overload permanently while inspecting with an emphasis on not exceeding the limits of fuses. This process must be carried out in order to achieve maximum values where the damage is not presented yet. After the operator is familiar with the threshold temperature values, it is possible to detect failures. Here comes to mind the question of whether these overloading actions may not cause the failure itself.  
Other risks include the unsolicited return to a home feature, which turns automatically when the drone battery reaches low values. Such a feature could endanger the aircraft itself if it is not done correctly. A suggested type of drone could fly in the windless conditions for about 31 min, depending on how many of its accessories are turned on, e.g., speaker, spotlight, beacon. These additional devices affect the battery, thus also the operation time.  
With the help of RFID technology and the installation of CMB equipment in critical places in composite structures, it provides an overview of previous maintenance performed. Regardless of this benefit, in our conditions, the use of CMB does not find its application; rather, we focused on the RAIN RFID tags, which could be read wirelessly, even from the drone.
## 5 Results and Analysis  
This section presents the results of the six scenarios mentioned above. The performance of a NovAtel SpanSE receiver is assessed using several evaluation metrics, including solution availability, dilution of precision (DOP), carrier-to-noise ratio, and positioning accuracy. The benefits of multi-constellation are explored by enabling only GPS constellation at a time, then repeating the same trajectory when both GPS and GLONASS are enabled. This section is structured as follows: First, it starts with the analysis of the results of scenarios #1 and #4, in which only GPS constellation is included in the position solution calculations. Second, the results of the remainder of the scenarios are presented; in which GPS/GLONASS constellations are enabled.
# GIS-Based Flood Assessment Using Hydraulic Modeling and Open Source Data: An Example of Application
## 3 Results
### Setting Parameters for Permanent Flow Analysis  
In the case study, one-dimensional analysis was applied for hydraulic modeling under conditions of steady, constant flow (steady flow analysis). This type of analysis was used to assess the possible extent of flooding, identifying areas that could be inundated under different flow conditions (varying discharge and water levels).  
Figure 4: The geometric elements of the selected river sector.  
Four scenarios (S1, S2, S3, and S4) were defined, each with a different flow rate and water level, ranging from low-flow conditions to flood potential. For the four scenarios, the flow rates and water levels, forecasted based on historical data, were as follows: S1: Q--8 m\({}^{3}\)/s; H--3 m; S2: Q--23 m\({}^{3}\)/s; H--3.2 m; S3: Q--41 m\({}^{3}\)/s; H--3.35 m; S4: Q--60 m\({}^{3}\)/s; H--3.5 m;  
The determination of the hydrological characteristics for the four scenarios was based on recurrence intervals of 1 year, 5 years, 10 years, and 15 years.
# TIG-DETR: Enhancing Texture Preservation and Information Interaction for Target Detection
## 4 Results
### Cityscapes  
We conducted additional experiments on TE-FPN using the Cityscapes [48] to assess its effectiveness in performing instance segmentation tasks. The Cityscapes dataset consists of street scenes captured by in-vehicle cameras, containing numerous overlapping and blurred instances. We utilized 2.9K images for training, 0.5K images for validation, and 1.5K images with fine annotations for testing. Additionally, 20K images with coarse annotations were included but excluded from training. We present the results on the validation and secret test subsets, evaluating the model performance based on AP and AP\({}_{50}\) metrics.  
The instance segmentation task focused on eight object classes: person, rider, car, truck, bus, train, motorcycle, and bicycle. We trained the model using eight GPUs, with eight images randomly sampled from each training image. The initial learning rate was set to 0.01 and it was reduced to 0.001 after 18K iterations. The test performance results are presented in Table 4.  
We utilized Mask R-CNN with ResNet-50 as the baseline model and replaced the FPN with TE-FPN. By pre-training our TE-FPN model on COCO, we achieved a performance improvement of 2.7 APs over Mask R-CNN for \"fine-only\" data. As shown in Table 4, TE-FPN  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**SRS** & **ETA** & **LFA** & **SRS+FWA** & **AP** & **AP\({}_{\mathbf{50}}\)** & **AP\({}_{\mathbf{75}}\)** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline \multirow{3}{*}{\(\surd\)} & & & & 36.2 & 56.1 & 38.6 & 20.0 & 39.6 & 47.5 \\  & \(\surd\) & & & 36.8 & 59.1 & 39.8 & 20.7 & 40.2 & 48.3 \\  & \(\surd\) & & & 37.0 & 56.7 & 39.9 & 20.8 & 40.3 & 48.1 \\  & & \(\surd\) & & 36.8 & 56.5 & 39.3 & 20.6 & 40.2 & 48.0 \\  & & & \(\surd\) & 37.5 & 57.4 & 40.1 & 21.5 & 40.7 & 49.0 \\ \hline \multirow{3}{*}{\(\surd\)} & \(\surd\) & & 37.5 & 57.5 & 40.2 & 21.4 & 41.0 & 49.4 \\  & \(\surd\) & & \(\surd\) & 37.6 & 58.0 & 40.1 & 21.5 & 41.3 & 49.6 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & 37.8 & 57.9 & 40.4 & 21.6 & 41.2 & 49.8 \\ \cline{1-1}  & \(\surd\) & \(\surd\) & \(\surd\) & **38.2** & **58.8** & **40.9** & **21.9** & **42.3** & **50.4** \\ \hline \hline \end{tabular} We used Resnet-50+FPN+Faster-R-CNN as our baseline method and gradually added enhancing texture information with a bottom-up architecture (ETA), Lightweight Feature-wise Attention (LFA), Feature-wise Attention (FWA). SRS is an abbreviation for the Soft RoI Selection method mentioned in the paper. The bolded part of the font indicates the largest indicator in the comparison experiment.  
\end{table}
Table 3: TE-FPN ablation experiments on COCO.  
consistently demonstrates notable results for the instance segmentation task, highlighting the model's strong generalization capability and its effectiveness across different tasks.